From 3264e05c0a8c697122fb0d5f7a961b4dd08d93ed Mon Sep 17 00:00:00 2001 From: tegwick Date: Tue, 5 May 2026 18:04:51 +0200 Subject: [PATCH] Major overhaul of requirements for refined INTENT.md --- INTENT.md | 238 +++++-- wiki/FunctionalRequirementsSpecification.md | 671 ++++++++++++++---- wiki/ProductRequirementsDocument.md | 667 ++++++++++++----- .../01_market-exploration.md | 302 ++++++++ .../02_ranked-corporate-usecases.md | 161 +++++ .../03_stated-usps.md | 134 ++++ .../04_core-capabilities-kpis.md | 173 +++++ .../05_project-scope-suggestions.md | 355 +++++++++ .../06_INTENT.refined.md | 238 +++++++ .../07_source-map.md | 74 ++ 10 files changed, 2659 insertions(+), 354 deletions(-) create mode 100644 wiki/kontextual-engine_scope_research_md_bundle/01_market-exploration.md create mode 100644 wiki/kontextual-engine_scope_research_md_bundle/02_ranked-corporate-usecases.md create mode 100644 wiki/kontextual-engine_scope_research_md_bundle/03_stated-usps.md create mode 100644 wiki/kontextual-engine_scope_research_md_bundle/04_core-capabilities-kpis.md create mode 100644 wiki/kontextual-engine_scope_research_md_bundle/05_project-scope-suggestions.md create mode 100644 wiki/kontextual-engine_scope_research_md_bundle/06_INTENT.refined.md create mode 100644 wiki/kontextual-engine_scope_research_md_bundle/07_source-map.md diff --git a/INTENT.md b/INTENT.md index 92bbb43..8a7ff88 100644 --- a/INTENT.md +++ b/INTENT.md @@ -2,111 +2,237 @@ ## Purpose -This repository exists to provide an **AI-first, headless knowledge and content engine** for managing, transforming, and operating structured information across heterogeneous data sources. +`kontextual-engine` exists to provide a **headless knowledge operations engine** for turning heterogeneous information assets into persistent, contextual, governed, retrievable, transformable, and agent-operable knowledge. -It enables persistent, service-based knowledge systems that support **efficient research, composition, and reuse of information**. +The project addresses the utility demand behind systems such as content management, document management, enterprise content management, file services, knowledge bases, research repositories, and AI-assisted knowledge workflows. It is not limited to any one of those categories. Its role is to provide reusable backend capabilities for making fragmented information operational. + +`kontextual-engine` should help people, teams, applications, automation systems, and AI agents work with knowledge assets across different sources, formats, domains, and lifecycle states. + +--- + +## Utility Demand + +Organizations and individuals accumulate valuable information in fragmented forms: + +* files and folders +* markdown and text repositories +* office documents +* PDFs +* datasets +* notes +* records +* policies +* project documentation +* knowledge-base articles +* generated AI outputs +* operational documents +* content archives +* application-linked documents and records + +These assets often remain economically underused because they are disconnected, inconsistently structured, weakly contextualized, difficult to govern, hard to retrieve, and unsafe to automate without explicit controls. + +`kontextual-engine` exists to solve this problem by giving knowledge assets durable identity, contextual structure, governed access, retrievable meaning, traceable transformation, and automation-ready interfaces. + +It is not merely a storage layer. It is an engine for making knowledge operational. --- ## Primary Utility -The repository provides a **runtime system and service layer** that: +The repository provides a **runtime and service layer for knowledge operations**. -* Manages knowledge as persistent, structured collections across projects and domains -* Integrates and normalizes data from multiple formats (markdown, documents, datasets, files) -* Orchestrates transformation workflows, including templating, generation, and analysis -* Provides APIs and service endpoints for accessing and operating on knowledge -* Supports AI-driven interaction, automation, and augmentation of knowledge processes +It is intended to support: -It transforms static content into a **living, operable knowledge system**. +* ingestion of knowledge assets from multiple sources and formats +* persistent representation of assets with stable identity +* extraction and normalization of useful structure, metadata, and content +* contextualization through metadata, relationships, provenance, classification, and lifecycle state +* retrieval through search, filtering, querying, browsing, APIs, and agent-compatible access patterns +* transformation of content into summaries, extracts, structured representations, generated artifacts, reports, views, or downstream formats +* workflow orchestration for recurring knowledge operations such as ingestion, enrichment, validation, review, publication, archival, and synchronization +* governed access through permissions, auditability, traceability, review state, and operational controls +* AI-assisted and agent-safe operation through explicit, permissioned, and auditable interfaces + +The core value of `kontextual-engine` is to make knowledge **durable, addressable, contextual, searchable, transformable, governable, and operationally useful**. --- ## Intended Users -* Developers building knowledge-driven applications and services -* Infrastructure operators (`adm`) managing knowledge systems and deployments -* Automation systems (`atm`) orchestrating workflows and transformations -* LLM agents (`agt`) interacting with and evolving structured knowledge environments +`kontextual-engine` is intended for: + +* developers building knowledge-driven applications and services +* teams that need structured access to documents, content, files, records, and datasets +* operators managing durable knowledge services +* product builders creating CMS, DMS, ECM, knowledge-base, research-support, file-service-like, or AI-assistant-backed systems +* automation systems that need reliable access to contextual information +* AI agents that need to inspect, retrieve, transform, enrich, and maintain knowledge assets +* researchers, analysts, and knowledge workers managing evolving collections of information + +The system should be usable by humans through applications and by machines through APIs, workflows, and controlled agent interfaces. --- -## Strategic Role in the System -This repository is part of a layered knowledge system with clearly separated responsibilities: +## Strategic Role -- markitect-tool → makes markdown structured and manipulable -- **kontextual-engine** → makes knowledge persistent and operable -- infospace-bench → makes knowledge concrete and meaningful +`kontextual-engine` serves as a **knowledge operations engine**. -These layers correspond to a deliberate separation of concerns: +Its role is to provide reusable backend capabilities for managing knowledge as an active operational resource rather than as passive content. -* **Syntax layer** — structuring and transforming semi-structured data (markdown) -* **System layer** — operating, persisting, and orchestrating knowledge -* **Application layer** — applying knowledge systems to real-world contexts +This includes: -This repository occupies the **system layer** and should maintain **clear boundaries** to the others. +* asset identity +* persistence +* ingestion +* normalization +* metadata +* contextual relationships +* indexing and retrieval +* transformation +* workflow execution +* permissions and access control +* provenance and traceability +* governance hooks +* integration interfaces +* agent-oriented operation -This repository acts as the **headless knowledge engine layer**: +The project should remain focused on the engine layer: the durable runtime capabilities needed to operate knowledge systems across many domains, applications, and deployment models. -* It sits above tool-level primitives (e.g. `markitect-tool`) -* It provides **persistence, orchestration, and access** to knowledge systems -* It enables **AI-native workflows** over structured and semi-structured data -* It supports multiple interaction modes: API, service, and agent-driven +It should not be constrained to a single content format, user interface, application domain, storage backend, AI model, or deployment scenario. -It is the **runtime substrate for knowledge systems**, not the tooling layer. +--- + +## Core Capabilities + +A mature `kontextual-engine` should provide capabilities in the following areas. + +### Knowledge Asset Management + +The system should manage knowledge assets as persistent entities with stable identity, metadata, relationships, provenance, versions, permissions, and lifecycle state. + +### Multi-Format Ingestion + +The system should ingest and normalize information from heterogeneous sources and formats, including text files, markdown, office documents, PDFs, datasets, structured records, generated outputs, and other content sources. + +### Contextualization + +The system should enrich knowledge assets with context such as tags, classifications, links, references, provenance, ownership, source information, temporal information, semantic annotations, review state, and derived relationships. + +### Retrieval and Access + +The system should expose knowledge through search, filtering, querying, browsing, APIs, and agent-compatible access patterns while respecting permissions and operational constraints. + +### Transformation + +The system should support controlled transformation of knowledge assets into summaries, extracts, structured representations, generated artifacts, reports, views, and downstream formats. + +Transformations should be traceable to their inputs, configuration, actor, workflow, and output artifacts. + +### Workflow Operation + +The system should support repeatable knowledge workflows such as ingestion, classification, validation, enrichment, review, approval, publication, archival, synchronization, and exception handling. + +### Governance and Traceability + +The system should preserve enough operational history to understand where knowledge came from, how it changed, who or what acted on it, which permissions applied, and what downstream artifacts depend on it. + +### AI-Assisted and Agent-Safe Operation + +The system should be designed so that AI agents can safely inspect, retrieve, transform, classify, enrich, and maintain knowledge assets through explicit interfaces and controlled workflows. + +Agent operation should be permissioned, auditable, reviewable, and reversible where practical. --- ## Strategic Boundaries -This repository is **not** intended to: +This repository is **not** intended to be: -* Replace low-level tooling for markdown or structured content manipulation -* Be constrained to markdown as a primary format -* Define end-user projects, experiments, or domain-specific knowledge spaces -* Act as a simple CLI toolkit +* a single-purpose document editor +* a simple file browser +* a format-specific markdown tool +* a pure vector database +* a generic chatbot over documents +* a finished end-user CMS by itself +* a visual website builder +* a file-sync client +* a domain-specific knowledge base +* a one-off automation script collection +* a full replacement for specialized authoring, publishing, legal, records-management, or analytical tools -Such concerns belong to: +Instead, it should provide reusable backend capabilities that such systems may depend on. -* `markitect-tool` (tooling layer) -* `infospace-bench` (project/workspace layer) +It may support user interfaces, command-line tools, importers, exporters, connectors, dashboards, and domain-specific applications, but those should remain consumers or extensions of the engine rather than the core identity of the project. --- ## Design Principles -* **AI-first operation** - The system is designed for interaction and orchestration by LLM agents +### Utility before presentation -* **Format-agnostic knowledge handling** - All data types are supported; markdown may serve as an interaction layer, not a constraint +The engine should focus first on making knowledge operationally useful. User interfaces and presentation layers may be built on top, but they should not define the core architecture. -* **Separation of concerns** - Tooling, runtime, and project layers are explicitly decoupled +### Format agnosticism -* **Persistent knowledge state** - Knowledge is stored, versioned, and evolved over time +The system should support many content types and should not be constrained by one preferred authoring format. -* **Operational composability** - Workflows are built from reusable, orchestratable primitives +### Persistent knowledge state + +Knowledge assets should have durable identity, lifecycle state, metadata, relationships, provenance, permissions, and operational history. + +### Context as a first-class concern + +The system should treat relationships, provenance, classification, lifecycle state, and usage context as core information, not as secondary decoration. + +### Traceable transformation + +Generated summaries, derived artifacts, classifications, extractions, and other transformations should remain linked to their source assets and workflow context. + +### API-first and automation-ready + +The system should expose stable interfaces suitable for applications, services, scripts, workflows, and AI agents. + +### Agent-safe operation + +AI agents should operate through explicit, permissioned, auditable, and bounded interfaces. Risky operations should support review gates, dry runs, or reversible workflows where appropriate. + +### Composable operation + +Knowledge operations should be built from reusable capabilities that can be combined into workflows. + +### Human and agent collaboration + +The system should support both human-directed and AI-assisted knowledge work, with clear ownership, permissions, review mechanisms, and traceability. + +### Separation of engine and application + +The repository should provide reusable engine capabilities rather than hard-coding one specific application, domain, user experience, storage backend, or AI model. --- ## Maturity Target -A mature version of this repository should: +A mature version of `kontextual-engine` should act as a robust, scalable backend for governed, AI-assisted knowledge management. -* Provide a **robust, scalable runtime for knowledge systems** -* Support **multi-format ingestion, transformation, and retrieval** -* Enable **fully automated and agent-driven knowledge workflows** -* Expose stable APIs for integration with external systems -* Act as the **default engine for AI-driven knowledge management** +It should be able to: + +* ingest and manage heterogeneous knowledge assets +* maintain persistent and traceable knowledge state +* represent context through metadata, relationships, provenance, and lifecycle state +* expose reliable APIs for applications, automation systems, and AI agents +* support search, retrieval, transformation, and workflow execution +* enforce permissions, auditability, review, and governance controls +* integrate with external storage, document, content, data, and search systems +* enable AI agents to operate knowledge safely and effectively +* support CMS, DMS, ECM, file-service, knowledge-base, research-support, and AI-assistant use cases +* serve as a reusable foundation for knowledge-driven products and platforms + +The long-term goal is to make `kontextual-engine` a default backend engine for systems that need to turn fragmented information into structured, contextual, governed, and operational knowledge. --- ## Stability Note -Changes to this file represent a **deliberate shift in the system’s role as a knowledge engine and runtime layer**. - -Such changes should be made with explicit architectural intent, as they affect all dependent systems and projects. +Changes to this file should represent deliberate changes to the intended role of the repository. +Because this document defines the project’s durable purpose, it should remain more stable than implementation details, feature plans, vendor comparisons, deployment-specific architecture decisions, or temporary implementation constraints. diff --git a/wiki/FunctionalRequirementsSpecification.md b/wiki/FunctionalRequirementsSpecification.md index c9ce252..3b0689d 100644 --- a/wiki/FunctionalRequirementsSpecification.md +++ b/wiki/FunctionalRequirementsSpecification.md @@ -1,14 +1,71 @@ -# Kontextual Engine Functional Requirements Specification V0.1 +# Kontextual Engine Functional Requirements Specification V0.2 ## kontextual-engine +Prepared: 2026-05-05 +Document type: Functional requirements specification +Status: Scope refinement draft +Aligned with: `ProductRequirementsDocument.V0.2.md` and `INTENT.refined.md` + --- ## 1. System Overview -kontextual-engine is a **headless knowledge system** that enables persistent storage, transformation, retrieval, and AI-driven operation of structured and semi-structured knowledge across heterogeneous data sources. +### 1.1 Product Summary -This FRS defines the **externally observable functional behavior** of the system. +`kontextual-engine` is a **headless knowledge operations engine** for making heterogeneous information assets persistent, contextual, governed, retrievable, transformable, and agent-operable. + +The system provides reusable backend capabilities for applications, workflows, services, and AI agents that need to operate documents, files, records, notes, datasets, generated outputs, and content collections as durable knowledge assets. + +This Functional Requirements Specification defines the **externally observable functional behavior** of the system. It does not prescribe a specific storage backend, search engine, AI provider, user interface, deployment model, or source-system implementation. + +--- + +### 1.2 Functional Scope + +The FRS covers the following functional areas: + +* knowledge asset registry and persistent identity +* ingestion from heterogeneous formats and sources +* normalization and extraction into common representations +* metadata, classification, context modeling, and relationships +* search, filtering, querying, and permission-aware retrieval +* transformation, composition, and traceable derived artifacts +* workflow and job orchestration +* permissions, policy enforcement, governance, audit, and lifecycle behavior +* versioning, provenance, and dependency traceability +* agent-safe AI interaction through explicit operations +* API-first access, integration, and extensibility +* observability, administration, export, portability, and error handling + +The system is not specified as a finished ECM, DMS, CMS, intranet, visual editor, file-sync client, pure vector database, or single-purpose AI chat application. Those may be built on top of the engine or integrated with it. + +--- + +### 1.3 Functional Operating Model + +The expected functional flow is: + +```text +knowledge sources + -> ingestion and normalization + -> stable knowledge asset identity + -> metadata, context, relationships, provenance, permissions, and lifecycle state + -> governed retrieval, transformation, workflow, and agent-safe operation + -> APIs, automation interfaces, exports, and downstream applications +``` + +The engine owns the middle layer: durable identity, context, governance, retrieval, transformation, workflow state, traceability, and operational interfaces. + +--- + +### 1.4 Requirement Priority Model + +Functional requirements use the following priority levels: + +* **P0 — Core engine requirement:** required for a credible MVP of the knowledge operations engine. +* **P1 — Enterprise readiness requirement:** required for strong corporate adoption, governance, scale, and operational maturity. +* **P2 — Expansion requirement:** useful for mature deployments, vertical packages, advanced workflows, or broader market coverage. --- @@ -16,232 +73,568 @@ This FRS defines the **externally observable functional behavior** of the system ### 2.1 Primary Actors -* **User (Human Operator)** via API or service interface -* **Automation System (`atm`)** executing workflows -* **LLM Agent (`agt`)** interacting with knowledge and workflows -* **External Systems** integrating via APIs +| Actor | Description | Typical Functional Needs | +|---|---|---| +| Human knowledge worker | A person using applications built on the engine. | Search, inspect, validate, compose, review, and reuse knowledge assets. | +| Developer | A person building applications, integrations, workflows, extensions, or services on the engine. | Stable APIs, schemas, events, SDKs, predictable errors, and testable behavior. | +| Platform operator | A person managing engine operation. | Ingestion status, job control, re-indexing, observability, audit access, and recovery tools. | +| Business process owner | A person responsible for a knowledge workflow, governance rule, or lifecycle process. | Workflow definition, approval rules, policy checks, exceptions, and reporting. | +| Reviewer or approver | A human participant in validation, correction, approval, or publication workflows. | Review queues, source context, decisions, comments, and audit trail. | +| External application | A product or service that uses the engine through APIs. | Asset operations, search, retrieval, workflow invocation, and export. | +| Automation system | Deterministic automation invoking recurring jobs or workflows. | Scheduled ingestion, enrichment, validation, transformation, synchronization, and archival. | +| AI agent | An AI system acting through explicit tool-like operations. | Bounded context access, source-grounded retrieval, transformations, workflow actions, and review submission. | +| Source system | A file store, repository, database, content system, document platform, or business application supplying assets. | Connector-mediated ingestion, permission context, metadata, source references, and update events. | +| Downstream system | A target application, storage location, publication channel, archive, or workflow system receiving outputs. | Exported assets, derived artifacts, events, and lineage-preserving integration. | --- ### 2.2 System Interfaces -* Service API (HTTP, RPC, or equivalent) -* Programmatic API (SDK/library interface) -* Storage interface (abstracted from implementation) +| Interface | Required Role | +|---|---| +| Service API | Primary interface for asset, metadata, retrieval, transformation, workflow, permission, audit, export, and agent operations. | +| Programmatic API or SDK | Developer-facing abstraction over the service API where provided. | +| Connector and adapter interface | Source-system and downstream-system integration boundary. | +| Workflow and job interface | Submission, execution, tracking, retry, cancellation, and result inspection for jobs and workflows. | +| Agent operation interface | Explicit bounded operations for AI agents with permission checks, audit logging, and review gates. | +| Admin and observability interface | Operational inspection, error recovery, audit access, metrics, and governance reporting. | +| Export and portability interface | Governed extraction of assets, metadata, relationships, versions, provenance, audit references, and derived artifacts. | --- -## 3. Functional Requirements +### 2.3 Authorization Context + +Every material operation should be evaluated against an authorization context containing, where available: + +* actor identity +* delegated user or service context +* role and group membership +* asset-specific policy +* source-system policy or effective permission data +* sensitivity classification +* lifecycle state +* workflow state +* operation type and requested output + +AI agents must not receive implicit privileged access. They are actors with explicit scope, permissions, task boundaries, and audit requirements. --- -## 3.1 Knowledge Persistence +## 3. Functional Entities -### FR-001: Store Knowledge Artifacts - -**Description:** -The system must allow storage of knowledge artifacts. - -**Input:** - -* Structured or semi-structured data - -**Output:** - -* Persisted knowledge artifact with identifier +| Entity | Functional Meaning | +|---|---| +| Knowledge asset | A durable unit of knowledge managed by the engine, such as a file, document, record, dataset, note, generated output, or content item. | +| Asset ID | Stable identifier assigned by the engine and used independent of path, filename, source URL, storage backend, or representation. | +| Source reference | Information that identifies where an asset originated, including source system, path, URL, external ID, checksum, or connector reference where available. | +| Source representation | The original or source-near form of the asset, preserved or referenced where configured. | +| Normalized representation | Engine-usable representation created from ingestion and extraction, suitable for search, metadata, transformation, workflows, and agent context. | +| Metadata | Structured descriptive information attached to an asset, including standard and custom fields. | +| Classification | A label or category used for type, topic, sensitivity, lifecycle, operational purpose, or governance. | +| Contextual entity | A non-asset entity such as person, project, case, customer, product, process, topic, source system, or business object. | +| Relationship | A typed link between assets or between an asset and a contextual entity. | +| Version | A traceable state of asset content, metadata, relationships, lifecycle, or derived artifact. | +| Derived artifact | An output produced from one or more source assets through transformation, composition, extraction, summarization, generation, or workflow. | +| Transformation run | A recorded operation that creates, updates, or derives information from assets. | +| Workflow run | An executed instance of a workflow template or job definition. | +| Policy | A rule or rule set controlling permissions, lifecycle, retention, review, transformation, publication, export, or agent behavior. | +| Audit event | A record of a material operation, actor, target, time, outcome, and relevant policy context. | +| Export package | A governed package containing selected assets and supporting metadata, relationships, versions, provenance, audit references, and manifests. | --- -### FR-002: