# 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**.