6.0 KiB
Kontextual Engine Product Requirements Document V0.1
kontextual-engine
1. Product Overview
1.1 Product Name
kontextual-engine
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.
It provides a persistent, service-oriented runtime for knowledge systems, enabling automated and agent-driven workflows over structured and semi-structured data.
2. Product Intent
2.1 Problem Statement
Modern knowledge systems face several limitations:
- 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 results in inefficient knowledge reuse, poor traceability, and limited scalability.
2.2 Intended Outcomes
kontextual-engine enables:
- 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
2.3 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
3. Scope Definition
3.1 In Scope
- 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)
3.2 Out of Scope
- 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)
3.3 Boundary Clarification
kontextual-engine provides a runtime system, not primitives or projects:
- Tooling primitives →
markitect-tool - Project/application usage →
infospace-bench
4. Functional Expectations
4.1 Core Capabilities
The product must support:
-
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
4.2 Interaction Modes
- API-first (service endpoints)
- Agent-driven execution
- Programmatic integration
5. Non-Functional Expectations
5.1 Performance
- Scalable handling of large and heterogeneous data sets
- Efficient retrieval and transformation operations
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:
markitect-tool → makes markdown structured and manipulable
kontextual-engine → makes knowledge persistent and operable
infospace-bench → makes knowledge concrete and meaningful
Layers:
- Syntax layer → markitect-tool
- System layer → kontextual-engine
- Application layer → infospace-bench
11. PRD Type
Hybrid / Boundary PRD
This PRD defines system-level intent and constraints while allowing architectural flexibility and iterative development.
🧠 Final insight (important)
If markitect-tool was about:
“making knowledge manipulable”
Then kontextual-engine is about:
“making knowledge operational”
That distinction will keep this repo from turning into an unbounded platform.