Retrieve Knowledge Artifacts +## 4. Functional Requirements -The system must allow retrieval of stored knowledge artifacts by identifier or query. +Each requirement below specifies externally observable system behavior. Verification should be possible through API contract tests, integration tests, workflow tests, permission tests, audit-log inspection, export validation, or operator-facing status inspection. + + +### 4.1 Knowledge Asset Registry and Persistence + +| ID | Priority | Requirement | Functional Behavior | Acceptance Signal | +| --- | --- | --- | --- | --- | +| FR-001 | P0 | Create knowledge assets | The system shall create a knowledge asset from submitted content, structured data, or a source reference. | A caller can submit content or a source reference and receive a persisted asset record with an asset ID and initial state. | +| FR-002 | P0 | Assign stable asset identity | The system shall assign an asset ID that remains stable across rename, move, re-ingestion, representation change, and transformation. | An asset can change path, filename, source representation, or normalized representation without losing its asset ID or history. | +| FR-003 | P0 | Persist asset state | The system shall persist asset content references, normalized content, metadata, relationships, lifecycle state, permissions, provenance, and operational status where available. | A persisted asset can be retrieved with its current content reference, normalized representation, metadata, relationships, lifecycle state, and provenance. | +| FR-004 | P0 | Retrieve assets by identifier | The system shall retrieve a knowledge asset by stable asset ID. | A valid asset ID returns the matching asset or an explicit permission or not-found error. | +| FR-005 | P0 | Update asset content and metadata | The system shall update asset content, normalized representation, metadata, relationships, and lifecycle state through explicit operations. | Updates are persisted, audit logged, and visible through subsequent retrieval calls. | +| FR-006 | P0 | Retire or delete assets under policy | The system shall support asset retirement, soft deletion, and deletion requests subject to lifecycle, retention, legal hold, and permission checks. | A deletion request either changes the asset to the expected terminal state or returns a structured policy error. | +| FR-007 | P0 | Group assets into collections | The system shall group assets into collections, domains, projects, spaces, or equivalent organizational containers. | Assets can be assigned to and retrieved from one or more configured containers. | +| FR-008 | P0 | Represent original and normalized forms | The system shall distinguish between source/original representation and normalized representation used for retrieval and workflows. | A caller can inspect source reference data and normalized content without confusing the two representations. | +| FR-009 | P1 | Detect duplicate or repeated ingestion | The system should identify likely duplicate assets or repeated ingestion events using configured identity, source, checksum, or fingerprint rules. | Repeated ingestion of the same source can update the existing asset or produce a duplicate warning according to configured policy. | +| FR-010 | P1 | Support aliases and supersession | The system should support aliases, redirects, canonical asset references, and supersession relationships. | A renamed, replaced, or superseded asset remains discoverable through configured aliases or successor references. | --- -### FR-003: Update Knowledge Artifacts +### 4.2 Ingestion and Normalization -The system must allow modification of existing knowledge artifacts. +| ID | Priority | Requirement | Functional Behavior | Acceptance Signal | +| --- | --- | --- | --- | --- | +| FR-020 | P0 | Submit ingestion jobs | The system shall create ingestion jobs from direct uploads, local or remote source references, connector events, or API requests. | A caller can submit an ingestion request and receive a job ID with observable status. | +| FR-021 | P0 | Ingest baseline heterogeneous formats | The system shall support ingestion of text, markdown, common office documents, PDFs, and structured datasets in the baseline implementation. | Each baseline format can be ingested into a knowledge asset with normalized content and source provenance. | +| FR-022 | P0 | Record ingestion provenance | The system shall record source system, source location, source identifier, ingestion time, extractor, transformation path, and actor where available. | Each ingested asset can report where it came from, when it was ingested, and how it was extracted. | +| FR-023 | P0 | Normalize content | The system shall convert ingested content into a common internal representation suitable for search, metadata, relationships, transformations, and workflows. | Assets from different supported formats can be queried and transformed through common APIs. | +| FR-024 | P0 | Extract structural elements | The system shall extract structural elements such as title, sections, headings, paragraphs, tables, links, and embedded references where supported by the source format. | The normalized representation exposes structure when the extractor can recover it. | +| FR-025 | P0 | Expose ingestion status and failures | The system shall expose queued, running, completed, failed, retried, and partially completed ingestion states. | Operators and callers can inspect failure reason, affected assets, correlation ID, and retry options. | +| FR-026 | P1 | Support incremental re-ingestion | The system should re-ingest changed sources without corrupting identity, version history, provenance, permissions, or relationships. | A changed source can be synchronized while preserving the stable asset ID and creating a traceable update. | +| FR-027 | P1 | Support pluggable extractors and connectors | The system should allow new source connectors and format extractors to be added without changing core engine behavior. | A new connector or extractor can register capabilities, submit assets, and return normalized content through a defined contract. | +| FR-028 | P1 | Validate ingestion output | The system should validate normalized content, required metadata, provenance, and policy constraints before marking ingestion complete. | Invalid ingestion output produces structured validation errors and does not silently enter the trusted asset set. | +| FR-029 | P2 | Support advanced OCR and layout extraction | The system may support OCR, visual layout extraction, table reconstruction, and image-region extraction for scanned or complex documents. | A scanned or layout-heavy document can produce text, structure, and confidence signals where configured. | +| FR-030 | P2 | Support media-derived representations | The system may create transcripts, captions, thumbnails, previews, embeddings, or metadata for image, audio, and video assets. | Rich-media assets can expose derived representations suitable for retrieval and governance. | --- -### FR-004: Delete Knowledge Artifacts +### 4.3 Metadata, Classification, and Context Modeling -The system must allow removal of stored knowledge artifacts. +| ID | Priority | Requirement | Functional Behavior | Acceptance Signal | +| --- | --- | --- | --- | --- | +| FR-040 | P0 | Manage explicit metadata | The system shall create, read, update, and remove explicit metadata fields on knowledge assets. | A caller can set and retrieve asset metadata through the API with audit logging. | +| FR-041 | P0 | Support standard metadata fields | The system shall support standard metadata fields for asset type, owner, source, domain, project or context, sensitivity, lifecycle state, tags, timestamps, and custom labels. | Standard metadata can be used consistently for filtering, permissions, workflows, and audit. | +| FR-042 | P0 | Support custom metadata schemas | The system shall allow configured schemas for domain-specific metadata without hard-coding one domain model into the engine. | A configured schema can validate and expose custom fields for a collection or asset type. | +| FR-043 | P0 | Assign classifications | The system shall assign classifications such as document type, topic, sensitivity, lifecycle status, and operational category manually or through configured automation. | Classifications can be stored, queried, corrected, and audited. | +| FR-044 | P0 | Define relationships between assets | The system shall create, retrieve, update, and remove typed relationships between knowledge assets. | Assets can be linked as source, derivative, reference, duplicate, successor, dependency, version, citation, or related item according to configured relationship types. | +| FR-045 | P0 | Represent contextual entities | The system shall represent contextual entities such as people, teams, projects, cases, customers, products, processes, source systems, topics, and generated artifacts. | Assets can be linked to contextual entities and retrieved through those links. | +| FR-046 | P0 | Query context | The system shall allow querying assets by relationship, contextual entity, collection, source, metadata, and lifecycle state. | A caller can retrieve all assets connected to a project, case, topic, person, process, or other configured entity. | +| FR-047 | P1 | Maintain relationship semantics | The system should support relationship direction, type, validity interval, confidence, actor, and provenance. | A relationship can indicate who or what created it, why it exists, and whether it is current, inferred, or manually confirmed. | +| FR-048 | P1 | Support inferred metadata review | The system should distinguish inferred metadata or relationships from human-confirmed metadata or relationships. | AI- or automation-generated annotations can be reviewed, accepted, corrected, or rejected. | +| FR-049 | P1 | Validate metadata against schemas | The system should enforce required fields, data types, allowed values, and conditional rules according to configured schemas. | Invalid metadata updates return structured validation errors. | +| FR-050 | P2 | Support domain-specific context packages | The system may allow deployable domain packages for legal, support, research, compliance, engineering, or marketing semantics. | A domain package can add schema, relationship types, workflow templates, and validation rules without redefining the core engine. | --- -## 3.2 Knowledge Organization +### 4.4 Search, Query, and Retrieval -### FR-010: Group Knowledge into Collections - -The system must allow grouping knowledge artifacts into collections or domains. +| ID | Priority | Requirement | Functional Behavior | Acceptance Signal | +| --- | --- | --- | --- | --- | +| FR-060 | P0 | Retrieve by asset ID | The system shall retrieve assets by stable asset ID through an API operation. | A permitted caller receives the current asset record, selected representations, metadata, relationships, and provenance. | +| FR-061 | P0 | Search by text | The system shall support text search across normalized content for supported ingested assets. | A query returns matching assets with relevance ordering and result metadata. | +| FR-062 | P0 | Filter by metadata and lifecycle state | The system shall support filtering by asset type, collection, source, owner, tags, classification, sensitivity, lifecycle state, and timestamps. | A query can combine text search with metadata and lifecycle filters. | +| FR-063 | P0 | Retrieve by relationship and context | The system shall support retrieval by relationships and contextual entities. | A caller can retrieve assets related to a given project, case, topic, source asset, generated artifact, or workflow run. | +| FR-064 | P0 | Return source-grounded result data | The system shall return asset IDs, titles, snippets or matched regions, relevant metadata, source references, and relationship context where available. | Search results provide enough information to inspect why a result was returned and where it originated. | +| FR-065 | P0 | Enforce permission-aware retrieval | The system shall apply permission and policy checks before returning asset content, metadata, snippets, derived artifacts, or relationship data. | Unauthorized assets do not appear in results, snippets, generated answers, exports, or relationship traversals. | +| FR-066 | P0 | Support stable pagination and sorting | The system shall support deterministic pagination and sorting for query results. | Repeated equivalent queries return stable pages within documented consistency limits. | +| FR-067 | P1 | Support facets and aggregations | The system should return facets or aggregations for configured metadata and classifications. | A caller can display counts by source, type, owner, sensitivity, lifecycle state, or configured taxonomy. | +| FR-068 | P1 | Support semantic retrieval | The system should support semantic or vector-based retrieval in addition to lexical search where configured. | A semantic query can return relevant assets even when exact terms differ, while preserving permissions and provenance. | +| FR-069 | P1 | Support grounded answer retrieval | The system should provide retrieval packages suitable for grounded answers, summaries, and analysis. | A grounded answer workflow receives supporting passages, citations, source IDs, metadata, and permission context. | +| FR-070 | P1 | Capture retrieval feedback | The system should allow users, applications, or evaluation jobs to record useful, irrelevant, missing, or unsafe retrieval feedback. | Feedback is stored with query context and can be used for quality analysis. | +| FR-071 | P2 | Support federated query patterns | The system may support querying across external repositories without fully ingesting all content when connector policy allows. | A query can combine engine-managed assets with connector-mediated external results while preserving source permissions. | --- -### FR-011: Maintain Relationships +### 4.5 Transformation, Composition, and Derived Artifacts -The system must allow defining and retrieving relationships between knowledge artifacts. +| ID | Priority | Requirement | Functional Behavior | Acceptance Signal | +| --- | --- | --- | --- | --- | +| FR-080 | P0 | Execute transformations | The system shall execute configured transformations over one or more knowledge assets. | A caller can request a transformation and receive a run ID, status, and result or structured error. | +| FR-081 | P0 | Compose outputs from multiple assets | The system shall compose derived outputs from multiple source assets where configured. | A report, summary, extract, view, bundle, or structured representation can be created from selected source assets. | +| FR-082 | P0 | Persist derived artifacts | The system shall persist derived outputs as knowledge assets or artifact records with stable identity. | A derived artifact can be retrieved, queried, governed, versioned, and related to its sources. | +| FR-083 | P0 | Record transformation lineage | The system shall record source assets, source versions where available, operation type, parameters, actor, time, policy context, and output artifact for each transformation. | A derived artifact can explain which sources and operation produced it. | +| FR-084 | P0 | Support parameterized transformations | The system shall support transformation parameters such as output type, scope, template, model, extraction fields, target schema, and review policy where applicable. | Transformation results include the parameters necessary to interpret or reproduce the operation within documented limits. | +| FR-085 | P0 | Enforce transformation permissions | The system shall enforce access and policy checks before reading source assets, generating outputs, or storing derived artifacts. | A caller cannot use transformation workflows to bypass retrieval, export, sensitivity, or lifecycle policies. | +| FR-086 | P1 | Support human review for transformations | The system should support review, approval, correction, and rejection of derived artifacts before publication or downstream use. | A transformation can produce a draft artifact that requires human decision before being marked approved. | +| FR-087 | P1 | Support controlled re-runs | The system should allow transformations to be re-run against the same or newer source versions with explicit lineage. | A re-run produces a new traceable run record and does not overwrite prior results without policy permission. | +| FR-088 | P1 | Compare derived artifacts | The system should compare derived artifacts across source versions, transformation parameters, or review states. | A caller can inspect differences between two summaries, reports, extracts, or generated representations. | +| FR-089 | P2 | Publish transformation outputs | The system may publish approved derived artifacts to downstream systems through configured adapters. | A derived artifact can be delivered to an external application while retaining lineage and publication audit. | +| FR-090 | P2 | Support reusable transformation templates | The system may support configurable templates for recurring summaries, reports, extracts, and generated artifacts. | A template can be versioned, invoked, audited, and reused across workflows. | --- -## 3.3 Ingestion and Normalization +### 4.6 Workflow and Job Orchestration -### FR-020: Ingest Multi-Format Data - -The system must accept input from multiple data formats (e.g. markdown, documents, files). +| ID | Priority | Requirement | Functional Behavior | Acceptance Signal | +| --- | --- | --- | --- | --- | +| FR-100 | P0 | Define workflow templates | The system shall define reusable workflow or job templates containing steps, dependencies, inputs, outputs, policies, and failure behavior. | A workflow template can be created and invoked through the API. | +| FR-101 | P0 | Execute workflows | The system shall execute multi-step workflows over assets, collections, queries, source events, or submitted inputs. | A workflow run can ingest, enrich, validate, transform, review, publish, synchronize, archive, or export knowledge according to the template. | +| FR-102 | P0 | Track workflow state | The system shall expose workflow run state, step state, actor, timestamps, input references, output references, and error status. | A caller can inspect queued, running, waiting, completed, failed, canceled, and retried states. | +| FR-103 | P0 | Respect step dependencies | The system shall execute workflow steps according to declared dependencies and preconditions. | A dependent step does not run until required prior steps succeed or enter an allowed alternate state. | +| FR-104 | P0 | Return workflow results | The system shall return workflow outputs, generated artifacts, updated assets, validation results, and failure details. | A completed workflow has observable outputs or an explicit no-output result. | +| FR-105 | P0 | Retry, resume, and cancel jobs | The system shall support retry, resume, and cancellation behavior for workflows and jobs where operation semantics allow. | A failed job can be retried from a safe state, resumed, or canceled with audit and visible outcome. | +| FR-106 | P0 | Audit workflow operations | The system shall audit workflow template changes, run starts, step executions, retries, cancellations, approvals, failures, and outputs. | A workflow run can be reconstructed from audit and run records. | +| FR-107 | P1 | Support event and schedule triggers | The system should trigger workflows from source changes, API events, schedules, lifecycle transitions, review decisions, and external webhooks. | A configured trigger starts the intended workflow and records trigger context. | +| FR-108 | P1 | Support human tasks | The system should support human review, validation, approval, correction, rejection, and exception-handling tasks inside workflows. | A workflow can pause for an assigned human decision and continue according to the result. | +| FR-109 | P1 | Maintain exception queues | The system should expose failed, blocked, low-confidence, policy-conflicted, or review-required workflow items as actionable queues. | Operators can list, inspect, assign, retry, approve, reject, or escalate exception items. | +| FR-110 | P2 | Support cross-system orchestration | The system may orchestrate workflows involving external ECM, CMS, DMS, ERP, CRM, ITSM, HR, support, storage, or publishing systems. | A workflow can call external systems through adapters while retaining engine-side state and audit. | --- -### FR-021: Normalize Data +### 4.7 Permissions, Governance, Audit, and Lifecycle -The system must convert ingested data into a structured representation usable by the system. +| ID | Priority | Requirement | Functional Behavior | Acceptance Signal | +| --- | --- | --- | --- | --- | +| FR-120 | P0 | Represent actors | The system shall represent human users, applications, automation systems, service accounts, and AI agents as actors with explicit identity context. | Every material operation can be associated with an actor or service principal. | +| FR-121 | P0 | Authorize operations | The system shall authorize retrieval, mutation, transformation, workflow, export, and agent operations based on actor, role, group, asset policy, sensitivity, lifecycle state, and source policy where available. | Unauthorized operations fail with structured authorization errors and do not leak protected content. | +| FR-122 | P0 | Enforce sensitivity and lifecycle constraints | The system shall apply sensitivity, lifecycle, review, publication, retention, deletion, and archival constraints to relevant operations. | A restricted asset cannot be transformed, exported, published, or deleted unless policy allows. | +| FR-123 | P0 | Preserve source permissions where available | The system shall store and apply source-system permission references or effective access rules when supplied by connectors. | Retrieval and derived operations respect source permissions or fail closed when required permission context is unavailable. | +| FR-124 | P0 | Audit material operations | The system shall audit asset creation, ingestion, update, deletion, metadata change, relationship change, permission change, query, transformation, workflow action, export, and agent operation according to configured audit policy. | Audit events include actor, operation, asset or job reference, timestamp, outcome, correlation ID, and policy context where available. | +| FR-125 | P0 | Query audit history | The system shall allow authorized callers to query audit events by asset, actor, operation, workflow, time range, source, and outcome. | An auditor can reconstruct who or what acted on an asset and when. | +| FR-126 | P0 | Fail closed on ambiguous access | The system shall deny or withhold protected content when permission or policy state is missing, stale, or ambiguous according to configured safety rules. | Ambiguous policy state produces an explicit error, hold, or redacted result rather than silent exposure. | +| FR-127 | P1 | Manage retention policies | The system should apply configured retention policies to assets, metadata, versions, audit events, and derived artifacts. | Assets subject to retention cannot be deleted before allowed disposition unless policy permits. | +| FR-128 | P1 | Support legal hold | The system should place assets, versions, metadata, derived artifacts, and relevant audit history under legal or compliance hold. | A held item cannot be altered or deleted in violation of the hold policy. | +| FR-129 | P1 | Support archival and defensible deletion | The system should support archival, disposal review, deletion approval, and deletion evidence for governed assets. | A deletion action produces traceable evidence or is blocked by retention, hold, or permission policy. | +| FR-130 | P1 | Synchronize permission changes | The system should update effective access when source-system permissions, internal roles, group membership, or policy rules change. | Permission changes propagate to retrieval, transformation, export, and agent access within documented latency. | +| FR-131 | P1 | Produce governance reports | The system should generate reports for retention coverage, policy exceptions, legal holds, access anomalies, stale assets, and audit completeness. | An authorized operator can export governance status for selected scopes. | +| FR-132 | P2 | Integrate with external policy and DLP systems | The system may integrate with external identity, classification, data loss prevention, records, privacy, or compliance systems. | External policy signals can influence access, transformation, export, and lifecycle decisions. | --- -## 3.4 Query and Retrieval +### 4.8 Versioning and Provenance -### FR-030: Query Knowledge - -The system must allow querying knowledge artifacts based on: - -* Content -* Metadata -* Relationships +| ID | Priority | Requirement | Functional Behavior | Acceptance Signal | +| --- | --- | --- | --- | --- | +| FR-140 | P1 | Version asset content | The system should track versions of asset content or source references when assets change. | A caller can list versions and retrieve a selected version where policy permits. | +| FR-141 | P1 | Version metadata and relationships | The system should track changes to metadata, classification, lifecycle state, and relationships. | A caller can inspect how metadata or relationships changed over time. | +| FR-142 | P1 | Compare and restore versions | The system should compare versions and restore a prior version subject to permission and lifecycle policy. | A restore operation creates a new auditable change rather than erasing history. | +| FR-143 | P0 | Expose source provenance | The system shall expose source provenance for ingested assets, including source reference and ingestion path where available. | A user, application, workflow, or agent can determine the origin of an asset. | +| FR-144 | P0 | Expose derived-artifact lineage | The system shall expose lineage for generated or transformed artifacts. | A summary, extract, report, or generated representation can point back to source assets and transformation runs. | +| FR-145 | P1 | Support dependency impact analysis | The system should identify derived artifacts, workflows, indexes, or downstream integrations that depend on a changed source asset. | A source update can show which artifacts or workflows may need refresh or review. | +| FR-146 | P2 | Support provenance graph traversal | The system may support graph-style traversal across sources, versions, transformations, workflows, reviews, and outputs. | A caller can query multi-hop lineage and dependency paths. | --- -### FR-031: Return Query Results +### 4.9 Agent-Safe AI Interaction -The system must return matching knowledge artifacts and associated data. +| ID | Priority | Requirement | Functional Behavior | Acceptance Signal | +| --- | --- | --- | --- | --- | +| FR-160 | P0 | Register AI agents as explicit actors | The system shall treat AI agents as explicit actors or delegated actors, not as implicit privileged internal processes. | Agent operations include agent identity, delegated user or service context where applicable, and policy scope. | +| FR-161 | P0 | Expose a bounded operation catalog | The system shall expose explicit agent-usable operations for inspection, retrieval, metadata enrichment, classification, transformation, workflow invocation, and review submission. | An agent can only act through documented operations with declared inputs, outputs, and permissions. | +| FR-162 | P0 | Apply permissions to agent operations | The system shall apply the same or stricter permission and policy checks to agent operations as to human, application, or automation operations. | An agent cannot retrieve, infer, transform, export, or publish content beyond its authorized scope. | +| FR-163 | P0 | Provide context packages | The system shall provide agents with bounded context packages containing selected assets, snippets, metadata, relationships, provenance, task instructions, and policy constraints. | Agent context is explicit, source-grounded, and does not require unrestricted repository access. | +| FR-164 | P0 | Audit agent operations | The system shall log agent reads, searches, transformations, metadata changes, workflow actions, generated artifacts, and review submissions. | An auditor can distinguish agent actions from human and deterministic automation actions. | +| FR-165 | P0 | Require review gates where policy demands | The system shall require human review or deny operations for destructive, sensitive, externally published, or high-impact agent actions when configured policy requires it. | A sensitive agent operation enters a review state or fails with a policy error rather than executing automatically. | +| FR-166 | P1 | Support grounded AI answer workflows | The system should support AI-assisted answers, summaries, and analyses that cite supporting assets and preserve source context. | Generated answers include source references and can be audited for supporting evidence. | +| FR-167 | P1 | Remain provider neutral | The system should support AI provider, embedding model, reranker, and prompt strategy substitution through configured adapters. | Changing an AI provider does not require redefining core asset, permission, provenance, or workflow models. | +| FR-168 | P1 | Constrain agent tasks | The system should support task scopes, budgets, time limits, allowed operation lists, and approval requirements for agent workflows. | Agent execution stops or requests review when boundaries are reached. | +| FR-169 | P2 | Support multi-step agent workflows | The system may support agent workflows that plan, execute, monitor, request review, recover from failures, and produce traceable artifacts. | A multi-step agent task can be replayed or inspected from operation logs and workflow state. | --- -## 3.5 Transformation and Composition +### 4.10 API, Integration, and Extensibility -### FR-040: Transform Knowledge Artifacts - -The system must allow applying transformations to knowledge artifacts. +| ID | Priority | Requirement | Functional Behavior | Acceptance Signal | +| --- | --- | --- | --- | --- | +| FR-180 | P0 | Provide service APIs | The system shall expose core capabilities through service APIs for assets, metadata, relationships, ingestion, retrieval, transformations, workflows, permissions, audit, and agent operations. | Core operations can be performed without requiring a specific user interface or CLI. | +| FR-181 | P0 | Provide stable programmatic contracts | The system shall define stable request, response, error, pagination, filtering, authentication, and authorization contracts for programmatic clients. | External clients can integrate through documented contracts and receive predictable responses. | +| FR-182 | P0 | Accept external processing results | The system shall accept results from external processors, such as extractors, classifiers, enrichment services, transformation services, or AI systems, through controlled interfaces. | External results can be attached to assets as metadata, relationships, normalized representations, or derived artifacts with provenance. | +| FR-183 | P1 | Support source adapters | The system should provide an adapter model for source repositories, file stores, document systems, databases, content platforms, and application systems. | A source adapter can submit assets, source references, permission context, and update events through defined interfaces. | +| FR-184 | P1 | Emit events and webhooks | The system should emit events for asset changes, ingestion completion, workflow status, policy exceptions, derived artifact creation, and review decisions. | External systems can subscribe to engine events and react without polling every operation. | +| FR-185 | P1 | Support extensible schemas and plugins | The system should allow custom metadata schemas, relationship types, workflow steps, transformations, validators, and policy checks to be added through extensions. | An extension can add domain behavior without modifying core engine code. | +| FR-186 | P1 | Abstract implementation backends | The system should abstract storage, index, queue, workflow, AI provider, and model backends where practical. | A deployment can swap supported backends without changing externally visible asset semantics. | +| FR-187 | P1 | Version APIs | The system should version APIs and avoid breaking existing integrations without documented migration paths. | A client pinned to a supported API version continues to operate within the version support policy. | +| FR-188 | P2 | Support extension registry patterns | The system may provide a registry for connectors, extractors, transformations, policy modules, and domain packages. | Operators can discover, enable, disable, and inspect extensions from a managed registry. | --- -### FR-041: Compose Knowledge +### 4.11 Observability and Administration -The system must allow combining multiple knowledge artifacts into derived outputs. +| ID | Priority | Requirement | Functional Behavior | Acceptance Signal | +| --- | --- | --- | --- | --- | +| FR-200 | P0 | Expose job and ingestion status | The system shall expose current and historical status for ingestion jobs, transformation runs, workflow runs, and exports. | Operators can inspect state, duration, input, output, actor, and failure details. | +| FR-201 | P0 | Return correlation identifiers | The system shall return correlation IDs or trace references for errors, jobs, workflows, and material operations. | A reported error can be linked to system logs and audit records. | +| FR-202 | P0 | Support administrative recovery actions | The system shall support authorized retry, re-run, re-index, cancel, quarantine, and repair actions where safe. | An operator can recover from common ingestion, workflow, indexing, and transformation failures without directly modifying storage. | +| FR-203 | P1 | Expose operational metrics | The system should expose metrics for ingestion throughput, query latency, API latency, workflow completion, job failure, queue age, reprocessing success, and storage/index health. | Operators can monitor service health and compare implementation quality against target KPIs. | +| FR-204 | P1 | Expose retrieval quality signals | The system should expose retrieval quality feedback, zero-result rate, low-confidence result rate, click or selection signals where available, and evaluation results. | Product teams can identify poor retrieval behavior and measure improvement over time. | +| FR-205 | P1 | Expose AI operation and cost signals | The system should expose model calls, token or compute usage where available, transformation cost, answer cost, agent task cost, and provider errors. | Operators can attribute AI usage and cost to workflows, assets, agents, or applications. | +| FR-206 | P1 | Support governance inspection | The system should allow authorized inspection of permission coverage, policy gaps, stale permissions, missing metadata, lifecycle exceptions, and audit completeness. | Governance operators can identify assets that are under-classified, overexposed, stale, or policy-conflicted. | +| FR-207 | P2 | Support policy simulation | The system may simulate the impact of permission, lifecycle, retention, and export policy changes before enforcement. | An operator can preview affected assets, workflows, exports, and agent scopes before activating a policy change. | --- -## 3.6 Workflow Orchestration +### 4.12 Export, Portability, and Migration -### FR-050: Execute Workflows - -The system must allow execution of multi-step workflows on knowledge artifacts. +| ID | Priority | Requirement | Functional Behavior | Acceptance Signal | +| --- | --- | --- | --- | --- | +| FR-220 | P1 | Export asset packages | The system should export assets, normalized representations, metadata, relationships, provenance, versions, audit references, and derived artifacts according to permission and policy. | An export package contains enough information to inspect or migrate selected knowledge assets. | +| FR-221 | P1 | Export by scope | The system should export by asset ID, collection, query, workflow run, source system, lifecycle state, date range, or governance policy. | An authorized caller can export a governed subset without manual database access. | +| FR-222 | P1 | Include manifests and integrity data | The system should include manifests, counts, checksums or hashes, schema versions, export time, actor, and policy context in export packages. | An exported package can be validated for completeness and integrity. | +| FR-223 | P1 | Support re-import or migration validation | The system should support validation of exported packages for re-import, migration, or downstream processing. | An export can be checked before migration and produce a validation report. | +| FR-224 | P2 | Support long-term archival formats | The system may support archival formats and preservation metadata for long-lived governed assets. | An archive package preserves source, context, lifecycle, and provenance information for long-term use. | +| FR-225 | P2 | Produce migration reports | The system may produce migration reports for completeness, skipped assets, unsupported fields, permission gaps, and relationship preservation. | A migration run can be evaluated before decommissioning a source system. | --- -### FR-051: Manage Workflow Dependencies +### 4.13 Error Handling and Functional Correctness -The system must handle dependencies between workflow steps. +| ID | Priority | Requirement | Functional Behavior | Acceptance Signal | +| --- | --- | --- | --- | --- | +| FR-240 | P0 | Return structured errors | The system shall return structured errors for invalid input, unauthorized access, unsupported format, failed ingestion, policy conflict, validation failure, dependency failure, and internal failure. | Clients receive machine-readable error code, message, correlation ID, operation, and remediation hint where available. | +| FR-241 | P0 | Avoid silent failures | The system shall not silently ignore failures that affect persistence, identity, permissions, retrieval correctness, transformation outputs, workflow state, or auditability. | Material failures produce visible job status, error records, audit records, or caller errors. | +| FR-242 | P0 | Validate inputs | The system shall validate asset, metadata, query, transformation, workflow, permission, export, and agent-operation inputs before execution. | Invalid input fails before partial state change unless the operation explicitly supports partial completion. | +| FR-243 | P0 | Report partial failures | The system shall report partial failures in batch ingestion, transformation, workflow, query, and export operations. | A batch operation reports succeeded, failed, skipped, quarantined, and retriable items separately. | +| FR-244 | P1 | Support idempotency | The system should support idempotency keys or equivalent safeguards for create, ingest, transform, workflow, and export operations where duplicate execution would be harmful. | A repeated request with the same idempotency context does not create unintended duplicate assets or jobs. | +| FR-245 | P1 | Support conflict detection | The system should detect concurrent update conflicts for content, metadata, relationships, policies, and workflow state. | A conflicting update returns a structured conflict response with the current version or resolution guidance. | --- -### FR-052: Provide Workflow Results +## 5. Functional Constraints -The system must return results of workflow execution. +The following constraints apply across all functional requirements: + +* The system must be **API-first** and must not require a specific user interface or CLI for core operation. +* The system must remain **format-agnostic** and must not be constrained to one authoring or storage format. +* The system must remain **provider-neutral** with respect to AI model provider, embedding model, search engine, workflow engine, storage backend, and deployment platform where practical. +* The system must treat **stable asset identity, source provenance, permissions, auditability, and transformation lineage** as core functional concerns. +* The system must not use transformations, workflows, exports, search snippets, or AI-generated answers to bypass access controls. +* The system must distinguish **source content**, **normalized representation**, and **derived artifacts**. +* The system must support both human and machine actors, including applications, automation systems, and AI agents. +* The system must surface material failure states explicitly through structured errors, job status, audit events, or operator-visible diagnostics. --- -## 3.7 AI Interaction +## 6. Core Capability KPIs -### FR-060: Support AI-Driven Operations +The following KPIs should be used to evaluate implementation quality and to compare the engine against relevant alternatives. -The system must allow AI agents to: - -* Access knowledge -* Trigger transformations -* Participate in workflows +| Capability | Primary KPIs | +|---|---| +| Multi-source ingestion | Connector coverage; ingestion success rate; source-update-to-index latency | +| Format normalization and extraction | Extraction accuracy or F1; unsupported-format rate; processing cost per asset | +| Persistent asset identity | Duplicate-detection rate; identity collision rate; percentage of assets with stable IDs | +| Metadata and classification | Metadata completeness; classification accuracy; manual correction rate | +| Context modeling and relationships | Relationship coverage; graph/query completeness; average context depth per asset | +| Search and retrieval | Precision@k or NDCG; p95 query latency; zero-result rate | +| Grounded AI answers and RAG | Grounded-answer accuracy; citation precision; unsupported-claim rate | +| Permissions and access control | Permission fidelity; access violation rate; policy propagation latency | +| Governance and lifecycle management | Retention-policy coverage; audit response time; legal-hold completeness | +| Versioning and provenance | Provenance completeness; version recovery success; change traceability coverage | +| Workflow orchestration | Workflow completion rate; manual-touch reduction; exception backlog | +| Intelligent document processing | Field extraction F1; straight-through processing rate; human validation time | +| API-first access | API uptime; p95 API latency; developer time to first integration | +| Extensibility and integration | Extension deployment time; integration count; breaking-change frequency | +| Collaboration and review | Review turnaround time; active contributor rate; correction acceptance rate | +| Agent-safe operation | Agent task success rate; human-intervention rate; policy-violation rate | +| Observability and administration | Mean time to detect or resolve failures; job failure rate; cost per indexed or answered item | +| Scalability and performance | Indexing throughput; p95/p99 latency; maximum tested corpus size | +| Data portability and lock-in control | Export completeness; migration success rate; proprietary-dependency count | +| User and developer experience | Time to complete common task; adoption rate; developer satisfaction | --- -### FR-061: Maintain Context for AI Interaction +## 7. MVP Functional Compliance -The system must provide contextual information to support AI-driven operations. +A system can be considered compliant with the MVP interpretation of this FRS when the following P0 behavior is demonstrably implemented: + +1. Assets can be created, assigned stable IDs, retrieved, updated, grouped, retired, and governed through APIs. +2. Baseline heterogeneous formats can be ingested and normalized into a common representation. +3. Source provenance is preserved for ingested assets. +4. Metadata, classification, contextual entities, and relationships can be created, queried, and updated. +5. Search and filtered retrieval work across content, metadata, lifecycle state, source context, and relationships. +6. Retrieval respects permissions and policy constraints. +7. Transformations produce traceable derived artifacts with source lineage. +8. Workflows can be executed, tracked, retried, canceled, and audited. +9. Material operations produce audit events. +10. Human, application, automation, and AI-agent actors are represented explicitly. +11. AI agents can only act through bounded, permissioned, auditable operations. +12. Structured errors and partial-failure reports are available for invalid or failed operations. +13. Operators can inspect job state and perform basic recovery actions. --- -## 3.8 Integration with External Tools +## 8. Traceability -### FR-070: Integrate with Tooling +### 8.1 PRD-to-FRS Coverage -The system must allow integration with external tooling (e.g. markitect-tool). +| PRD Concept | FRS Coverage | +|---|---| +| Stable knowledge asset identity | FR-001–FR-010 | +| Ingestion and normalization | FR-020–FR-030 | +| Metadata, classification, and contextualization | FR-040–FR-050 | +| Search, query, and retrieval | FR-060–FR-071 | +| Traceable transformation and derived artifacts | FR-080–FR-090 | +| Workflow and job orchestration | FR-100–FR-110 | +| Permissions, governance, audit, and lifecycle | FR-120–FR-132 | +| Versioning and provenance | FR-140–FR-146 | +| Agent-safe operation | FR-160–FR-169 | +| API-first access, integration, and extensibility | FR-180–FR-188 | +| Observability and administration | FR-200–FR-207 | +| Export, portability, and migration | FR-220–FR-225 | +| Structured error handling and correctness | FR-240–FR-245 | --- -### FR-071: Accept External Processing Results +### 8.2 Corporate Use-Case Coverage -The system must accept outputs from external tools and incorporate them into knowledge. +| Corporate Use Case | Most Relevant FRS Areas | +|---|---| +| Enterprise AI knowledge access and grounded assistants | Retrieval, context modeling, permissions, provenance, grounded AI workflows, agent-safe operation | +| Document-centric process automation | Ingestion, extraction, transformation, workflow, human review, audit, lifecycle | +| Governance, records, compliance, and audit readiness | Permissions, governance, lifecycle, audit, versioning, export, reporting | +| Secure content collaboration and file-service modernization | Asset identity, metadata, relationships, permissions, source references, retrieval | +| Legal and professional-services knowledge work | Contextual entities, strict permissions, provenance, relationship modeling, review, audit | +| Customer service and support knowledge | Search, classification, freshness/lifecycle state, review, grounded answers, feedback | +| Digital content supply chain and omnichannel publishing | Transformation, derived artifacts, workflow, approval, publishing adapters, export | +| Enterprise application content services | API-first access, adapters, contextual entities, relationships, workflows, events | +| R&D, engineering, technical, and project knowledge reuse | Context modeling, relationship retrieval, provenance, semantic retrieval, dependency analysis | +| Digital asset and rich-media operations | Media-derived representations, metadata, rights, renditions, rich-media retrieval | +| Corporate intranet, policy, onboarding, and team knowledge base | Search, metadata, lifecycle, review, publishing consumers, application APIs | +| Custom knowledge-backed applications | APIs, schemas, extensibility, export, provider neutrality, workflow services | --- -## 3.9 API Interaction - -### FR-080: Provide API Access - -The system must expose its capabilities through a programmatic interface. - ---- - -### FR-081: Support External Invocation - -The system must allow external systems to invoke operations on knowledge. - ---- - -## 3.10 Error Handling - -### FR-090: Provide Structured Errors - -The system must return structured error information for invalid operations. - ---- - -### FR-091: Avoid Silent Failures - -The system must not silently ignore errors affecting correctness. - ---- - -## 4. Functional Constraints - -* Functions must be accessible through service interfaces -* System must support heterogeneous data formats -* AI-related functions must operate independently of specific providers -* System must not require CLI-based interaction - ---- - -## 5. Traceability - -| PRD Concept | FRS Coverage | -| ---------------------------- | ------------- | -| Knowledge persistence | FR-001–FR-004 | -| Organization & relationships | FR-010–FR-011 | -| Ingestion & normalization | FR-020–FR-021 | -| Query & retrieval | FR-030–FR-031 | -| Transformation & composition | FR-040–FR-041 | -| Workflow orchestration | FR-050–FR-052 | -| AI interaction | FR-060–FR-061 | -| Integration | FR-070–FR-071 | -| API access | FR-080–FR-081 | - ---- - -## 6. Acceptance Perspective +## 9. Acceptance Perspective The system satisfies this FRS when: -* Knowledge can be stored, retrieved, and manipulated via API -* Queries return expected results -* Workflows execute and produce observable outputs -* AI agents can interact with knowledge meaningfully -* Errors are explicit and traceable +* P0 requirements are implemented and verified through repeatable functional tests. +* Each material operation has explicit input, output, error, permission, and audit behavior. +* Assets retain stable identity across common lifecycle changes. +* Ingestion and normalization produce retrievable, contextualized, traceable assets. +* Search, retrieval, transformation, workflow, export, and agent operations enforce permissions consistently. +* Derived artifacts can be traced back to source assets and operation context. +* Workflows expose observable state, outputs, failures, retries, and audit trails. +* AI agents can operate only through explicit, bounded, reviewable, and auditable interfaces. +* Operators can inspect status, diagnose failures, and recover common operational issues. +* The system can be evaluated against the capability KPIs in this document. +--- + +## 10. Requirement Index + +| ID | Priority | Title | Section | +| --- | --- | --- | --- | +| FR-001 | P0 | Create knowledge assets | 1 Knowledge Asset Registry and Persistence | +| FR-002 | P0 | Assign stable asset identity | 1 Knowledge Asset Registry and Persistence | +| FR-003 | P0 | Persist asset state | 1 Knowledge Asset Registry and Persistence | +| FR-004 | P0 | Retrieve assets by identifier | 1 Knowledge Asset Registry and Persistence | +| FR-005 | P0 | Update asset content and metadata | 1 Knowledge Asset Registry and Persistence | +| FR-006 | P0 | Retire or delete assets under policy | 1 Knowledge Asset Registry and Persistence | +| FR-007 | P0 | Group assets into collections | 1 Knowledge Asset Registry and Persistence | +| FR-008 | P0 | Represent original and normalized forms | 1 Knowledge Asset Registry and Persistence | +| FR-009 | P1 | Detect duplicate or repeated ingestion | 1 Knowledge Asset Registry and Persistence | +| FR-010 | P1 | Support aliases and supersession | 1 Knowledge Asset Registry and Persistence | +| FR-020 | P0 | Submit ingestion jobs | 2 Ingestion and Normalization | +| FR-021 | P0 | Ingest baseline heterogeneous formats | 2 Ingestion and Normalization | +| FR-022 | P0 | Record ingestion provenance | 2 Ingestion and Normalization | +| FR-023 | P0 | Normalize content | 2 Ingestion and Normalization | +| FR-024 | P0 | Extract structural elements | 2 Ingestion and Normalization | +| FR-025 | P0 | Expose ingestion status and failures | 2 Ingestion and Normalization | +| FR-026 | P1 | Support incremental re-ingestion | 2 Ingestion and Normalization | +| FR-027 | P1 | Support pluggable extractors and connectors | 2 Ingestion and Normalization | +| FR-028 | P1 | Validate ingestion output | 2 Ingestion and Normalization | +| FR-029 | P2 | Support advanced OCR and layout extraction | 2 Ingestion and Normalization | +| FR-030 | P2 | Support media-derived representations | 2 Ingestion and Normalization | +| FR-040 | P0 | Manage explicit metadata | 3 Metadata, Classification, and Context Modeling | +| FR-041 | P0 | Support standard metadata fields | 3 Metadata, Classification, and Context Modeling | +| FR-042 | P0 | Support custom metadata schemas | 3 Metadata, Classification, and Context Modeling | +| FR-043 | P0 | Assign classifications | 3 Metadata, Classification, and Context Modeling | +| FR-044 | P0 | Define relationships between assets | 3 Metadata, Classification, and Context Modeling | +| FR-045 | P0 | Represent contextual entities | 3 Metadata, Classification, and Context Modeling | +| FR-046 | P0 | Query context | 3 Metadata, Classification, and Context Modeling | +| FR-047 | P1 | Maintain relationship semantics | 3 