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INTENT.md

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+# INTENT.md — Project Vantage Point
+
+## Purpose
+
+Project Vantage Point aims to establish a generic, extensible system for exploring, analyzing, and reasoning about dependency structures across arbitrary domains.
+
+At its core, Vantage Point treats systems as **network-based graph models (NBGM)** consisting of entities (nodes) and relationships (edges) enriched with attributes, provenance, and semantics. The project provides a unified way to inspect these structures and derive actionable understanding from them.
+
+## Core Idea
+
+Understanding complex systems requires more than visualizing connections — it requires the ability to:
+
+- shift perspective,
+- reduce complexity,
+- reveal structure,
+- and interpret relationships in context.
+
+Vantage Point is designed as a **multi-perspective exploration environment** where users can adopt different “vantage points” on the same underlying graph to answer domain-specific questions.
+
+This provides a practical implementation of *Network-Based Graph Models (NBGM)* based on the article
+"The Four-Level Nested Model Revisited: Blocks and Guidelines" by Miriah Meyer, Michael Sedlmair and Tamara Munzner
+detailing concepts of Tamara Munzner’s nested model explorations. 
+
+We will try and use this as the foundation for a clean, research-grounded vocabulary.
+
+See: https://miriah.github.io/publications/nbgm-beliv.pdf
+
+## Scope of Intent
+
+The project focuses on building a **domain-agnostic dependency intelligence layer** that can be bound to specific domains through configuration rather than code changes.
+
+It is not limited to any specific application area. Intended domains include, but are not limited to:
+
+- Software architecture and code dependencies
+- Infrastructure and operational systems
+- Organizational and ownership structures
+- Product and capability models
+- Knowledge graphs and conceptual systems
+- Legal, economic, or argumentation networks
+
+## Guiding Principles
+
+### 1. Generic Core, Domain-Specific Interpretation
+The underlying graph model remains neutral. Meaning emerges through domain bindings, lenses, and interpretations.
+
+### 2. Perspective over Representation
+There is no single “correct” visualization. Different questions require different vantage points, layouts, and abstractions.
+
+### 3. Reduction over Exhaustiveness
+Clarity is achieved by filtering, aggregating, and focusing — not by rendering the entire graph at once.
+
+### 4. Provenance and Trust
+All relationships should carry information about origin, confidence, and freshness to support reliable reasoning.
+
+### 5. Explainability First
+Every visual element should be inspectable and explainable in terms of:
+- what it represents,
+- why it exists,
+- and how it was derived.
+
+### 6. Evolution Awareness
+Graphs are dynamic. The system should support comparison, drift detection, and temporal reasoning.
+
+### 7. Composability
+The system should allow composition of:
+- data sources,
+- graph transformations,
+- visual mappings,
+- and analytical lenses.
+
+## Intended Capabilities
+
+Vantage Point is intended to support:
+
+- Exploration of large, attribute-rich dependency graphs
+- Identification of structure (clusters, layers, cycles, hubs)
+- Analysis of impact, risk, and dependency chains
+- Comparison of graph states over time
+- Detection of inconsistencies and violations of intended structure
+- Domain-specific interpretation through configurable lenses
+
+## Non-Goals
+
+- Not a static diagramming tool
+- Not limited to a single domain or schema
+- Not dependent on a single visualization technique or layout
+- Not a passive data viewer without analytical capabilities
+
+## Vision
+
+Vantage Point becomes a foundational tool for making complex interconnected systems **inspectable, understandable, and actionable** by enabling users to observe them from the right perspective at the right level of abstraction.
+
+It turns graphs from static representations into **interactive instruments for reasoning**.
+