Metadata, Classification, and Context Modeling | +| FR-048 | P1 | Support inferred metadata review | 3 Metadata, Classification, and Context Modeling | +| FR-049 | P1 | Validate metadata against schemas | 3 Metadata, Classification, and Context Modeling | +| FR-050 | P2 | Support domain-specific context packages | 3 Metadata, Classification, and Context Modeling | +| FR-060 | P0 | Retrieve by asset ID | 4 Search, Query, and Retrieval | +| FR-061 | P0 | Search by text | 4 Search, Query, and Retrieval | +| FR-062 | P0 | Filter by metadata and lifecycle state | 4 Search, Query, and Retrieval | +| FR-063 | P0 | Retrieve by relationship and context | 4 Search, Query, and Retrieval | +| FR-064 | P0 | Return source-grounded result data | 4 Search, Query, and Retrieval | +| FR-065 | P0 | Enforce permission-aware retrieval | 4 Search, Query, and Retrieval | +| FR-066 | P0 | Support stable pagination and sorting | 4 Search, Query, and Retrieval | +| FR-067 | P1 | Support facets and aggregations | 4 Search, Query, and Retrieval | +| FR-068 | P1 | Support semantic retrieval | 4 Search, Query, and Retrieval | +| FR-069 | P1 | Support grounded answer retrieval | 4 Search, Query, and Retrieval | +| FR-070 | P1 | Capture retrieval feedback | 4 Search, Query, and Retrieval | +| FR-071 | P2 | Support federated query patterns | 4 Search, Query, and Retrieval | +| FR-080 | P0 | Execute transformations | 5 Transformation, Composition, and Derived Artifacts | +| FR-081 | P0 | Compose outputs from multiple assets | 5 Transformation, Composition, and Derived Artifacts | +| FR-082 | P0 | Persist derived artifacts | 5 Transformation, Composition, and Derived Artifacts | +| FR-083 | P0 | Record transformation lineage | 5 Transformation, Composition, and Derived Artifacts | +| FR-084 | P0 | Support parameterized transformations | 5 Transformation, Composition, and Derived Artifacts | +| FR-085 | P0 | Enforce transformation permissions | 5 Transformation, Composition, and Derived Artifacts | +| FR-086 | P1 | Support human review for transformations | 5 Transformation, Composition, and Derived Artifacts | +| FR-087 | P1 | Support controlled re-runs | 5 Transformation, Composition, and Derived Artifacts | +| FR-088 | P1 | Compare derived artifacts | 5 Transformation, Composition, and Derived Artifacts | +| FR-089 | P2 | Publish transformation outputs | 5 Transformation, Composition, and Derived Artifacts | +| FR-090 | P2 | Support reusable transformation templates | 5 Transformation, Composition, and Derived Artifacts | +| FR-100 | P0 | Define workflow templates | 6 Workflow and Job Orchestration | +| FR-101 | P0 | Execute workflows | 6 Workflow and Job Orchestration | +| FR-102 | P0 | Track workflow state | 6 Workflow and Job Orchestration | +| FR-103 | P0 | Respect step dependencies | 6 Workflow and Job Orchestration | +| FR-104 | P0 | Return workflow results | 6 Workflow and Job Orchestration | +| FR-105 | P0 | Retry, resume, and cancel jobs | 6 Workflow and Job Orchestration | +| FR-106 | P0 | Audit workflow operations | 6 Workflow and Job Orchestration | +| FR-107 | P1 | Support event and schedule triggers | 6 Workflow and Job Orchestration | +| FR-108 | P1 | Support human tasks | 6 Workflow and Job Orchestration | +| FR-109 | P1 | Maintain exception queues | 6 Workflow and Job Orchestration | +| FR-110 | P2 | Support cross-system orchestration | 6 Workflow and Job Orchestration | +| FR-120 | P0 | Represent actors | 7 Permissions, Governance, Audit, and Lifecycle | +| FR-121 | P0 | Authorize operations | 7 Permissions, Governance, Audit, and Lifecycle | +| FR-122 | P0 | Enforce sensitivity and lifecycle constraints | 7 Permissions, Governance, Audit, and Lifecycle | +| FR-123 | P0 | Preserve source permissions where available | 7 Permissions, Governance, Audit, and Lifecycle | +| FR-124 | P0 | Audit material operations | 7 Permissions, Governance, Audit, and Lifecycle | +| FR-125 | P0 | Query audit history | 7 Permissions, Governance, Audit, and Lifecycle | +| FR-126 | P0 | Fail closed on ambiguous access | 7 Permissions, Governance, Audit, and Lifecycle | +| FR-127 | P1 | Manage retention policies | 7 Permissions, Governance, Audit, and Lifecycle | +| FR-128 | P1 | Support legal hold | 7 Permissions, Governance, Audit, and Lifecycle | +| FR-129 | P1 | Support archival and defensible deletion | 7 Permissions, Governance, Audit, and Lifecycle | +| FR-130 | P1 | Synchronize permission changes | 7 Permissions, Governance, Audit, and Lifecycle | +| FR-131 | P1 | Produce governance reports | 7 Permissions, Governance, Audit, and Lifecycle | +| FR-132 | P2 | Integrate with external policy and DLP systems | 7 Permissions, Governance, Audit, and Lifecycle | +| FR-140 | P1 | Version asset content | 8 Versioning and Provenance | +| FR-141 | P1 | Version metadata and relationships | 8 Versioning and Provenance | +| FR-142 | P1 | Compare and restore versions | 8 Versioning and Provenance | +| FR-143 | P0 | Expose source provenance | 8 Versioning and Provenance | +| FR-144 | P0 | Expose derived-artifact lineage | 8 Versioning and Provenance | +| FR-145 | P1 | Support dependency impact analysis | 8 Versioning and Provenance | +| FR-146 | P2 | Support provenance graph traversal | 8 Versioning and Provenance | +| FR-160 | P0 | Register AI agents as explicit actors | 9 Agent-Safe AI Interaction | +| FR-161 | P0 | Expose a bounded operation catalog | 9 Agent-Safe AI Interaction | +| FR-162 | P0 | Apply permissions to agent operations | 9 Agent-Safe AI Interaction | +| FR-163 | P0 | Provide context packages | 9 Agent-Safe AI Interaction | +| FR-164 | P0 | Audit agent operations | 9 Agent-Safe AI Interaction | +| FR-165 | P0 | Require review gates where policy demands | 9 Agent-Safe AI Interaction | +| FR-166 | P1 | Support grounded AI answer workflows | 9 Agent-Safe AI Interaction | +| FR-167 | P1 | Remain provider neutral | 9 Agent-Safe AI Interaction | +| FR-168 | P1 | Constrain agent tasks | 9 Agent-Safe AI Interaction | +| FR-169 | P2 | Support multi-step agent workflows | 9 Agent-Safe AI Interaction | +| FR-180 | P0 | Provide service APIs | 10 API, Integration, and Extensibility | +| FR-181 | P0 | Provide stable programmatic contracts | 10 API, Integration, and Extensibility | +| FR-182 | P0 | Accept external processing results | 10 API, Integration, and Extensibility | +| FR-183 | P1 | Support source adapters | 10 API, Integration, and Extensibility | +| FR-184 | P1 | Emit events and webhooks | 10 API, Integration, and Extensibility | +| FR-185 | P1 | Support extensible schemas and plugins | 10 API, Integration, and Extensibility | +| FR-186 | P1 | Abstract implementation backends | 10 API, Integration, and Extensibility | +| FR-187 | P1 | Version APIs | 10 API, Integration, and Extensibility | +| FR-188 | P2 | Support extension registry patterns | 10 API, Integration, and Extensibility | +| FR-200 | P0 | Expose job and ingestion status | 11 Observability and Administration | +| FR-201 | P0 | Return correlation identifiers | 11 Observability and Administration | +| FR-202 | P0 | Support administrative recovery actions | 11 Observability and Administration | +| FR-203 | P1 | Expose operational metrics | 11 Observability and Administration | +| FR-204 | P1 | Expose retrieval quality signals | 11 Observability and Administration | +| FR-205 | P1 | Expose AI operation and cost signals | 11 Observability and Administration | +| FR-206 | P1 | Support governance inspection | 11 Observability and Administration | +| FR-207 | P2 | Support policy simulation | 11 Observability and Administration | +| FR-220 | P1 | Export asset packages | 12 Export, Portability, and Migration | +| FR-221 | P1 | Export by scope | 12 Export, Portability, and Migration | +| FR-222 | P1 | Include manifests and integrity data | 12 Export, Portability, and Migration | +| FR-223 | P1 | Support re-import or migration validation | 12 Export, Portability, and Migration | +| FR-224 | P2 | Support long-term archival formats | 12 Export, Portability, and Migration | +| FR-225 | P2 | Produce migration reports | 12 Export, Portability, and Migration | +| FR-240 | P0 | Return structured errors | 13 Error Handling and Functional Correctness | +| FR-241 | P0 | Avoid silent failures | 13 Error Handling and Functional Correctness | +| FR-242 | P0 | Validate inputs | 13 Error Handling and Functional Correctness | +| FR-243 | P0 | Report partial failures | 13 Error Handling and Functional Correctness | +| FR-244 | P1 | Support idempotency | 13 Error Handling and Functional Correctness | +| FR-245 | P1 | Support conflict detection | 13 Error Handling and Functional Correctness | + +--- + +## 11. Open Functional Decisions + +The following decisions should be resolved during architecture and implementation planning: + +* Asset identity strategy: UUID, content fingerprint, source-derived ID, hybrid identity, or pluggable resolver. +* Source-of-truth strategy for permissions: engine-owned, source-synchronized, delegated, or hybrid. +* Minimum baseline format set for MVP and required extraction depth per format. +* Versioning model for content, metadata, relationships, derived artifacts, and workflow state. +* Workflow execution model: embedded engine, external orchestrator, or adapter-based hybrid. +* Search architecture: lexical only for MVP, semantic retrieval in V1, or combined retrieval from the start. +* Provenance storage model: relational, event-sourced, graph-backed, or hybrid. +* Export package format and schema versioning policy. +* Extension boundary for source connectors, transformation modules, policy modules, and AI/model adapters. +* Human review model: built-in review primitives only, external task system integration, or both. + +--- + +## 12. Stability Note + +Changes to this FRS should be treated as deliberate changes to externally observable product behavior. Implementation details may change independently, but requirements related to identity, provenance, permission enforcement, auditability, traceable transformation, and agent-safe operation should remain stable unless the product scope is intentionally revised. diff --git a/wiki/ProductRequirementsDocument.md b/wiki/ProductRequirementsDocument.md index ea8e48a..101f4b5 100644 --- a/wiki/ProductRequirementsDocument.md +++ b/wiki/ProductRequirementsDocument.md @@ -1,7 +1,11 @@ -# Kontextual Engine Product Requirements Document V0.1 +# Kontextual Engine Product Requirements Document V0.2 ## kontextual-engine +Prepared: 2026-05-05 +Document type: Product requirements document +Status: Scope refinement draft + --- ## 1. Product Overview @@ -14,9 +18,28 @@ ### 1.2 Product Definition -kontextual-engine is an **AI-first, headless knowledge and content engine** that manages, transforms, and operates structured information across heterogeneous data sources. +`kontextual-engine` is a **headless knowledge operations engine** for making heterogeneous information assets persistent, contextual, governed, retrievable, transformable, and agent-operable. -It provides a **persistent, service-oriented runtime for knowledge systems**, enabling automated and agent-driven workflows over structured and semi-structured data. +The product provides reusable backend capabilities for systems that need to manage scattered documents, files, records, notes, datasets, generated outputs, and content collections as durable knowledge assets rather than as disconnected storage items. + +It can support CMS-like, DMS-like, ECM-like, file-service, knowledge-base, research-support, and AI-assisted workflow scenarios, but it should not be reduced to any single one of those categories. + +--- + +### 1.3 Product Positioning + +The product is not primarily a document editor, file browser, CMS, enterprise search product, vector database, or finished end-user application. It is the engine layer that allows such applications to operate knowledge through stable identity, contextual structure, governed access, traceable transformation, and automation-ready interfaces. + +The market alternatives cluster into several categories: + +* enterprise content, document, and records platforms +* secure file collaboration and content governance systems +* AI enterprise search, RAG, and agent platforms +* headless CMS and composable content platforms +* team knowledge bases and collaboration workspaces +* developer-oriented backend, search, and content infrastructure + +`kontextual-engine` should compete by being **context-first, traceable, composable, API-first, and agent-safe**, not by cloning a mature suite in any one category. --- @@ -24,218 +47,544 @@ It provides a **persistent, service-oriented runtime for knowledge systems**, en ### 2.1 Problem Statement -Modern knowledge systems face several limitations: +Corporate information is valuable but often operationally weak. It is spread across files, folders, repositories, documents, databases, collaboration tools, generated AI outputs, and application-specific records. -* Content is fragmented across formats, tools, and storage systems -* Automation and orchestration of knowledge workflows are ad-hoc -* AI interaction lacks stable, persistent context -* Systems either focus on tooling (too low-level) or platforms (too rigid) +This causes several recurring problems: -This results in inefficient knowledge reuse, poor traceability, and limited scalability. +* assets lack durable identity beyond filenames, paths, URLs, or source-system IDs +* metadata, relationships, ownership, provenance, and lifecycle state are incomplete or inconsistent +* retrieval is fragmented across tools and does not reliably preserve permissions or context +* AI assistants lack governed, traceable, source-grounded context +* document-centric workflows depend on manual routing, review, copying, extraction, and summarization +* generated summaries, reports, classifications, and derived artifacts can become detached from their sources +* governance, auditability, retention, and access control are difficult to enforce consistently +* custom knowledge-backed applications require repeated rebuilding of ingestion, retrieval, workflow, and context infrastructure + +The result is inefficient knowledge reuse, weak traceability, poor automation leverage, duplicated effort, and limited trust in AI-assisted knowledge work. --- -### 2.2 Intended Outcomes +### 2.2 Utility Demand -kontextual-engine enables: +The product addresses the demand for a backend system that can: -* Persistent, structured **knowledge environments** across domains -* Unified handling of **multi-format data and files** -* AI-driven **interaction, transformation, and orchestration** of knowledge -* Efficient **retrieval, composition, and reuse of information** -* Stable APIs for integrating knowledge into applications and systems +* ingest knowledge assets from heterogeneous sources and formats +* preserve original source references and provenance +* assign durable asset identity independent of storage location +* enrich assets with metadata, classification, relationships, lifecycle state, and operational context +* expose knowledge through reliable retrieval, APIs, workflows, and agent-compatible interfaces +* transform assets into summaries, extracts, reports, structured representations, and generated artifacts +* make transformations traceable back to their sources and operation history +* enforce permissions, policy constraints, review gates, and audit trails +* support repeatable knowledge workflows rather than one-off manual operations + +The core utility is to turn fragmented information into **operable knowledge**. --- -### 2.3 Success Criteria +### 2.3 Intended Outcomes + +`kontextual-engine` should enable: + +* persistent, structured knowledge asset management across domains +* unified handling of multi-format documents, files, records, datasets, notes, and generated outputs +* context-rich retrieval for humans, services, applications, automation systems, and AI agents +* traceable transformation and composition of knowledge artifacts +* workflow automation for ingestion, enrichment, review, validation, publication, synchronization, archival, and maintenance +* stable APIs for building knowledge-backed applications and services +* governed AI-assisted operation without bypassing access controls or auditability + +--- + +### 2.4 Product Success Criteria The product is successful when: -* Knowledge can be **persisted, queried, and transformed across formats** -* AI agents can operate on knowledge with **context continuity and efficiency** -* Workflows can be **automated and orchestrated reliably** -* Systems can integrate with the engine via **clear, stable interfaces** -* markitect-tool and other primitives can be used seamlessly within the engine +* knowledge assets can be persisted, identified, queried, related, governed, versioned, and transformed across formats +* retrieval can return useful, permission-aware, source-grounded results with measurable quality +* transformations create traceable derived artifacts rather than detached outputs +* workflows can be automated, monitored, retried, and audited reliably +* AI agents can inspect, retrieve, enrich, transform, and maintain knowledge through explicit, bounded, permissioned interfaces +* customers can build CMS-like, DMS-like, ECM-like, file-service, knowledge-base, research-support, and AI-assisted workflow applications without rebuilding the same knowledge infrastructure repeatedly --- -## 3. Scope Definition +## 3. Target Customers and Users -### 3.1 In Scope +### 3.1 Target Customer Profiles -* Persistent storage and management of structured knowledge -* Multi-format data handling (markdown, documents, files, datasets) -* Knowledge ingestion, normalization, and indexing -* Workflow orchestration for transformation, generation, and analysis -* API and service interfaces for knowledge access and operations -* AI/LLM-driven interaction and automation -* Integration with lower-layer tooling (e.g. markitect-tool) +`kontextual-engine` is most relevant for organizations that need durable knowledge operations across heterogeneous information assets. + +High-fit corporate contexts include: + +* regulated organizations that require governance, auditability, lifecycle management, and access control +* knowledge-heavy organizations where employees repeatedly search, summarize, compose, and reuse information +* teams building AI assistants or RAG workflows that require permission-aware, source-grounded context +* organizations modernizing document-centric processes such as intake, review, approval, routing, and archival +* product teams building knowledge-backed internal or customer-facing applications +* research, engineering, consulting, legal, support, and operations teams with evolving knowledge collections --- -### 3.2 Out of Scope +### 3.2 User Groups -* Low-level markdown parsing or transformation primitives -* CLI-first tooling or standalone document manipulation -* Domain-specific knowledge models or project-level content -* Visual UI applications (headless system only) -* Direct ownership of LLM provider integrations (delegated to libraries like `llm-connect`) +The product should serve the following users and operators: + +* **Developers** building knowledge-driven applications, integrations, workflows, and services +* **Platform operators** managing durable knowledge services, indexing jobs, workflows, permissions, and system health +* **Business process owners** defining knowledge workflows, review rules, lifecycle policies, and governance expectations +* **Knowledge workers and analysts** retrieving, validating, composing, and reusing knowledge through applications built on the engine +* **Automation systems** executing repeatable ingestion, enrichment, synchronization, validation, and transformation tasks +* **AI agents** inspecting, retrieving, summarizing, classifying, enriching, transforming, and maintaining knowledge assets through controlled interfaces + +The engine should be usable by humans through applications, by systems through APIs, by workflows through jobs/events, and by agents through explicit tools. --- -### 3.3 Boundary Clarification +## 4. Corporate Use Cases Ranked by Economic Value -kontextual-engine provides a **runtime system**, not primitives or projects: +The following use-case ranking translates market findings into product strategy. Rankings are directional and should guide prioritization, not imply that every organization will realize value in the same order. -* Tooling primitives → `markitect-tool` -* Project/application usage → `infospace-bench` +| Rank | Use Case | Economic-Value Rationale | Product Implication | Main KPIs | +|---:|---|---|---|---| +| 1 | Enterprise AI knowledge access and grounded assistants | Broad horizontal value across knowledge workers; reduces search, repeated questions, summarization, and context reconstruction. | Permission-aware retrieval, source grounding, citations, context modeling, agent-safe access must be foundational. | Time saved per employee; answer accuracy; citation precision; active adoption; repeated-question reduction | +| 2 | Document-centric process automation | High direct ROI where documents trigger work such as invoices, claims, contracts, HR packets, case folders, and approvals. | Workflows, extraction, classification, validation, routing, and traceable transformation must be core capabilities. | Manual-touch reduction; cycle-time reduction; straight-through processing rate; exception rate | +| 3 | Governance, records, compliance, and audit readiness | High risk-avoidance value in regulated industries; supports audit evidence, retention, legal hold, privacy response, and access control. | Governance cannot be bolted on later; provenance, lifecycle state, permissions, and audit logs belong in the core model. | Retention-policy coverage; legal-hold completeness; audit response time; access violations | +| 4 | Secure content collaboration and file-service modernization | Shared drives, duplicated files, email attachments, and uncontrolled sharing remain major pain points. | The engine should provide durable identity and context for files rather than clone sync-and-share tools. | Permission hygiene; duplicate-file reduction; secure-sharing adoption; external-collaboration cycle time | +| 5 | Legal and professional-services knowledge work | High-value, confidential, precedent-heavy, matter-centric documents create strong demand for contextual retrieval and strict boundaries. | The engine should support domain context, relationship modeling, and strong access segmentation. | Matter retrieval time; precedent reuse; confidentiality incidents; review cycle time | +| 6 | Customer service and support knowledge | Improves self-service, agent productivity, and issue resolution when knowledge is current and trusted. | Review, verification, freshness tracking, ownership, and source-to-answer traceability should be supported. | Self-service deflection; first-contact resolution; average handle time; knowledge freshness | +| 7 | Digital content supply chain and omnichannel publishing | Valuable for marketing, commerce, brand, and media organizations where content velocity and reuse affect revenue. | Publishing and content supply-chain use cases should be supported as consumers of the engine, not define the engine. | Time to publish; content reuse; localization speed; campaign throughput | +| 8 | Enterprise application content services | Content becomes valuable when embedded into ERP, CRM, HR, ITSM, procurement, service, and line-of-business workflows. | API-first and integration-first design are required. | Content-in-context coverage; workflow completion time; task-switching reduction; integration count | +| 9 | R&D, engineering, technical, and project knowledge reuse | Reduces duplicate research, preserves project memory, and improves decision traceability. | Relationship modeling, provenance, project memory, and cross-source retrieval are important. | Reuse rate; duplicate-work reduction; expert-finding time; onboarding time | +| 10 | Digital asset and rich-media operations | Valuable where assets require metadata, variants, rights, renditions, and searchability. | Rich media should be modeled as knowledge assets, but DAM-specific features are later-stage scope. | Asset reuse rate; rights-compliance rate; media search success; delivery time | +| 11 | Corporate intranet, policy, onboarding, and team knowledge base | Broad but often lower direct economic value; reduces repeated questions and improves onboarding. | Applications can be built on the engine, but intranet/wiki UI should not drive core scope. | Time to onboard; policy findability; stale-page rate; active usage | +| 12 | Custom knowledge-backed applications and internal developer platforms | Medium direct value but high strategic leverage for organizations building domain-specific products. | Stable APIs, extensibility, portability, and composable capabilities are core. | Time to build; API coverage; search relevance; extensibility; operating cost | --- -## 4. Functional Expectations +## 5. Scope Definition -### 4.1 Core Capabilities +### 5.1 In Scope -The product must support: +The following are in scope for `kontextual-engine`: -* **Knowledge Persistence** - Store and manage structured knowledge across collections and domains - -* **Ingestion & Normalization** - Convert heterogeneous data into structured representations - -* **Transformation & Composition** - Apply workflows to generate, modify, and combine knowledge artifacts - -* **Query & Retrieval** - Provide efficient access to knowledge via APIs - -* **Workflow Orchestration** - Coordinate multi-step operations and dependencies - -* **AI Interaction Layer** - Enable LLM-driven interaction, reasoning, and automation +* knowledge asset registry with stable identity +* persistent management of structured and semi-structured knowledge assets +* ingestion from multiple sources and formats +* source reference preservation and provenance tracking +* metadata, classification, relationships, and lifecycle state +* normalization and extraction of content and structure +* search, filtering, querying, and API-based retrieval +* permission-aware and policy-aware access patterns +* transformation into summaries, extracts, views, reports, structured representations, and generated artifacts +* traceability from derived artifacts back to source assets and operations +* workflow orchestration for recurring knowledge processes +* audit logging of material operations +* observability for ingestion, retrieval, transformation, workflows, and agent operations +* API-first service interfaces +* controlled agent operation through explicit, bounded, auditable interfaces +* extensibility through adapters, connectors, plugins, schemas, events, or hooks +* export and portability of assets, metadata, relationships, versions, audit history, and derived artifacts --- -### 4.2 Interaction Modes +### 5.2 Out of Scope -* API-first (service endpoints) -* Agent-driven execution -* Programmatic integration +The following are out of scope for the engine identity: + +* a finished end-user ECM, DMS, CMS, intranet, or file-sharing application by itself +* a visual website builder or page-authoring suite +* a standalone document editor +* a simple file browser or sync-and-share client +* a format-specific markdown manipulation tool +* a pure vector database, search index, or RAG wrapper +* a one-off collection of automation scripts +* a domain-specific knowledge base with hard-coded domain semantics +* direct ownership of every possible enterprise connector from the initial version +* direct coupling to one LLM provider, embedding model, storage backend, search engine, or deployment platform + +Such features may exist as integrations, extensions, applications, adapters, or deployment choices, but they should not define the core product scope. --- -## 5. Non-Functional Expectations +### 5.3 Boundary Clarification -### 5.1 Performance +`kontextual-engine` provides reusable engine capabilities. Applications, user interfaces, authoring tools, source-specific connectors, deployment infrastructure, and domain-specific packages may depend on the engine, but they should remain consumers or extensions of it. -* Scalable handling of large and heterogeneous data sets -* Efficient retrieval and transformation operations +The core boundary is: ---- - -### 5.2 Reliability - -* Consistent and deterministic system behavior where applicable -* Robust handling of failures in workflows and external dependencies - ---- - -### 5.3 Extensibility - -* Modular architecture supporting plugins and adapters -* Ability to integrate new data sources and workflows - ---- - -### 5.4 Usability - -* Clear API surface for integration -* Predictable behavior across operations -* Minimal friction for common workflows - ---- - -## 6. Assumptions and Dependencies - -### 6.1 Assumptions - -* Knowledge systems benefit from persistent, structured representation -* AI agents are primary consumers and operators of knowledge workflows -* Multiple data formats must be supported - ---- - -### 6.2 Dependencies - -* markitect-tool for markdown-native operations -* llm-connect (or equivalent) for LLM integration -* Underlying storage systems (filesystem, databases, object storage) - ---- - -## 7. Constraints - -* Must remain **format-agnostic at the system level** -* Must maintain **clear separation from tooling and project layers** -* Must avoid vendor lock-in and provider-specific coupling -* Must support both deterministic and AI-driven operations - ---- - -## 8. Risks - -* Scope creep toward full application/platform ownership -* Over-complex orchestration reducing usability -* Tight coupling to specific data formats or tools -* AI-driven behavior reducing predictability - ---- - -## 9. Related Systems - -* **markitect-tool** – syntax layer (markdown primitives) -* **infospace-bench** – application layer (knowledge projects) -* **llm-connect** – LLM abstraction layer - ---- - -## 10. Ecosystem Context - -This product is part of a layered knowledge system: - -```text id="m2k9s4" -markitect-tool → makes markdown structured and manipulable -kontextual-engine → makes knowledge persistent and operable -infospace-bench → makes knowledge concrete and meaningful +```text +knowledge sources + -> ingestion and normalization + -> stable asset identity + -> metadata, context, relationships, provenance, and lifecycle state + -> governed retrieval and transformation + -> workflow operation + -> APIs, automation interfaces, and agent-safe tools + -> downstream applications and user experiences ``` -Layers: - -* **Syntax layer** → markitect-tool -* **System layer** → kontextual-engine -* **Application layer** → infospace-bench +The engine owns the middle layer: identity, context, governance, retrieval, transformation, workflow, and operational interfaces. --- -## 11. PRD Type +## 6. Functional Requirements -**Hybrid / Boundary PRD** +### 6.1 Priority Model -This PRD defines system-level intent and constraints while allowing architectural flexibility and iterative development. +Requirements use the following priority levels: + +* **P0 — Core engine requirement:** necessary for the product to be credible as a knowledge operations engine +* **P1 — Enterprise readiness requirement:** important for corporate use, scale, governance, and operational reliability +* **P2 — Expansion requirement:** useful for mature deployments, verticals, or advanced workflows --- -# 🧠 Final insight (important) +### 6.2 Requirement Table -If markitect-tool was about: +| ID | Priority | Requirement | Acceptance Signal | +|---|---|---|---| +| FR-01 | P0 | Maintain a knowledge asset registry with stable asset IDs independent of file path, filename, storage backend, or representation. | Assets can be renamed, moved, re-ingested, or transformed without losing identity or history. | +| FR-02 | P0 | Preserve source references and provenance for ingested assets. | Each asset can report origin, source location, ingestion time, extraction method, and source-system reference where available. | +| FR-03 | P0 | Ingest a baseline set of heterogeneous formats. | Text, markdown, common office documents, PDFs, and structured datasets can be represented as knowledge assets. | +| FR-04 | P0 | Normalize extracted content into a common internal representation suitable for retrieval, metadata, transformation, and workflows. | Assets from different formats can be searched, filtered, transformed, and related through common APIs. | +| FR-05 | P0 | Support explicit metadata and classification. | Assets can store and update type, owner, domain, project/context, sensitivity, lifecycle state, tags, and custom metadata. | +| FR-06 | P0 | Support relationships between assets and contextual entities. | Assets can be linked to other assets, people, projects, cases, topics, processes, source systems, and generated artifacts. | +| FR-07 | P0 | Provide search and filtered retrieval. | Users, applications, and agents can retrieve assets by text, metadata, relationship, lifecycle state, and source context. | +| FR-08 | P0 | Provide API-first access to assets, metadata, retrieval, transformations, workflows, and audit data. | Core operations are available through stable service interfaces without requiring a specific UI. | +| FR-09 | P0 | Create traceable derived artifacts through transformations. | Summaries, extracts, reports, generated outputs, and structured representations record source assets, operation type, actor, parameters, and time. | +| FR-10 | P0 | Support basic workflow/job orchestration. | Ingestion, enrichment, validation, transformation, review, publication, synchronization, and archival jobs can be executed, tracked, retried, and inspected. | +| FR-11 | P0 | Maintain an audit log for material operations. | Asset creation, ingestion, update, deletion, transformation, permission change, workflow action, and agent operation events are recorded. | +| FR-12 | P0 | Provide an initial permission and policy model. | Retrieval, transformation, and agent operations can be constrained by role, group, asset, sensitivity, lifecycle state, or source policy. | +| FR-13 | P0 | Provide explicit agent-safe operation interfaces. | AI agents can only act through defined operations with permission checks, audit logs, and optional review gates. | +| FR-14 | P1 | Support versioning and change history. | Asset content, metadata, relationships, and derived artifacts can be compared, restored, and traced across versions. | +| FR-15 | P1 | Support semantic retrieval and grounded AI answer workflows. | Answers can cite supporting assets and respect permissions and source provenance. | +| FR-16 | P1 | Support advanced extraction and intelligent document processing. | Document classification, field extraction, table extraction, OCR/layout extraction, and validation workflows are supported where configured. | +| FR-17 | P1 | Provide lifecycle management and governance controls. | Retention, review state, archival, legal hold, defensible deletion, and policy enforcement can be configured. | +| FR-18 | P1 | Support human review and approval steps. | Workflows can require human validation for transformations, classifications, publications, destructive operations, or agent actions. | +| FR-19 | P1 | Provide observability and admin controls. | Operators can inspect ingestion status, workflow status, failures, retrieval quality signals, AI usage, permissions, audit logs, and operational cost. | +| FR-20 | P1 | Support extensibility through adapters, schemas, plugins, webhooks, events, or SDKs. | New sources, transformations, metadata models, workflow steps, and downstream integrations can be added without changing the core engine. | +| FR-21 | P1 | Support data portability and export. | Assets, metadata, relationships, versions, provenance, audit logs, and derived artifacts can be exported in usable formats. | +| FR-22 | P2 | Support rich media and digital asset workflows. | Images, video, audio, renditions, rights metadata, variants, and media-specific search can be represented and governed. | +| FR-23 | P2 | Support deep enterprise application integrations. | ERP, CRM, ITSM, HR, support, procurement, and line-of-business integrations can attach knowledge assets to operational entities. | +| FR-24 | P2 | Support advanced agent workflows. | Multi-step agent workflows can plan, execute, request review, recover from failures, and produce traceable artifacts under policy constraints. | -> **“making knowledge manipulable”** +--- -Then kontextual-engine is about: +## 7. Core Capabilities and Quality KPIs -> **“making knowledge operational”** +The following capability model should be used to compare `kontextual-engine` against alternatives and to assess implementation maturity. -That distinction will keep this repo from turning into an unbounded platform. +| Capability | Description | Main KPIs | +|---|---|---| +| Multi-source ingestion | Bring in files, documents, datasets, records, generated outputs, and application content. | Connector coverage; ingestion success rate; source-update-to-index latency | +| Format normalization and extraction | Extract text, structure, fields, tables, layout, entities, and metadata where possible. | Extraction accuracy/F1; unsupported-format rate; processing cost per asset | +| Persistent asset identity | Maintain stable identity independent of path, filename, storage backend, or representation. | Duplicate-detection rate; identity collision rate; percentage of assets with stable IDs | +| Metadata and classification | Capture explicit and inferred metadata such as type, owner, sensitivity, lifecycle state, topic, and source. | Metadata completeness; classification accuracy; manual correction rate | +| Context modeling and relationships | Connect assets to projects, people, cases, processes, topics, source systems, and other assets. | Relationship coverage; graph/query completeness; average context depth per asset | +| Search and retrieval | Provide keyword, semantic, filtered, faceted, permission-aware, and API-accessible retrieval. | Precision@k/NDCG; p95 query latency; zero-result rate | +| Grounded AI answers and RAG | Generate source-grounded answers, summaries, and analyses over governed content. | Grounded-answer accuracy; citation precision; unsupported-claim rate | +| Permissions and access control | Enforce roles, groups, policies, sharing rules, lifecycle state, and source-system restrictions. | Permission fidelity; access violation rate; policy propagation latency | +| Governance and lifecycle management | Support retention, legal hold, archival, review, deletion, compliance evidence, and policy state. | Retention-policy coverage; audit response time; legal-hold completeness | +| Versioning and provenance | Track origin, changes, actors, operations, dependencies, and derived artifacts. | Provenance completeness; version recovery success; change traceability coverage | +| Workflow orchestration | Automate ingestion, enrichment, validation, approval, publication, synchronization, and archival. | Workflow completion rate; manual-touch reduction; exception backlog | +| Intelligent document processing | Classify documents, extract fields, validate data, and route work. | Field extraction F1; straight-through processing rate; human validation time | +| API-first access | Expose assets, metadata, search, transformations, workflows, permissions, and audit logs through stable APIs. | API uptime; p95 API latency; developer time to first integration | +| Extensibility and integration | Support adapters, plugins, custom schemas, events, webhooks, SDKs, and external backends. | Extension deployment time; integration count; breaking-change frequency | +| Collaboration and review | Enable humans to inspect, correct, annotate, approve, reject, and curate knowledge assets. | Review turnaround time; active contributor rate; correction acceptance rate | +| Agent-safe operation | Let AI agents act through explicit, permissioned, auditable, reviewable operations. | Agent task success rate; human-intervention rate; policy-violation rate | +| Observability and administration | Provide system health, job, cost, permission, AI, retrieval, and workflow visibility. | Mean time to detect/resolve failures; job failure rate; cost per indexed or answered item | +| Scalability and performance | Handle growth in content volume, users, queries, transformations, and AI workloads. | Indexing throughput; p95/p99 latency; maximum tested corpus size | +| Data portability and lock-in control | Export assets, metadata, relationships, versions, audit trails, and generated artifacts. | Export completeness; migration success rate; proprietary-dependency count | +| User and developer experience | Make common tasks usable for developers, operators, applications, humans, and agents. | Time to complete common task; adoption rate; developer satisfaction | +--- + +## 8. Non-Functional Requirements + +### 8.1 Performance + +* Retrieval APIs should be optimized for predictable latency under realistic content and permission loads. +* Ingestion and transformation jobs should support batching, retries, and incremental processing. +* The system should measure p95 and p99 latency for retrieval, API operations, and workflow execution. + +Recommended KPIs: + +* p95 query latency +* p95 API latency +* indexing throughput +* source-update-to-index latency +* transformation throughput + +--- + +### 8.2 Reliability + +* Workflow steps should be retryable, inspectable, and recoverable after partial failure. +* Ingestion should be idempotent where possible and should not corrupt identity, versions, or provenance on reprocessing. +* External dependency failures should be isolated and visible to operators. + +Recommended KPIs: + +* workflow completion rate +* job failure rate +* reprocessing success rate +* mean time to recover failed jobs + +--- + +### 8.3 Governance and Security + +* Permissions and policy constraints must be enforced across retrieval, transformation, workflow, export, and agent operations. +* Sensitive assets should carry explicit sensitivity, ownership, lifecycle, and policy metadata where available. +* Audit logs must capture actor, operation, asset, time, outcome, and relevant policy context. + +Recommended KPIs: + +* permission fidelity +* access violation rate +* audit-log completeness +* policy propagation latency +* retention-policy coverage + +--- + +### 8.4 Extensibility + +* Connectors, storage backends, indexing systems, AI/model providers, metadata schemas, workflow steps, and transformation operations should be pluggable where practical. +* The core engine should avoid hard-coding one source system, format, LLM provider, or deployment environment. + +Recommended KPIs: + +* extension deployment time +* supported integration patterns +* breaking-change frequency +* developer time to first integration + +--- + +### 8.5 Observability + +* Operators should be able to inspect ingestion status, indexing health, workflow runs, failures, permissions, audit events, AI operations, and cost drivers. +* The system should expose enough telemetry to compare implementation quality against capability KPIs. + +Recommended KPIs: + +* mean time to detect failures +* mean time to resolve failures +* cost per indexed asset +* cost per answer or transformation +* job queue age + +--- + +### 8.6 Portability and Lock-In Control + +* The system should support exporting knowledge assets, metadata, relationships, provenance, versions, audit trails, and derived artifacts. +* Internal abstractions should minimize avoidable dependency on a single vendor or proprietary format. + +Recommended KPIs: + +* export completeness +* migration success rate +* proprietary-dependency count + +--- + +## 9. MVP and Release Priorities + +### 9.1 MVP / P0 Capability Set + +A credible first version should include: + +1. Asset registry with stable IDs +2. Source provenance and ingestion history +3. Multi-format ingestion for a limited common set of formats +4. Metadata and classification model +5. Relationship/context model +6. Basic versioning or change tracking +7. Search and filtered retrieval +8. API access to core operations +9. Traceable transformations producing derived artifacts +10. Simple permission and policy model +11. Basic workflow/job orchestration +12. Audit log for material operations +13. Agent-safe API operations with explicit permission checks +14. Basic observability for jobs, failures, assets, and retrieval + +--- + +### 9.2 V1 / Enterprise-Ready Expansion + +An enterprise-ready version should add: + +* semantic retrieval and grounded AI answer workflows +* stronger versioning and provenance graph +* advanced extraction and intelligent document processing +* configurable lifecycle, retention, review, and archival policies +* human review and approval workflows +* improved connector/adaptor framework +* export and migration utilities +* operator dashboards or admin interfaces +* retrieval quality measurement and feedback loops +* stronger permission inheritance and policy synchronization + +--- + +### 9.3 Later Expansion + +Later versions may support: + +* deep integrations with ERP, CRM, ITSM, HR, legal, support, and line-of-business platforms +* rich media and DAM-style operations +* content supply-chain and publishing workflows +* advanced multi-agent workflow execution +* domain-specific packages for legal, support, research, engineering, compliance, or marketing use cases +* enterprise-scale connector marketplaces or partner integrations + +--- + +## 10. Assumptions and Dependencies + +### 10.1 Assumptions + +* Corporate knowledge value depends on more than storage; identity, context, provenance, retrieval, workflow, and governance are core. +* AI systems are important consumers and operators of knowledge, but the engine must also serve human users, applications, and deterministic automation. +* Many useful workflows require heterogeneous formats and sources. +* Governance, traceability, and permissions must be designed into the engine early, not added as optional afterthoughts. +* Customer value is highest when knowledge operations reduce time spent searching, manual document handling, repeated work, review cycles, compliance effort, and AI uncertainty. + +--- + +### 10.2 Dependencies + +The engine may depend on or integrate with: + +* storage backends such as filesystems, databases, object storage, or content repositories +* indexing and retrieval systems such as keyword search, semantic search, vector search, or hybrid retrieval +* extraction tools for document parsing, OCR, layout analysis, and metadata extraction +* AI/model providers for embeddings, summarization, classification, generation, and agent tasks +* identity providers and permission systems for authentication, authorization, and policy enforcement +* workflow, queue, event, and scheduling infrastructure +* source systems such as document repositories, file stores, CMSs, collaboration suites, enterprise applications, and datasets + +Dependencies should be integrated through adapters where possible to avoid unnecessary coupling to one vendor, model, backend, or format. + +--- + +## 11. Constraints + +* The system must remain format-agnostic at the engine level. +* The system must remain headless and API-first; any UI should be a consumer, not the defining product. +* The system must avoid hard-coding one domain, source system, storage backend, search engine, AI provider, or deployment model. +* The system must preserve identity, provenance, and auditability across ingestion, retrieval, transformation, workflow, and agent operations. +* The system must treat permissions and policy constraints as part of core operation, not as optional UI-layer behavior. +* The system must support deterministic operations and AI-assisted operations without allowing AI behavior to reduce traceability or governance. + +--- + +## 12. Risks and Mitigations + +| Risk | Description | Mitigation | +|---|---|---| +| Scope creep into a full ECM/DMS/CMS suite | Mature vendors already dominate full-suite categories. | Keep the core identity as a headless engine and treat applications as consumers. | +| AI-first framing narrows utility | Corporate buyers value governance, workflow, and retrieval even without AI. | Frame the product as AI-ready and agent-operable, but not AI-only. | +| Governance added too late | Retrofitting permissions, audit, retention, and provenance is difficult. | Include identity, provenance, permission checks, and audit logs in P0. | +| Connector explosion | Enterprise source coverage can consume the roadmap. | Define a connector framework first; prioritize source types by target use case. | +| Weak retrieval quality | Poor retrieval undermines AI answers, automation, and user adoption. | Track precision, citation quality, zero-result rate, and retrieval latency from the start. | +| Untraceable transformations | Generated summaries or derived artifacts can become unreliable if detached from sources. | Require transformation provenance for all derived artifacts. | +| Unsafe agent operations | Agents can create governance, privacy, or quality risk if allowed uncontrolled action. | Expose only bounded, permissioned, auditable, optionally review-gated operations. | +| Over-complex architecture | Too many abstractions can prevent usable delivery. | Use P0/P1/P2 phasing and validate against concrete use cases. | +| Vendor lock-in | Hard dependency on one model, search backend, storage backend, or provider limits adoption. | Use adapters and define export/portability requirements. | +| Insufficient operator visibility | Hidden ingestion failures, workflow errors, and permission issues reduce trust. | Add observability and admin inspection as core operational requirements. | + +--- + +## 13. Competitive Differentiation Requirements + +To be meaningfully different from leading alternatives, `kontextual-engine` should emphasize the following differentiators. + +### 13.1 Context-First Asset Identity + +Assets should be identifiable and operable by meaning, source, provenance, relationships, lifecycle state, and operational use — not only by path, folder, URL, or repository identifier. + +Differentiation test: + +> Can the system identify and operate a knowledge asset even when its source path, file name, storage location, or representation changes? + +--- + +### 13.2 Traceable Transformation + +Every summary, extraction, classification, report, generated artifact, and derived representation should remain connected to its source assets and operation history. + +Differentiation test: + +> Can every generated or transformed artifact explain what sources, operations, parameters, actors, and policies produced it? + +--- + +### 13.3 Agent-Safe Knowledge Operation + +AI agents should operate through explicit, bounded, permissioned, auditable, and reviewable interfaces. + +Differentiation test: + +> Can an AI agent inspect, enrich, transform, and route knowledge without bypassing access controls, audit trails, or human review gates? + +--- + +### 13.4 Composable Backend Posture + +The engine should support many knowledge applications without being hard-coded to one domain, UI, source, or product category. + +Differentiation test: + +> Can the same engine support CMS-like, DMS-like, ECM-like, file-service, knowledge-base, research-support, and AI/RAG workflows through reusable capabilities? + +--- + +### 13.5 Governed Retrieval + +Search, API access, AI answers, and agent operations should preserve permissions, policy constraints, source provenance, and lifecycle state. + +Differentiation test: + +> Can retrieval results and generated answers be trusted in a corporate environment where access, sensitivity, source, and auditability matter? + +--- + +## 14. Open Product Questions + +The following decisions should be resolved during architecture and roadmap planning: + +1. What is the canonical internal representation of a knowledge asset? +2. Which source types and formats are included in the first ingestion baseline? +3. How is durable identity assigned and reconciled across re-ingestion, duplicates, moved files, and transformed outputs? +4. What minimum permission model is needed for P0 without overbuilding enterprise IAM from day one? +5. How are relationships represented: graph, typed links, embedded metadata, or another model? +6. Which retrieval modes are required first: keyword, metadata filters, semantic retrieval, graph/context retrieval, or hybrid retrieval? +7. What transformation operations are first-class in the engine rather than external workflow steps? +8. What review gates are mandatory for agent actions and destructive operations? +9. What telemetry is required to measure retrieval quality, transformation quality, workflow reliability, and agent safety? +10. What export format best preserves assets, metadata, relationships, provenance, audit logs, and derived artifacts? + +--- + +## 15. PRD Type + +**Headless Knowledge Operations PRD** + +This PRD defines product-level utility, scope, capabilities, requirements, constraints, and success metrics for a reusable engine. It intentionally leaves implementation architecture flexible while establishing firm boundaries around identity, context, governance, retrieval, transformation, workflow, and agent-safe operation. + +--- + +## 16. Final Product Thesis + +`kontextual-engine` is about: + +> **making knowledge operational** + +In product terms, that means turning heterogeneous information assets into durable, addressable, contextual, retrievable, governable, transformable, and agent-operable knowledge. + +This keeps the repository from becoming an unbounded platform while giving it a strong economic reason to exist: corporations need systems that do more than store content — they need systems that can operate knowledge safely, repeatedly, and intelligently. diff --git a/wiki/kontextual-engine_scope_research_md_bundle/01_market-exploration.md b/wiki/kontextual-engine_scope_research_md_bundle/01_market-exploration.md new file mode 100644 index 0000000..2cc3ecb --- /dev/null +++ b/wiki/kontextual-engine_scope_research_md_bundle/01_market-exploration.md @@ -0,0 +1,302 @@ +# kontextual-engine — Market Exploration + +Research date: 2026-05-05 +Purpose: explore leading alternative systems and market patterns relevant to `kontextual-engine`. + +--- + +## Executive conclusion + +The relevant market is not a single category. `kontextual-engine` overlaps with enterprise content management, document management, content services, file collaboration, enterprise search, AI knowledge assistants, headless CMS, team knowledge bases, and developer-oriented content backends. + +The strongest market signal is that these categories are converging around one problem: + +> Enterprises have large amounts of fragmented, permissioned, weakly contextualized content. They want to use this content for search, automation, AI assistants, agents, workflow, compliance, and reuse without losing governance or control. + +This suggests that `kontextual-engine` should not be scoped as another CMS, DMS, ECM, file server, or vector-search tool. The stronger scope is: + +> A headless knowledge operations engine for turning heterogeneous information assets into persistent, contextual, governed, retrievable, transformable, and agent-operable knowledge. + +That scope places the project between mature ECM/DMS products and newer AI enterprise-search / agentic-work platforms. + +--- + +## Market landscape + +### 1. Enterprise content, document, and records platforms + +Representative systems: + +- Microsoft SharePoint / SharePoint Premium +- OpenText Content Cloud +- Hyland OnBase / Alfresco / Nuxeo +- Box Intelligent Content Management +- Egnyte +- M-Files +- Laserfiche +- DocuWare +- Doxis +- iManage +- NetDocuments + +These systems usually own or govern the repository. Their value is strongest where documents, records, files, policies, contracts, case folders, matter files, invoices, claims, compliance evidence, and operational content are core to the business. + +Common strengths: + +- secure repository and access model +- content lifecycle management +- metadata and classification +- retention, records, audit, legal hold +- document-centric workflow +- integration with Microsoft 365, Google Workspace, SAP, Salesforce, ServiceNow, email, scanners, file shares, and business applications +- increasing use of AI for extraction, summarization, classification, document generation, and assistant-style retrieval + +Implication for `kontextual-engine`: + +- Competing as a full ECM replacement would be expensive and slow. +- The stronger angle is to provide the engine capabilities that make content operational across systems: identity, metadata, relationships, retrieval, provenance, transformations, workflows, and agent-safe APIs. + +--- + +### 2. Enterprise AI search, context, RAG, and agentic platforms + +Representative systems: + +- Glean +- Google Gemini Enterprise / Agentspace lineage +- Sinequa +- Coveo +- Elastic / Elasticsearch +- Dropbox Dash + +These systems often do not own the source repository. They connect to many repositories, synchronize permissions, build indexes or context graphs, and provide search, answers, AI assistants, or agents. + +Common strengths: + +- connectors across workplace applications +- permissions-aware search +- keyword + semantic retrieval +- enterprise graph or context layer +- grounded answers with citations +- AI assistant and agent interfaces +- search analytics and relevance tuning + +Implication for `kontextual-engine`: + +- This is the closest market to the idea of an AI-first knowledge engine. +- A system like `kontextual-engine` must take permission fidelity, grounding, retrieval quality, provenance, and observability seriously from the start. +- Differentiation should come from operating knowledge, not just finding it: structured transformation, traceable derived artifacts, workflows, and lifecycle state. + +--- + +### 3. Headless CMS, composable content, and digital experience platforms + +Representative systems: + +- Contentful +- Contentstack +- Sanity +- Adobe Experience Manager / GenStudio +- Strapi +- Directus + +These systems focus on structured content modeling, API-first delivery, omnichannel publishing, editorial workflows, media assets, localization, personalization, and content supply chains. + +Common strengths: + +- content models and structured schemas +- API-first delivery +- editorial workflows +- release and publishing management +- localization and personalization +- content reuse across channels +- increasing use of AI and agents for content production, governance, and experience assembly + +Implication for `kontextual-engine`: + +- These systems are strong when content is authored for publication and customer-facing experiences. +- `kontextual-engine` should not become a web CMS first. It should support publishing-like utilities where needed, but its durable core should be broader: operating knowledge assets regardless of whether they are published, archived, analyzed, transformed, or used by agents. + +--- + +### 4. Team knowledge and collaboration workspaces + +Representative systems: + +- Atlassian Confluence +- Notion +- Guru +- ServiceNow Knowledge Management + +These systems focus on human-facing knowledge creation, internal documentation, support knowledge, team pages, wiki structures, onboarding, self-service, and knowledge article workflows. + +Common strengths: + +- easy human authoring +- team collaboration +- knowledge base workflows +- comments, review, ownership, verification +- AI summarization and answers +- support or project workflow integration + +Implication for `kontextual-engine`: + +- These systems are strong at end-user experience. +- `kontextual-engine` should not try to win first as a workspace UI. It should provide the operational substrate that a workspace, support portal, dashboard, or agent interface can consume. + +--- + +### 5. Developer-oriented open platforms and build components + +Representative systems: + +- Elastic / Elasticsearch +- Strapi +- Directus +- Alfresco Community / Alfresco platform +- Nuxeo + +These systems matter because a build-first customer may assemble a custom platform using open or extensible components. + +Common strengths: + +- strong APIs +- self-hosting or flexible deployment +- extension mechanisms +- schema/custom data models +- developer tooling +- lower lock-in than monolithic SaaS + +Implication for `kontextual-engine`: + +- `kontextual-engine` can compete as a composable engine only if it is genuinely integration-friendly. +- It needs clear APIs, exportability, schema extensibility, workflow primitives, and implementation transparency. + +--- + +## Vendor archetypes + +| Archetype | What the customer buys | Strong examples | What this means for kontextual-engine | +|---|---|---|---| +| Repository owner | A governed place to store and manage content | SharePoint, OpenText, Box, Hyland, M-Files, Laserfiche, DocuWare, Doxis, iManage, NetDocuments | Hard to displace directly; better to interoperate unless the use case needs a new repository. | +| Search/context overlay | A layer that connects many sources and answers questions | Glean, Sinequa, Gemini Enterprise, Coveo, Dropbox Dash | Directly relevant; `kontextual-engine` needs strong retrieval, permissions, grounding, and context modeling. | +| Digital-experience content platform | Structured content creation and omnichannel publishing | Contentful, Contentstack, Sanity, Adobe AEM | Relevant for content modeling and API delivery, but not the full identity of `kontextual-engine`. | +| Team knowledge workspace | Human authoring, wiki, articles, onboarding, collaboration | Confluence, Notion, Guru, ServiceNow Knowledge | Useful as an interface pattern, but `kontextual-engine` should remain headless. | +| Build component | Search, content API, database API, open platform | Elastic, Directus, Strapi, Alfresco, Nuxeo | Useful benchmarks for extensibility and API-first operation. | + +--- + +## Market convergence patterns + +### 1. AI needs governed content, not just documents + +Vendors increasingly position content management as the foundation for AI. OpenText frames content management as a governed foundation that prepares enterprise content for AI. Microsoft positions AI in SharePoint as a way to manage, organize, and make content Copilot-ready. Box frames its platform as intelligent content management with AI, security, metadata, workflow, and APIs. + +For `kontextual-engine`, this means AI should not be treated as a surface feature. AI should be supported by durable content identity, context, permissions, provenance, and workflow boundaries. + +### 2. Search is becoming an AI substrate + +Glean, Gemini Enterprise, Sinequa, Coveo, Elastic, and Dropbox Dash all emphasize connecting enterprise data, searching across systems, grounding answers, and supporting assistants or agents. The market is moving from “find documents” to “answer and act using governed context.” + +For `kontextual-engine`, retrieval must be designed as an operational capability: search results, source references, permissions, transformations, and generated outputs should remain traceable. + +### 3. Metadata and context are becoming differentiators + +M-Files is especially explicit about metadata-driven, context-first document management. Sanity emphasizes structured content and referential integrity. Contentful emphasizes composable structured content. Guru emphasizes verified governed knowledge. + +For `kontextual-engine`, context cannot be an add-on. Context should include metadata, relationships, provenance, lifecycle state, usage context, and domain references. + +### 4. Agents increase the need for controls + +Glean, Google, Box, Laserfiche, Notion, Contentstack, Sanity, Adobe, and others increasingly discuss agents or agentic workflows. But enterprise buyers will care about permissions, audit trails, reversibility, review, and observability. + +For `kontextual-engine`, “agent-operable” should mean explicit, bounded, auditable operations rather than unconstrained autonomous behavior. + +### 5. Enterprise value concentrates in workflow and risk reduction + +High-value corporate use cases are not only about better search. They include process automation, regulatory compliance, audit readiness, support deflection, legal work, invoice and claims processing, content supply chains, and knowledge reuse. + +For `kontextual-engine`, the engine should support both retrieval and operation: ingestion, classification, enrichment, review, publication, archival, synchronization, transformation, and exception handling. + +--- + +## Competitive conclusions + +### Strong alternatives to buying/building kontextual-engine + +- **Microsoft SharePoint / SharePoint Premium** when the customer is deeply invested in Microsoft 365 and wants integrated content, collaboration, Copilot readiness, governance, and business process support. +- **OpenText / Hyland / Doxis / Laserfiche / DocuWare / M-Files** when the customer needs mature ECM/DMS, records, document workflows, compliance, or process automation. +- **Box / Egnyte / Dropbox Dash** when secure file collaboration, scattered-content discovery, and external/internal sharing are primary. +- **Glean / Gemini Enterprise / Sinequa / Coveo** when enterprise-wide AI search, contextual answers, assistants, and cross-app retrieval are primary. +- **Contentful / Contentstack / Sanity / Adobe AEM** when the problem is structured content for digital experiences, marketing, publishing, or content supply chain. +- **Confluence / Notion / Guru / ServiceNow Knowledge** when the problem is human-facing team knowledge, support knowledge, onboarding, or internal wiki workflows. +- **Elastic / Strapi / Directus / Alfresco / Nuxeo** when the buyer wants composable infrastructure and can invest engineering effort. + +### Where kontextual-engine can be meaningfully different + +1. **Context-first identity across heterogeneous assets** + Assets should be addressable by stable identity, meaning, provenance, relation, lifecycle, and operational role rather than only path, URL, title, or folder. + +2. **Traceable transformations** + Summaries, classifications, extracted records, generated artifacts, reports, and derived knowledge should remain linked to their sources, prompts, workflows, versions, and review state. + +3. **Agent-safe operations** + AI agents should operate through explicit APIs, permissions, review gates, action scopes, audit logs, and reversible workflow steps. + +4. **Composable engine rather than monolithic application** + CMS, DMS, ECM, file-service, knowledge-base, and RAG utilities should be supported as use cases built on the engine, not as separate identities. + +5. **Governed knowledge operations** + The project should focus on operations over knowledge: ingest, contextualize, retrieve, transform, validate, publish, archive, synchronize, and explain. + +--- + +## Implication for INTENT.md + +The `INTENT.md` should define the project as a headless knowledge operations engine, not as part of a larger internal stack and not as a clone of an existing product category. + +Recommended core sentence: + +> `kontextual-engine` exists to turn heterogeneous information assets into persistent, contextual, governed, retrievable, transformable, and agent-operable knowledge. + +Recommended boundary sentence: + +> It may support CMS, DMS, ECM, file-service, knowledge-base, research, and AI-assistant use cases, but it should remain a reusable backend engine rather than a single-purpose end-user application. + +--- + +## Sources consulted + +Primary vendor and market sources consulted while preparing this document: + +- Microsoft SharePoint / SharePoint Premium: , +- OpenText Content Cloud / AI Content Management: , , +- Hyland content services / Alfresco / Nuxeo: , , +- Box Intelligent Content Management / Box AI: , , +- M-Files: , , +- Laserfiche: , , +- DocuWare: , +- Doxis / SER: +- iManage: , , +- NetDocuments: , +- Glean: , , +- Google Gemini Enterprise: , , +- Sinequa: , , +- Coveo: , , +- Elastic: , +- Dropbox Dash: , , +- Contentful: , , +- Contentstack: , , +- Sanity: , , +- Adobe Experience Manager / GenStudio: , , , +- Atlassian Confluence: +- Notion: , , +- Guru: , , +- ServiceNow Knowledge Management: , +- Strapi: , +- Directus: , , +- Forrester content platforms market framing: +- McKinsey generative AI economic potential: +- AIIM Intelligent Information Management 2025: + +Research date: 2026-05-05. diff --git a/wiki/kontextual-engine_scope_research_md_bundle/02_ranked-corporate-usecases.md b/wiki/kontextual-engine_scope_research_md_bundle/02_ranked-corporate-usecases.md new file mode 100644 index 0000000..e7db45a --- /dev/null +++ b/wiki/kontextual-engine_scope_research_md_bundle/02_ranked-corporate-usecases.md @@ -0,0 +1,161 @@ +# kontextual-engine — Corporate Use Cases Ranked by Economic Value + +Research date: 2026-05-05 +Ranking method: directional assessment based on affected employee population, labor intensity, regulatory/risk exposure, revenue impact, integration complexity, and executive budget ownership. + +This is a strategy ranking, not a claim that every company will realize value in the same order. + +--- + +## Ranked use cases + +| Rank | Use case | Economic-value rationale | Best-fit alternative systems | Main value KPIs | +|---:|---|---|---|---| +| 1 | Enterprise AI knowledge access and grounded assistants | Highest horizontal value because the use case spans departments and reduces time spent searching, asking repeated questions, summarizing, and reassembling context. McKinsey’s generative AI analysis supports the broad productivity potential of AI in knowledge-heavy functions. | Glean, Google Gemini Enterprise, Microsoft SharePoint / Copilot ecosystem, Sinequa, Coveo, Elastic, Dropbox Dash | Time saved per employee; answer accuracy; citation precision; active adoption; repeated-question reduction | +| 2 | Document-centric process automation | High direct ROI where documents trigger work: invoices, claims, contracts, HR packets, loan files, case folders, purchase orders, and compliance documents. | OpenText, Hyland, Laserfiche, DocuWare, M-Files, Doxis, Box Automate | Manual-touch reduction; cycle-time reduction; straight-through processing rate; exception rate | +| 3 | Governance, records, compliance, audit readiness | High risk-avoidance value in regulated industries. Reduces cost and exposure around retention, legal hold, audit evidence, privacy requests, and overexposed content. | OpenText, Microsoft Purview / SharePoint, iManage, NetDocuments, Box, Hyland, M-Files, Alfresco | Retention-policy coverage; legal-hold completeness; audit response time; access violations; stale/overexposed content | +| 4 | Secure content collaboration and file-service modernization | High value because shared drives, email attachments, duplicated files, and uncontrolled external sharing remain common enterprise pain points. | Box, Egnyte, Microsoft SharePoint / OneDrive, Google Drive, Dropbox Dash | Secure-sharing adoption; permission hygiene; duplicate-file reduction; external-collaboration cycle time | +| 5 | Legal and professional-services knowledge work | High value because the underlying knowledge is confidential, billable, precedent-heavy, and matter-centric. Small productivity gains can translate into meaningful economic impact. | iManage, NetDocuments, OpenText eDOCS, Microsoft, Glean | Matter-document retrieval time; precedent reuse; confidentiality incidents; legal-review cycle time | +| 6 | Customer service and support knowledge | High value where better knowledge reduces agent effort, improves self-service, and accelerates issue resolution. | ServiceNow Knowledge, Guru, Coveo, Glean, Confluence, Notion | Self-service deflection; first-contact resolution; average handle time; knowledge freshness; article reuse | +| 7 | Digital content supply chain and omnichannel publishing | High for marketing-heavy, commerce, media, retail, and brand organizations where content velocity and reuse affect revenue and campaign throughput. | Adobe AEM / GenStudio, Contentful, Contentstack, Sanity, Strapi | Time to publish; content reuse rate; localization speed; campaign throughput; conversion impact | +| 8 | Enterprise application content services | High where content must be embedded in ERP, CRM, HR, procurement, service, or industry workflows. | OpenText, Hyland, Doxis, M-Files, Laserfiche, Microsoft | Content-in-context coverage; workflow completion time; task-switching reduction; integration count | +| 9 | R&D, engineering, technical, and project knowledge reuse | Medium-high to high depending on industry. Strong value in engineering, pharma, manufacturing, consulting, and research-intensive companies. | Sinequa, Glean, Confluence, Notion, Elastic, SharePoint, Gemini Enterprise | Reuse rate; duplicate-work reduction; expert-finding time; onboarding time; decision traceability | +| 10 | Digital asset management and rich media operations | Medium-high for media, brand, retail, manufacturing, architecture, and creative operations. | Adobe AEM Assets, Nuxeo, Box, Dropbox Dash, DAM-specific platforms | Asset reuse rate; rights-compliance rate; search success for media; time to campaign asset delivery | +| 11 | Corporate intranet, policy, onboarding, and team knowledge base | Medium value but broad applicability. Improves onboarding, policy findability, alignment, and repeated-question reduction. | Confluence, Notion, SharePoint, Guru, ServiceNow Knowledge | Time to onboard; policy findability; stale-page rate; active usage; employee satisfaction | +| 12 | Custom knowledge-backed applications and internal developer platforms | Medium direct value, high strategic leverage. Useful when a company needs domain-specific knowledge apps or wants to avoid a monolithic vendor. | Elastic, Directus, Strapi, Alfresco, Nuxeo, custom RAG stacks | Time to build; API coverage; search relevance; extensibility; operating cost | + +--- + +## Use-case notes + +### 1. Enterprise AI knowledge access and grounded assistants + +This should be treated as the highest-value use case because it cuts across almost every knowledge-worker function. The problem is no longer merely “search documents”; it is “answer questions and perform work using trusted context.” + +For `kontextual-engine`, this means high-quality retrieval, citation, source traceability, permission enforcement, and context modeling are foundational. + +### 2. Document-centric process automation + +This is often the most immediately measurable use case. A corporate customer can compare before/after values for invoice processing, document intake, approval cycles, case routing, claims handling, document review, and exception handling. + +For `kontextual-engine`, this means workflows and transformations are economically important. The engine should not stop at indexing content. + +### 3. Governance, records, and compliance + +This is less glamorous than AI search but often more budget-secure. Compliance buyers care about auditability, records retention, access control, legal hold, data classification, defensible deletion, and privacy response. + +For `kontextual-engine`, governance cannot be bolted on after the fact. Provenance, lifecycle state, permissions, and audit logs should be basic design concepts. + +### 4. Secure collaboration and file-service modernization + +File chaos remains one of the largest real-world problems. Corporate users need to share, find, protect, classify, archive, and reuse files without uncontrolled duplication and permission sprawl. + +For `kontextual-engine`, the opportunity is not to clone a sync-and-share product. The opportunity is to give files durable identity and context so they can participate in workflows, retrieval, and AI operations. + +### 5. Legal and professional-services knowledge work + +Legal platforms show how valuable contextual knowledge can become when organized around matters, clients, precedents, confidentiality, document bundles, and review workflows. + +For `kontextual-engine`, this reinforces the importance of domain context and strict permission boundaries. + +### 6. Customer service and support knowledge + +Support knowledge is economically valuable because it directly affects service costs and customer experience. However, support knowledge must stay current and trusted, or it becomes dangerous. + +For `kontextual-engine`, this suggests built-in review, verification, ownership, freshness tracking, and source-to-answer traceability. + +### 7. Digital content supply chain + +Marketing and digital-experience platforms show the value of reusable structured content, localization, brand governance, channel delivery, and content performance analytics. + +For `kontextual-engine`, this is relevant but should not dominate the scope. Publishing should be a utility built on the engine, not the defining identity of the project. + +### 8. Enterprise application content services + +Many high-value workflows happen inside systems such as ERP, CRM, ITSM, HR, and line-of-business applications. Content becomes valuable when it is available in the context of the task. + +For `kontextual-engine`, this supports an API-first, integration-first architecture. + +### 9. R&D, engineering, and project memory + +Technical knowledge reuse is hard because information is scattered across tickets, design docs, repositories, diagrams, test reports, meeting notes, and domain-specific data. + +For `kontextual-engine`, this favors relationship modeling, provenance, and long-term project memory. + +### 10. Digital asset and rich-media operations + +DAM systems highlight the importance of asset metadata, rights, variants, rendition generation, searchability, and channel activation. + +For `kontextual-engine`, rich media should be handled as knowledge assets, but specialist creative workflows may remain outside the core. + +### 11. Intranet, policies, onboarding, team knowledge + +This is a broad but lower-intensity use case. Many teams need it, but mature end-user tools are strong. + +For `kontextual-engine`, do not start by trying to beat Confluence or Notion as a writing UI. Provide backend utility that can power such interfaces. + +### 12. Custom knowledge-backed applications + +This use case has lower immediate mass-market value but high strategic importance for a reusable engine. It is where `kontextual-engine` can become a developer platform for domain-specific knowledge utilities. + +For `kontextual-engine`, APIs, extensibility, schema design, portability, and observability matter more than polished single-purpose UX. + +--- + +## Implications for project priority + +Recommended first-priority use cases for `kontextual-engine`: + +1. **AI-ready knowledge access with citations and governance** +2. **Document/content ingestion, contextualization, and retrieval** +3. **Traceable transformations and derived artifacts** +4. **Workflow-driven knowledge operations** +5. **Agent-safe APIs and permissioned automation** + +Recommended lower-priority use cases: + +- full intranet authoring +- office-suite replacement +- file sync client +- visual website building +- standalone legal DMS replacement +- specialist DAM replacement +- proprietary enterprise search appliance clone + +--- + +## Sources consulted + +Primary vendor and market sources consulted while preparing this document: + +- Microsoft SharePoint / SharePoint Premium: , +- OpenText Content Cloud / AI Content Management: , , +- Hyland content services / Alfresco / Nuxeo: , , +- Box Intelligent Content Management / Box AI: , , +- M-Files: , , +- Laserfiche: , , +- DocuWare: , +- Doxis / SER: +- iManage: , , +- NetDocuments: , +- Glean: , , +- Google Gemini Enterprise: , , +- Sinequa: , , +- Coveo: , , +- Elastic: , +- Dropbox Dash: , , +- Contentful: , , +- Contentstack: , , +- Sanity: , , +- Adobe Experience Manager / GenStudio: , , , +- Atlassian Confluence: +- Notion: , , +- Guru: , , +- ServiceNow Knowledge Management: , +- Strapi: , +- Directus: , , +- Forrester content platforms market framing: +- McKinsey generative AI economic potential: +- AIIM Intelligent Information Management 2025: + +Research date: 2026-05-05. diff --git a/wiki/kontextual-engine_scope_research_md_bundle/03_stated-usps.md b/wiki/kontextual-engine_scope_research_md_bundle/03_stated-usps.md new file mode 100644 index 0000000..12e764d --- /dev/null +++ b/wiki/kontextual-engine_scope_research_md_bundle/03_stated-usps.md @@ -0,0 +1,134 @@ +# kontextual-engine — Stated Unique Selling Points of Relevant Alternative Systems + +Research date: 2026-05-05 +Purpose: capture vendor-stated positioning and explain why each USP is specific to the respective system. + +--- + +## Vendor USP table + +| System | Category | Stated USP / public positioning | Why this USP is specific | Relevance to kontextual-engine | +|---|---|---|---|---| +| Microsoft SharePoint / SharePoint Premium | Enterprise content + collaboration | AI-powered content management, SharePoint sites/lists/pages, content organization, AI and automation, Copilot readiness. | Specific because SharePoint is deeply embedded in Microsoft 365, Teams, OneDrive, Office, Entra ID, Purview, and Copilot workflows. | The default corporate alternative where Microsoft 365 is the customer’s content substrate. | +| OpenText Content Cloud | Enterprise content management, governance, process integration | Governed foundation for enterprise content, AI-ready content, process integration, capture, IDP, archiving, governance, and industry solutions. | Specific because OpenText has deep ECM, records, governance, and enterprise-app integration heritage. | Strong alternative for regulated, large-enterprise content estates. | +| Hyland | Content services, ECM, process automation | Connect content, data, and processes; content management, process automation, governance, integrations, collaboration, AI-enabled content. | Specific because Hyland has a broad portfolio including OnBase, Alfresco, and Nuxeo, spanning process-heavy ECM and extensible content platforms. | Strong alternative for mature content services and process-heavy deployments. | +| Alfresco | Open content, process, governance | Open-source content, process, and governance services, lifecycle automation, compliance support. | Specific because Alfresco combines open-source heritage with enterprise content and governance services. | Relevant benchmark for open/extensible ECM. | +| Nuxeo | Cloud-native content services / DAM | Highly scalable, cloud-native enterprise content management with rich multimedia support. | Specific because Nuxeo is content-services infrastructure for flexible metadata/content models and rich-media-heavy applications. | Relevant benchmark for scalable content models and rich media. | +| Box Intelligent Content Management | Secure cloud content, collaboration, AI APIs | Secure AI-powered content management, collaboration, content security, AI capabilities, developer APIs, AI-native workflows. | Specific because Box centers on secure enterprise content collaboration and unstructured content in the cloud. | Strong alternative for secure file/content collaboration and AI over stored content. | +| Egnyte | Secure content collaboration + governance | Secure collaboration, content intelligence, governance, mission-critical content protection, industry solutions. | Specific because Egnyte bridges file collaboration, governance, and vertical workflows such as AEC and life sciences. | Strong alternative for file-service modernization and governance. | +| M-Files | Metadata-driven DMS | Context-first, metadata-driven document management; organizes documents by what they are, not where they are stored. | Specific because metadata/context-first identity is the core architectural and marketing differentiator. | Very relevant reference for context-first knowledge identity. | +| Laserfiche | Intelligent content platform | Manage documents, automate work, centralize and secure content, use AI to reduce manual effort and surface insights. | Specific because Laserfiche is strong in document process automation, records, and departmental workflows. | Strong alternative for business-process-centric document management. | +| DocuWare | Cloud DMS + workflow automation | Document management and workflow automation, with intelligent document processing and AI-driven document lifecycle automation. | Specific because DocuWare is often bought for practical, department-level document workflows such as AP, HR, and approvals. | Strong alternative for focused DMS/IDP workflows. | +| Doxis | Intelligent content automation | AI-powered platform to connect and automate enterprise-wide content; document intelligence lifecycle: gather, analyze, manage, automate, act, generate, secure. | Specific because Doxis frames the whole document lifecycle as intelligent content automation across enterprise processes. | Strong alternative for document intelligence and cross-application process integration. | +| iManage | Knowledge work platform | Secure, governed document management, AI-ready context, and flexible connectivity for knowledge workers. | Specific because iManage is optimized for legal and professional-services knowledge work, confidentiality, and matter-centric work. | Strong vertical alternative for high-value professional knowledge work. | +| NetDocuments | Legal DMS + legal AI | Secure, compliant legal document/email management, legal AI assistant, AI app builder, Microsoft integrations. | Specific because NetDocuments is purpose-built for law firms, corporate legal, and public-sector legal workflows. | Strong vertical alternative; good model for domain-specific knowledge operation. | +| Glean | Work AI / enterprise search / agents | Work AI platform connected to enterprise data, unifying search, assistants, agents, connectors, and enterprise context. | Specific because Glean’s main differentiator is cross-application enterprise context rather than repository ownership. | Direct alternative to the AI-context/search layer of `kontextual-engine`. | +| Google Gemini Enterprise | Enterprise AI search, assistant, agent platform | Intranet search, AI assistant, and agentic platform using enterprise data, prebuilt connectors, multimodal search, permissions-aware access, and agent governance. | Specific because Google combines Gemini models, Google-grade search, Workspace/Cloud integration, and agent platform capabilities. | Strong alternative for Google Cloud / Workspace customers. | +| Sinequa | Enterprise AI search / agentic AI | Securely connects, understands, and activates enterprise knowledge for search, assistants, and autonomous agents with document-level security. | Specific because Sinequa focuses on large, complex, heterogeneous enterprise search with many connectors and permission-aware sync. | Strong alternative where cross-repository retrieval is the central problem. | +| Coveo | AI relevance / generative search | Composable AI search and generative-experience platform for commerce, service, workplace, websites, AI agents, recommendations, and personalization. | Specific because Coveo emphasizes AI relevance and personalization across customer and employee journeys. | Strong alternative where relevance directly affects CX, support, or commerce outcomes. | +| Elastic / Elasticsearch | Search and AI-app infrastructure | High-performance search, vector search, structured/unstructured/vector data, context engineering, and AI app infrastructure. | Specific because Elastic is developer/infrastructure-first, not a turnkey knowledge app. | Strong component alternative for search/RAG infrastructure. | +| Dropbox Dash | AI universal search + content control | AI universal search and organization with universal content access control across apps, files, media, and messages. | Specific because Dropbox extends from file sync/storage into cross-app discovery and content organization. | Alternative for scattered-content discovery, less for deep governance/ECM. | +| Contentful | Composable content platform | Structured composable content for scalable digital experiences, content reuse, channels, brands, regions, and AI-supported content operations. | Specific because Contentful is built around structured content models and API-first delivery for digital experiences. | Relevant if `kontextual-engine` supports CMS-like publishing utilities. | +| Contentstack | Headless CMS / Agentic Experience Platform | Enterprise headless CMS and agentic experience platform combining CMS, data cloud, personalization, analytics, and agents. | Specific because Contentstack targets digital-experience operations and agentic personalization at scale. | Relevant for experience/content supply chain use cases. | +| Sanity | Content Operating System / Content Lake | Backend for AI content operations; structured JSON content, query precision, referential integrity, real-time content workflows, agentic applications. | Specific because Sanity treats content as structured data in a content lake with developer-friendly modeling/querying. | Strong reference for structured content, referential integrity, and API-first content operations. | +| Adobe Experience Manager / GenStudio | Enterprise CMS, DAM, content supply chain | Agentic CMS, AI-powered DAM, content supply chain modernization, brand governance, asset activation, and marketing workflows. | Specific because Adobe combines CMS, DAM, creative tooling, analytics, brand workflows, and marketing activation. | Strong alternative for marketing and rich digital-content operations. | +| Atlassian Confluence | Team workspace / knowledge base | Team workspace for creating and sharing knowledge, with AI drafting, summarization, and answers. | Specific because Confluence sits inside the Atlassian system of work with Jira and project/developer workflows. | Strong alternative for team/project knowledge, not full ECM. | +| Notion | AI workspace | AI workspace with docs, wiki, projects, enterprise search, custom agents, permissions inheritance, logged/reversible agent runs. | Specific because Notion blends documents, databases, projects, wiki, AI, and lightweight apps in one end-user workspace. | Strong alternative for lightweight internal knowledge and team operations. | +| Guru | Governed knowledge layer | Structures, governs, verifies, and continuously improves knowledge so people and AI tools get trusted answers. | Specific because Guru emphasizes verification and trust workflows around knowledge, not broad document storage. | Strong reference for verified knowledge and freshness workflows. | +| ServiceNow Knowledge Management | Service/support knowledge | Contextual knowledge base to increase customer/employee self-service and boost agent productivity. | Specific because ServiceNow knowledge lives inside ITSM/CSM/HR service workflows and case resolution. | Strong alternative for support and service knowledge. | +| Strapi | Open-source headless CMS | Leading open-source headless CMS; developer freedom; editors manage content and distribute it anywhere. | Specific because Strapi is JavaScript/TypeScript, open-source, customizable, and content-API oriented. | Build-component reference for open headless CMS primitives. | +| Directus | Database-first backend workspace | Turns SQL databases into shared platforms and APIs where developers, content teams, and AI work on live data. | Specific because Directus works on top of existing SQL databases without forcing migration into a proprietary content model. | Strong reference for database-first extensibility and API generation. | + +--- + +## USP patterns that matter for kontextual-engine + +### Pattern 1: “AI-ready content” + +Microsoft, OpenText, Box, Hyland, Laserfiche, Doxis, Sanity, Contentstack, Adobe, and others all increasingly present content management as a prerequisite for useful AI. + +Scope implication: + +- `kontextual-engine` should make content ready for AI by design: identity, structure, metadata, permissions, provenance, retrieval, and review. + +### Pattern 2: “Context-first” or “structured content” + +M-Files, Sanity, Contentful, Guru, and Glean use different language but converge around a similar idea: content becomes more valuable when its business context is explicit. + +Scope implication: + +- Context should be a first-class layer, not merely tags or search facets. + +### Pattern 3: “Permission-aware retrieval” + +Glean, Gemini Enterprise, Sinequa, Dropbox Dash, Box, and others emphasize secure access to enterprise content. + +Scope implication: + +- Retrieval and AI answers are only enterprise-ready if they preserve source-system permissions and generate auditable evidence. + +### Pattern 4: “Workflow and automation” + +OpenText, Hyland, Box, Laserfiche, DocuWare, Doxis, Contentstack, Notion, and others increasingly move from storing content to automating work around content. + +Scope implication: + +- `kontextual-engine` should be able to execute knowledge workflows, not only index documents. + +### Pattern 5: “Agentic operation” + +Glean, Gemini Enterprise, Sinequa, Laserfiche, Notion, Contentstack, Sanity, Adobe, and Box show that agents are becoming part of the category narrative. + +Scope implication: + +- The project should define agent-safe operation clearly: explicit actions, permission checks, scoped tools, review gates, logs, reversibility, and provenance. + +--- + +## Most strategically important competitor lessons + +1. **From M-Files:** context-first identity is a powerful differentiator. +2. **From Glean/Sinequa/Gemini Enterprise:** enterprise AI depends on connectors, permissions, retrieval quality, and context. +3. **From OpenText/Hyland/Doxis/Laserfiche:** corporate value often comes from workflow, governance, and document lifecycle automation. +4. **From Box/Egnyte/Dropbox Dash:** file chaos is a real and persistent enterprise problem, but file storage alone is not enough. +5. **From Contentful/Sanity/Contentstack/Adobe:** structured content enables reuse, omnichannel delivery, automation, and AI readiness. +6. **From Guru/ServiceNow:** trusted answers require ownership, verification, freshness, and workflow integration. +7. **From Elastic/Directus/Strapi:** developer adoption requires APIs, extensibility, transparency, and portability. + +--- + +## Sources consulted + +Primary vendor and market sources consulted while preparing this document: + +- Microsoft SharePoint / SharePoint Premium: , +- OpenText Content Cloud / AI Content Management: , , +- Hyland content services / Alfresco / Nuxeo: , , +- Box Intelligent Content Management / Box AI: , , +- M-Files: , , +- Laserfiche: , , +- DocuWare: , +- Doxis / SER: +- iManage: , , +- NetDocuments: , +- Glean: , , +- Google Gemini Enterprise: , , +- Sinequa: , , +- Coveo: , , +- Elastic: , +- Dropbox Dash: , , +- Contentful: , , +- Contentstack: , , +- Sanity: , , +- Adobe Experience Manager / GenStudio: , , , +- Atlassian Confluence: +- Notion: , , +- Guru: , , +- ServiceNow Knowledge Management: , +- Strapi: , +- Directus: , , +- Forrester content platforms market framing: +- McKinsey generative AI economic potential: +- AIIM Intelligent Information Management 2025: + +Research date: 2026-05-05. diff --git a/wiki/kontextual-engine_scope_research_md_bundle/04_core-capabilities-kpis.md b/wiki/kontextual-engine_scope_research_md_bundle/04_core-capabilities-kpis.md new file mode 100644 index 0000000..eb01af0 --- /dev/null +++ b/wiki/kontextual-engine_scope_research_md_bundle/04_core-capabilities-kpis.md @@ -0,0 +1,173 @@ +# kontextual-engine — Core Capabilities and KPIs + +Research date: 2026-05-05 +Purpose: define the capabilities that all relevant contenders need to provide, with KPIs that can rank implementation quality. + +--- + +## Capability matrix + +| # | Capability | Definition | Main KPIs for implementation quality | +|---:|---|---|---| +| 1 | Multi-source ingestion | Bring in files, documents, markdown, PDFs, office docs, datasets, messages, records, and application content. | Connector coverage; ingestion success rate; source-update-to-index latency | +| 2 | Format normalization and extraction | Extract text, structure, metadata, tables, images, layout, entities, and references from heterogeneous formats. | Extraction F1 / accuracy; unsupported-format rate; processing cost per document | +| 3 | Persistent asset identity | Assign stable identity to each asset independent of path, filename, URL, or storage backend. | Duplicate-detection rate; identity collision rate; percentage of assets with stable IDs | +| 4 | Metadata and classification | Capture explicit and inferred metadata such as type, owner, domain, sensitivity, lifecycle, topic, and status. | Metadata completeness; classification accuracy; manual correction rate | +| 5 | Context modeling and relationships | Connect assets to people, projects, customers, matters, cases, products, decisions, processes, and other assets. | Relationship coverage; graph/query completeness; average context depth per asset | +| 6 | Search and retrieval | Provide keyword, semantic, filtered, faceted, graph-aware, API-accessible, and permission-aware retrieval. | Precision@k / NDCG; p95 query latency; zero-result rate | +| 7 | Grounded AI answers / RAG | Generate answers, summaries, and analyses grounded in governed enterprise content. | Grounded-answer accuracy; citation precision; unsupported-claim rate | +| 8 | Permissions and access control | Enforce roles, groups, policies, source permissions, sharing controls, and sensitive-data restrictions. | Permission fidelity vs source systems; access violation rate; policy propagation latency | +| 9 | Governance and lifecycle management | Manage retention, legal hold, audit, archival, review, disposition, privacy response, and compliance evidence. | Retention-policy coverage; audit-log completeness; eDiscovery / DSAR response time | +| 10 | Versioning and provenance | Track where content came from, how it changed, who or what changed it, and what depends on it. | Version recovery success; provenance completeness; change traceability coverage | +| 11 | Workflow orchestration | Automate ingestion, classification, enrichment, validation, review, approval, publication, archival, and synchronization. | Workflow completion rate; manual-touch reduction; exception backlog | +| 12 | Intelligent document processing | Classify documents, extract fields, validate data, and route work based on document content and context. | Field extraction F1; straight-through processing rate; human validation time | +| 13 | API-first access | Expose assets, metadata, search, relationships, workflows, and AI operations through stable APIs. | API uptime; p95 API latency; developer time to first integration | +| 14 | Extensibility and integration | Support connectors, plugins, webhooks, SDKs, custom schemas, event streams, and external storage/indexing systems. | Supported integration patterns; extension deployment time; breaking-change frequency | +| 15 | Collaboration and review | Let humans inspect, annotate, approve, correct, verify, and curate knowledge assets and derived outputs. | Review turnaround time; active contributor rate; approval/rejection accuracy | +| 16 | Agent-safe operation | Let AI agents inspect, transform, enrich, and operate knowledge through permissioned, auditable, explicit interfaces. | Agent task success rate; human-approval intervention rate; policy-violation rate | +| 17 | Observability and admin control | Provide visibility into ingestion, search, workflows, permissions, AI usage, failures, costs, and system health. | Mean time to detect/resolve failures; job failure rate; cost per indexed/answered item | +| 18 | Scalability and performance | Handle growing volumes of content, users, queries, transformations, and AI workloads. | Indexing throughput; p95/p99 latency under load; maximum tested corpus size | +| 19 | Data portability and lock-in control | Export assets, metadata, relationships, versions, audit trails, and derived artifacts in usable formats. | Export completeness; migration success rate; proprietary-dependency count | +| 20 | User and developer experience | Make the system usable by operators, developers, applications, humans, and agents. | Time to complete common task; adoption rate; developer satisfaction / NPS | + +--- + +## Capability maturity scale + +Use this simple quality scale to rank `kontextual-engine` or any contender. + +| Maturity level | Description | +|---|---| +| 0 — Missing | Capability is absent or only possible through ad hoc scripts. | +| 1 — Prototype | Capability exists but is unreliable, narrow, undocumented, or manually operated. | +| 2 — Usable baseline | Capability works for normal use, has clear interfaces, and supports repeatable operation. | +| 3 — Enterprise-ready | Capability supports permissions, audit, observability, scale, configuration, and operational controls. | +| 4 — Differentiating | Capability creates strategic advantage through context, automation, quality, usability, extensibility, or cost profile. | + +--- + +## Capability priorities for kontextual-engine + +Not all capabilities have equal strategic value. For `kontextual-engine`, the highest-priority capabilities are: + +1. **Persistent asset identity** + Without stable identity, the system cannot reliably manage versions, relationships, provenance, permissions, or transformations. + +2. **Context modeling and relationships** + This is the key differentiator from generic file storage, vector search, or document repositories. + +3. **Search and retrieval** + Retrieval is the operational access layer for humans, APIs, applications, and agents. + +4. **Grounded AI answers / RAG** + AI utility depends on reliable retrieval, citation, permission enforcement, and provenance. + +5. **Versioning and provenance** + Traceability is essential for trusted summaries, transformations, compliance, and derived artifacts. + +6. **Workflow orchestration** + Economic value comes from operating knowledge, not merely storing or finding it. + +7. **Agent-safe operation** + AI agents need bounded, explicit, reversible, auditable action surfaces. + +8. **Governance and lifecycle management** + Corporate customers require retention, access control, auditability, and policy enforcement. + +--- + +## Suggested KPI definitions + +### Retrieval KPIs + +- **Precision@k:** percentage of top-k results that are relevant. +- **NDCG:** ranking quality metric that rewards relevant results appearing near the top. +- **Zero-result rate:** percentage of searches that return no useful result. +- **Permission-filter latency:** additional latency introduced by permission enforcement. + +### AI-answer KPIs + +- **Grounded-answer accuracy:** percentage of answers judged correct and supported by available sources. +- **Citation precision:** percentage of cited sources that actually support the answer claim. +- **Unsupported-claim rate:** percentage of generated claims not supported by retrieved evidence. +- **Escalation rate:** percentage of AI tasks requiring human clarification or review. + +### Governance KPIs + +- **Retention-policy coverage:** percentage of eligible assets governed by an explicit retention policy. +- **Audit-log completeness:** percentage of relevant actions captured with actor, time, asset, operation, and outcome. +- **Legal-hold completeness:** percentage of in-scope assets preserved under legal hold. +- **DSAR/eDiscovery response time:** time needed to identify and package in-scope information. + +### Workflow KPIs + +- **Manual-touch reduction:** percentage reduction in human interventions compared with baseline process. +- **Straight-through processing rate:** percentage of items completed without manual exception handling. +- **Exception backlog:** number or age of workflow items waiting for human resolution. +- **Review turnaround time:** time from review request to approval/rejection. + +### Ingestion KPIs + +- **Ingestion success rate:** percentage of assets successfully imported and represented. +- **Source-update-to-index latency:** time between source change and availability in retrieval. +- **Extraction completeness:** percentage of expected text, structure, fields, and metadata extracted. +- **Reprocessing success:** ability to re-run ingestion without corrupting identity, versions, or provenance. + +--- + +## Minimal viable capability set + +For a credible first version, `kontextual-engine` should aim for: + +1. Asset registry with stable IDs +2. Multi-format ingestion for a small set of common formats +3. Metadata and source provenance +4. Basic versioning +5. Search and filtered retrieval +6. Relationship/context model +7. API access +8. Transformations that create traceable derived artifacts +9. Permission model, even if initially simple +10. Basic workflow/job orchestration +11. Audit log for all material operations +12. Agent-safe operation through explicit API endpoints + +This minimal set is enough to support the project’s intended identity without prematurely becoming a full ECM, DMS, CMS, or enterprise search suite. + +--- + +## Sources consulted + +Primary vendor and market sources consulted while preparing this document: + +- Microsoft SharePoint / SharePoint Premium: , +- OpenText Content Cloud / AI Content Management: , , +- Hyland content services / Alfresco / Nuxeo: , , +- Box Intelligent Content Management / Box AI: , , +- M-Files: , , +- Laserfiche: , , +- DocuWare: , +- Doxis / SER: +- iManage: , , +- NetDocuments: , +- Glean: , , +- Google Gemini Enterprise: , , +- Sinequa: , , +- Coveo: , , +- Elastic: , +- Dropbox Dash: , , +- Contentful: , , +- Contentstack: , , +- Sanity: , , +- Adobe Experience Manager / GenStudio: , , , +- Atlassian Confluence: +- Notion: , , +- Guru: , , +- ServiceNow Knowledge Management: , +- Strapi: , +- Directus: , , +- Forrester content platforms market framing: +- McKinsey generative AI economic potential: +- AIIM Intelligent Information Management 2025: + +Research date: 2026-05-05. diff --git a/wiki/kontextual-engine_scope_research_md_bundle/05_project-scope-suggestions.md b/wiki/kontextual-engine_scope_research_md_bundle/05_project-scope-suggestions.md new file mode 100644 index 0000000..3b686ed --- /dev/null +++ b/wiki/kontextual-engine_scope_research_md_bundle/05_project-scope-suggestions.md @@ -0,0 +1,355 @@ +# kontextual-engine — Project Scope Suggestions + +Research date: 2026-05-05 +Purpose: convert market exploration into concrete scope guidance for the project and its `INTENT.md`. + +--- + +## Recommended project definition + +`kontextual-engine` should be defined as: + +> A headless knowledge operations engine for turning heterogeneous information assets into persistent, contextual, governed, retrievable, transformable, and agent-operable knowledge. + +This definition is broad enough to support CMS, DMS, ECM, file-service, knowledge-base, research, and AI-assistant use cases, but narrow enough to avoid becoming an unfocused clone of mature enterprise suites. + +--- + +## Recommended utility-demand framing + +The project should start from the customer problem: + +> Corporate customers accumulate valuable information across files, folders, documents, records, datasets, applications, generated AI outputs, and knowledge bases. This information is economically underused because it is fragmented, inconsistently structured, weakly contextualized, hard to govern, difficult to retrieve, and unsafe to automate without explicit controls. + +`kontextual-engine` addresses this demand by giving knowledge assets: + +- stable identity +- metadata and context +- relationships +- provenance +- lifecycle state +- permissions and governance +- search and retrieval +- transformation workflows +- API access +- agent-safe operation + +--- + +## Strategic scope + +### In scope + +`kontextual-engine` should provide reusable backend capabilities for: + +- ingesting heterogeneous information assets +- representing assets as persistent entities +- normalizing and extracting useful structure +- assigning metadata, relationships, provenance, and lifecycle state +- retrieving assets through keyword, filtered, semantic, and contextual search +- transforming content into summaries, extracts, structured views, reports, and generated artifacts +- orchestrating recurring knowledge workflows +- exposing APIs for applications, automation systems, and AI agents +- enforcing permissions, traceability, review, and governance controls + +### Out of scope as core identity + +`kontextual-engine` should not define itself as: + +- a finished end-user CMS +- a website builder +- a generic office suite +- a sync-and-share client +- a simple file browser +- a markdown-only tool +- a pure vector database +- a generic chatbot over documents +- a single-domain knowledge base +- a one-off automation script collection +- a full replacement for mature ECM/DMS/records systems in its first maturity phases + +These capabilities can be supported at the edges, but they should not define the engine. + +--- + +## Recommended differentiation + +### 1. Context-first knowledge identity + +Competitors often anchor identity in repositories, paths, records, pages, documents, or content models. `kontextual-engine` can differentiate by making identity more semantic and operational. + +Recommended design focus: + +- stable asset IDs +- source IDs and source aliases +- semantic type +- business context +- relationship graph +- provenance chain +- lifecycle state +- derived artifact lineage + +### 2. Traceable transformations + +Many systems generate summaries or extract fields, but the strategic value lies in knowing where derived knowledge came from and how it was produced. + +Recommended design focus: + +- transformations as first-class operations +- explicit input/output asset links +- versioned prompts/configuration where applicable +- transformation metadata +- review status +- reproducibility hooks +- rollback or supersession semantics + +### 3. Agent-safe operation + +Agents should not be treated merely as chat UIs. Agents need permissioned, explicit, auditable operations. + +Recommended design focus: + +- scoped tool/API permissions +- actor identity for human, service, and agent actors +- precondition checks +- dry-run support +- review gates for risky actions +- audit logs +- reversible changes where possible +- policy violation detection + +### 4. Composable utility layer + +CMS, DMS, ECM, file-service, knowledge-base, research, and AI-assistant capabilities should be framed as utilities built on the engine. + +Recommended design focus: + +- APIs before UI +- workflows before monolithic apps +- exportability +- integration adapters +- schema extensibility +- domain-specific extensions + +### 5. Governance without becoming bureaucratic + +Governance should be a capability, not a drag on utility. + +Recommended design focus: + +- lightweight but explicit permissions +- lifecycle state +- review state +- retention and archival hooks +- audit log by default +- policy-aware retrieval and transformation + +--- + +## Suggested architecture-level scope boundaries + +| Layer | Should kontextual-engine own it? | Notes | +|---|---:|---| +| Asset registry | Yes | Stable identity and core metadata should be central. | +| Source connectors | Yes, selectively | Build common connectors and allow extension. Do not try to support every enterprise app initially. | +| Storage abstraction | Yes | Assets may live in external systems, but the engine needs a durable representation. | +| Extraction / normalization | Yes | Required for search, metadata, AI, and transformations. | +| Search index | Yes or integrated | The engine must provide retrieval; it may use external search/vector systems internally. | +| Relationship graph | Yes | Core differentiator. | +| Workflow engine | Yes, initially simple | Needed for recurring knowledge operations and traceable transformations. | +| Permissions model | Yes | Must exist from the beginning even if initially simple. | +| Audit/provenance | Yes | Core trust capability. | +| End-user workspace UI | No, optional consumer | Useful later, but not the engine’s identity. | +| Visual website CMS | No, optional extension | Publishing can be supported through APIs. | +| File sync client | No | Avoid competing directly with Box, Dropbox, OneDrive, Egnyte. | +| Full records-management suite | Not initially | Support hooks and lifecycle state; specialized compliance can mature later. | +| General vector database | No | Use or integrate with search/vector systems; do not define the project as one. | + +--- + +## Recommended first implementation wedge + +The first strong wedge should be: + +> Ingest a heterogeneous project or organizational knowledge corpus, assign stable asset identities, extract metadata and structure, build contextual relationships, support governed retrieval, and produce traceable derived artifacts through API-accessible workflows. + +This wedge demonstrates the project’s essence without requiring a full enterprise suite. + +### MVP capability package + +1. Asset registry +2. Source ingestion for local files, markdown, PDFs, and office-like documents +3. Metadata extraction and manual metadata override +4. Stable source/provenance tracking +5. Search and filtered retrieval +6. Relationship model +7. Traceable transformation jobs +8. API access +9. Basic permission model +10. Audit log +11. Agent-safe operation endpoints + +### MVP demonstration scenarios + +- “Turn a project folder into a contextual knowledge space.” +- “Find and cite relevant knowledge assets across mixed formats.” +- “Generate a traceable summary or report from selected sources.” +- “Classify and enrich assets through a reviewable workflow.” +- “Expose project knowledge to an agent through controlled APIs.” + +--- + +## Recommended language for INTENT.md + +Use language like: + +- “headless knowledge operations engine” +- “heterogeneous information assets” +- “persistent identity” +- “contextual structure” +- “governed access” +- “retrievable meaning” +- “traceable transformation” +- “workflow-ready and agent-operable interfaces” + +Avoid language like: + +- “runtime substrate” unless clarified for external readers +- “system layer” without a self-contained explanation +- references to other internal projects +- “not the tooling layer” unless the tooling is explained generically +- “AI-first” without grounding it in concrete utility + +--- + +## Recommended final positioning statement + +> `kontextual-engine` exists to operate knowledge assets across heterogeneous sources by giving them durable identity, contextual structure, governed access, retrievable meaning, traceable transformation, and automation-ready interfaces. + +Expanded version: + +> It supports the utility demand behind CMS, DMS, ECM, file-service, knowledge-base, research, and AI-assistant systems without becoming any one of those products. Its core role is to provide reusable backend capabilities for making fragmented information operational. + +--- + +## Risks to avoid + +### Risk 1: Becoming too broad + +Trying to be a CMS, DMS, ECM, file server, RAG system, intranet, and workflow suite at the same time will dilute implementation quality. + +Mitigation: + +- Frame these as utility domains supported by a shared engine. +- Prioritize identity, context, retrieval, transformations, workflows, and governance. + +### Risk 2: Becoming “chat over files” + +Many AI knowledge products reduce to a chatbot over indexed documents. + +Mitigation: + +- Make traceability, lifecycle state, transformations, review, and workflows core. + +### Risk 3: Ignoring permissions until later + +Permission retrofits are difficult and dangerous. + +Mitigation: + +- Model actors, roles, permissions, and audit from the beginning. + +### Risk 4: Overfitting to one content format + +The project should handle markdown well if useful, but the market demand is heterogeneous. + +Mitigation: + +- Treat markdown, PDFs, documents, datasets, and records as asset types, not the system identity. + +### Risk 5: No clear buyer/use-case anchor + +A general knowledge engine can sound abstract. + +Mitigation: + +- Anchor early demos in concrete use cases: AI-ready project corpus, document workflow automation, governed retrieval, traceable report generation, contextual knowledge base. + +--- + +## Recommended roadmap priorities + +### Phase 1 — Engine credibility + +- asset registry +- ingestion +- metadata +- provenance +- search +- API +- audit log + +### Phase 2 — Knowledge operation + +- relationships +- transformations +- workflow jobs +- review state +- permissions +- derived artifacts + +### Phase 3 — AI and agent operation + +- grounded answers +- citations +- agent-safe APIs +- dry-run and review gates +- evaluation metrics +- prompt/config provenance + +### Phase 4 — Enterprise hardening + +- advanced governance +- retention and legal hold hooks +- scaling and performance +- observability +- connector ecosystem +- export and migration tooling + +--- + +## Sources consulted + +Primary vendor and market sources consulted while preparing this document: + +- Microsoft SharePoint / SharePoint Premium: , +- OpenText Content Cloud / AI Content Management: , , +- Hyland content services / Alfresco / Nuxeo: , , +- Box Intelligent Content Management / Box AI: , , +- M-Files: , , +- Laserfiche: , , +- DocuWare: , +- Doxis / SER: +- iManage: , , +- NetDocuments: , +- Glean: , , +- Google Gemini Enterprise: , , +- Sinequa: , , +- Coveo: , , +- Elastic: , +- Dropbox Dash: , , +- Contentful: , , +- Contentstack: , , +- Sanity: , , +- Adobe Experience Manager / GenStudio: , , , +- Atlassian Confluence: +- Notion: , , +- Guru: , , +- ServiceNow Knowledge Management: , +- Strapi: , +- Directus: , , +- Forrester content platforms market framing: +- McKinsey generative AI economic potential: +- AIIM Intelligent Information Management 2025: + +Research date: 2026-05-05. diff --git a/wiki/kontextual-engine_scope_research_md_bundle/06_INTENT.refined.md b/wiki/kontextual-engine_scope_research_md_bundle/06_INTENT.refined.md new file mode 100644 index 0000000..8a7ff88 --- /dev/null +++ b/wiki/kontextual-engine_scope_research_md_bundle/06_INTENT.refined.md @@ -0,0 +1,238 @@ +# INTENT + +## Purpose + +`kontextual-engine` exists to provide a **headless knowledge operations engine** for turning heterogeneous information assets into persistent, contextual, governed, retrievable, transformable, and agent-operable knowledge. + +The project addresses the utility demand behind systems such as content management, document management, enterprise content management, file services, knowledge bases, research repositories, and AI-assisted knowledge workflows. It is not limited to any one of those categories. Its role is to provide reusable backend capabilities for making fragmented information operational. + +`kontextual-engine` should help people, teams, applications, automation systems, and AI agents work with knowledge assets across different sources, formats, domains, and lifecycle states. + +--- + +## Utility Demand + +Organizations and individuals accumulate valuable information in fragmented forms: + +* files and folders +* markdown and text repositories +* office documents +* PDFs +* datasets +* notes +* records +* policies +* project documentation +* knowledge-base articles +* generated AI outputs +* operational documents +* content archives +* application-linked documents and records + +These assets often remain economically underused because they are disconnected, inconsistently structured, weakly contextualized, difficult to govern, hard to retrieve, and unsafe to automate without explicit controls. + +`kontextual-engine` exists to solve this problem by giving knowledge assets durable identity, contextual structure, governed access, retrievable meaning, traceable transformation, and automation-ready interfaces. + +It is not merely a storage layer. It is an engine for making knowledge operational. + +--- + +## Primary Utility + +The repository provides a **runtime and service layer for knowledge operations**. + +It is intended to support: + +* ingestion of knowledge assets from multiple sources and formats +* persistent representation of assets with stable identity +* extraction and normalization of useful structure, metadata, and content +* contextualization through metadata, relationships, provenance, classification, and lifecycle state +* retrieval through search, filtering, querying, browsing, APIs, and agent-compatible access patterns +* transformation of content into summaries, extracts, structured representations, generated artifacts, reports, views, or downstream formats +* workflow orchestration for recurring knowledge operations such as ingestion, enrichment, validation, review, publication, archival, and synchronization +* governed access through permissions, auditability, traceability, review state, and operational controls +* AI-assisted and agent-safe operation through explicit, permissioned, and auditable interfaces + +The core value of `kontextual-engine` is to make knowledge **durable, addressable, contextual, searchable, transformable, governable, and operationally useful**. + +--- + +## Intended Users + +`kontextual-engine` is intended for: + +* developers building knowledge-driven applications and services +* teams that need structured access to documents, content, files, records, and datasets +* operators managing durable knowledge services +* product builders creating CMS, DMS, ECM, knowledge-base, research-support, file-service-like, or AI-assistant-backed systems +* automation systems that need reliable access to contextual information +* AI agents that need to inspect, retrieve, transform, enrich, and maintain knowledge assets +* researchers, analysts, and knowledge workers managing evolving collections of information + +The system should be usable by humans through applications and by machines through APIs, workflows, and controlled agent interfaces. + +--- + +## Strategic Role + +`kontextual-engine` serves as a **knowledge operations engine**. + +Its role is to provide reusable backend capabilities for managing knowledge as an active operational resource rather than as passive content. + +This includes: + +* asset identity +* persistence +* ingestion +* normalization +* metadata +* contextual relationships +* indexing and retrieval +* transformation +* workflow execution +* permissions and access control +* provenance and traceability +* governance hooks +* integration interfaces +* agent-oriented operation + +The project should remain focused on the engine layer: the durable runtime capabilities needed to operate knowledge systems across many domains, applications, and deployment models. + +It should not be constrained to a single content format, user interface, application domain, storage backend, AI model, or deployment scenario. + +--- + +## Core Capabilities + +A mature `kontextual-engine` should provide capabilities in the following areas. + +### Knowledge Asset Management + +The system should manage knowledge assets as persistent entities with stable identity, metadata, relationships, provenance, versions, permissions, and lifecycle state. + +### Multi-Format Ingestion + +The system should ingest and normalize information from heterogeneous sources and formats, including text files, markdown, office documents, PDFs, datasets, structured records, generated outputs, and other content sources. + +### Contextualization + +The system should enrich knowledge assets with context such as tags, classifications, links, references, provenance, ownership, source information, temporal information, semantic annotations, review state, and derived relationships. + +### Retrieval and Access + +The system should expose knowledge through search, filtering, querying, browsing, APIs, and agent-compatible access patterns while respecting permissions and operational constraints. + +### Transformation + +The system should support controlled transformation of knowledge assets into summaries, extracts, structured representations, generated artifacts, reports, views, and downstream formats. + +Transformations should be traceable to their inputs, configuration, actor, workflow, and output artifacts. + +### Workflow Operation + +The system should support repeatable knowledge workflows such as ingestion, classification, validation, enrichment, review, approval, publication, archival, synchronization, and exception handling. + +### Governance and Traceability + +The system should preserve enough operational history to understand where knowledge came from, how it changed, who or what acted on it, which permissions applied, and what downstream artifacts depend on it. + +### AI-Assisted and Agent-Safe Operation + +The system should be designed so that AI agents can safely inspect, retrieve, transform, classify, enrich, and maintain knowledge assets through explicit interfaces and controlled workflows. + +Agent operation should be permissioned, auditable, reviewable, and reversible where practical. + +--- + +## Strategic Boundaries + +This repository is **not** intended to be: + +* a single-purpose document editor +* a simple file browser +* a format-specific markdown tool +* a pure vector database +* a generic chatbot over documents +* a finished end-user CMS by itself +* a visual website builder +* a file-sync client +* a domain-specific knowledge base +* a one-off automation script collection +* a full replacement for specialized authoring, publishing, legal, records-management, or analytical tools + +Instead, it should provide reusable backend capabilities that such systems may depend on. + +It may support user interfaces, command-line tools, importers, exporters, connectors, dashboards, and domain-specific applications, but those should remain consumers or extensions of the engine rather than the core identity of the project. + +--- + +## Design Principles + +### Utility before presentation + +The engine should focus first on making knowledge operationally useful. User interfaces and presentation layers may be built on top, but they should not define the core architecture. + +### Format agnosticism + +The system should support many content types and should not be constrained by one preferred authoring format. + +### Persistent knowledge state + +Knowledge assets should have durable identity, lifecycle state, metadata, relationships, provenance, permissions, and operational history. + +### Context as a first-class concern + +The system should treat relationships, provenance, classification, lifecycle state, and usage context as core information, not as secondary decoration. + +### Traceable transformation + +Generated summaries, derived artifacts, classifications, extractions, and other transformations should remain linked to their source assets and workflow context. + +### API-first and automation-ready + +The system should expose stable interfaces suitable for applications, services, scripts, workflows, and AI agents. + +### Agent-safe operation + +AI agents should operate through explicit, permissioned, auditable, and bounded interfaces. Risky operations should support review gates, dry runs, or reversible workflows where appropriate. + +### Composable operation + +Knowledge operations should be built from reusable capabilities that can be combined into workflows. + +### Human and agent collaboration + +The system should support both human-directed and AI-assisted knowledge work, with clear ownership, permissions, review mechanisms, and traceability. + +### Separation of engine and application + +The repository should provide reusable engine capabilities rather than hard-coding one specific application, domain, user experience, storage backend, or AI model. + +--- + +## Maturity Target + +A mature version of `kontextual-engine` should act as a robust, scalable backend for governed, AI-assisted knowledge management. + +It should be able to: + +* ingest and manage heterogeneous knowledge assets +* maintain persistent and traceable knowledge state +* represent context through metadata, relationships, provenance, and lifecycle state +* expose reliable APIs for applications, automation systems, and AI agents +* support search, retrieval, transformation, and workflow execution +* enforce permissions, auditability, review, and governance controls +* integrate with external storage, document, content, data, and search systems +* enable AI agents to operate knowledge safely and effectively +* support CMS, DMS, ECM, file-service, knowledge-base, research-support, and AI-assistant use cases +* serve as a reusable foundation for knowledge-driven products and platforms + +The long-term goal is to make `kontextual-engine` a default backend engine for systems that need to turn fragmented information into structured, contextual, governed, and operational knowledge. + +--- + +## Stability Note + +Changes to this file should represent deliberate changes to the intended role of the repository. + +Because this document defines the project’s durable purpose, it should remain more stable than implementation details, feature plans, vendor comparisons, deployment-specific architecture decisions, or temporary implementation constraints. diff --git a/wiki/kontextual-engine_scope_research_md_bundle/07_source-map.md b/wiki/kontextual-engine_scope_research_md_bundle/07_source-map.md new file mode 100644 index 0000000..695fe70 --- /dev/null +++ b/wiki/kontextual-engine_scope_research_md_bundle/07_source-map.md @@ -0,0 +1,74 @@ +# kontextual-engine — Source Map + +Research date: 2026-05-05 + +This file collects the main sources consulted for the market exploration and scope refinement. Vendor descriptions in the research files are based primarily on vendor-owned pages, with market framing supplemented by analyst and industry sources. + +--- + +## Market and economic framing + +| Source | What it informed | +|---|---| +| Forrester, “Highlights From The Forrester Wave™: Content Platforms, Q1 2025” — | Content-platform market structure and AI/content-platform convergence. | +| Forrester, “Generative AI Is Ushering In A New Era Of Intelligent Content Management” — | AI as a driver of renewed ECM/content-management value. | +| McKinsey, “The economic potential of generative AI” — | Broad economic-value framing for knowledge-worker AI use cases. | +| AIIM, “2025 State of the Intelligent Information Management Industry” — | Strategic importance of unstructured data, information management, and governance for AI initiatives. | + +--- + +## Enterprise content, document, governance, and file platforms + +| Vendor/system | Sources | +|---|---| +| Microsoft SharePoint / SharePoint Premium | , | +| OpenText Content Cloud | , , | +| Hyland | | +| Alfresco | , | +| Nuxeo | , | +| Box | , , | +| M-Files | , , | +| Laserfiche | , , | +| DocuWare | , | +| Doxis | | +| iManage | , , | +| NetDocuments | , | + +--- + +## Enterprise search, AI context, RAG, and agentic platforms + +| Vendor/system | Sources | +|---|---| +| Glean | , , | +| Google Gemini Enterprise | , , | +| Sinequa | , , | +| Coveo | , , | +| Elastic | , | +| Dropbox Dash | , , | + +--- + +## Headless CMS, composable content, content supply chain + +| Vendor/system | Sources | +|---|---| +| Contentful | , , | +| Contentstack | , , | +| Sanity | , , | +| Adobe Experience Manager / GenStudio | , , , | +| Strapi | , | +| Directus | , , | + +--- + +## Team knowledge and collaboration systems + +| Vendor/system | Sources | +|---|---| +| Atlassian Confluence | | +| Notion | , , | +| Guru | , , | +| ServiceNow Knowledge Management | , | + +---