DocuGraph Business Vision & Monetization Strategy

Estado (MASTER-00): PROPUESTA — contiene afirmaciones de negocio (precio, KPIs, personas) aún no validadas. Ver hipótesis H07–H09 en research/02_HYPOTHESES.md. Gobernanza: MASTER-00.

Consolidación multiplataforma (DEC-002): el posicionamiento se amplía a Mobile + Desktop con núcleo KMP compartido. Desktop es el entorno operativo principal (Fase D); Mobile es la superficie de validación Shipaton. La monetización es multiplataforma (§5.4–§5.6 y §8.4 de MASTER-00 v0.7, marcado PROPUESTA). Los precios verificados se declaran en H12.

Business Vision & Product Strategy

Nota de alcance vigente (DEC-013/DEC-014): esta visión de negocio y monetización se conserva como propuesta histórica no activa. La herramienta actual es self-hosted y gratuita, sin aplicación Compose, backend relacional ni RevenueCat.

DocuGraph is engineered to become the industry-standard AI-assisted atomic documentation platform, dependency graph engine, and cross-platform editing environment (Mobile + Desktop) for modern software engineering teams, AI-native developers, and software architects.

Problem Statement

As modern software development shifts toward AI-driven code generation, traditional documentation methodologies are failing due to four critical bottlenecks:

Context Window Overflow & AI Hallucinations: Large, monolithic documentation files overload the context windows of AI coding agents (such as Cursor, Claude Code, and Copilot). Lacking precise context, agents hallucinate non-existent APIs or introduce architectural regressions.
Documentation Fragmentation & Drift: Technical specifications quickly become disconnected from actual implementation code. Updates to database models or API contracts are rarely reflected across dependent documents.
Lack of Visual Dependency Comprehension: Software architects lack interactive visualization tools to understand relationships between business user stories, system endpoints, UI screens, and database schemas.
Unverifiable Requirement Traceability: Engineering leads have no automated mechanism to prove that 100% of user stories map to verified backend endpoints, frontend screens, and test cases.

Target Audience & User Personas

DocuGraph directly targets three core engineering personas:

Persona 1: Alex — AI-Native Software Developer

Role: Full-stack developer building features with AI coding assistants (Cursor, Claude Code).
Pain Point: Spends hours trimming down documentation files to fit context limits or fixing bugs caused by AI agent hallucinations.
Goal: Instantly query an MCP server to retrieve a 100% accurate, minimal context bundle containing only the specific user story, API contract, and schema required for the current task.

Persona 2: Elena — Lead Software Architect

Role: System architect designing multi-service systems and cross-platform mobile/desktop applications.
Pain Point: Hard to visualize system dependencies across complex monorepos; frequent circular dependencies introduced by team members.
Goal: Maintain an interactive, visual DAG canvas of all system nodes, automatically enforce structural frontmatter rules, and run automated cycle detection algorithms.

Persona 3: Marcus — Engineering Director / Tech Lead

Role: Engineering manager overseeing compliance, delivery velocity, and product quality.
Pain Point: Inability to verify if all user stories are backed by implemented code and passing unit/E2E tests.
Goal: Leverage continuous integration traceability audits to ensure 100% matrix coverage between user requirements, OpenAPI endpoints, and test suites.

Value Proposition & Core Capabilities

DocuGraph delivers value through four pillars:

Atomic Frontmatter DAG Engine: Converts standard Markdown files with YAML frontmatter headers into a directed acyclic graph, establishing strict parent/child relationships (depends_on, depended_by).
Multiplatform Compose Visual Canvas: Offers a high-performance interactive visual canvas (Android, iOS, Desktop, Web) with real-time zooming, panning, multi-criteria filtering, and inspector panels.
Kotlin MCP Context Delivery: Exposes an MCP server that computes reverse topological traversals, delivering hyper-focused markdown context bundles to AI agents without token waste.
Automated Audit Suite: Runs Tarjan’s Strongly Connected Components algorithm and broken-link detectors to guarantee zero circular dependencies and 100% specification integrity.

Freemium Monetization Model (RevenueCat Integration)

DocuGraph adopts a Freemium business model designed to drive bottom-up developer adoption while monetizing advanced team collaboration, cloud synchronization, and unlimited MCP context extraction. Subscription billing is managed via RevenueCat.

Feature Tier Comparison

Feature Capability	Free Tier	Pro Tier ($19/mo or $190/yr)
Local Documentation Projects	Up to 3 projects	Unlimited projects
Visual Graph Canvas & Inspector	Standard local graph editing	Advanced filtering, export, custom themes
MCP Server Context Extraction	Basic context (max depth = 2 levels)	Deep context extraction (unlimited depth)
Relational Sync & Indexing	Local SQLite storage	PostgreSQL Cloud Sync & team collaboration
Traceability Audit Engine	Manual graph validation	Automated CI/CD audit runner & export
RevenueCat Entitlement ID	None (Default)	`pro_tier`

RevenueCat Architectural Integration

DocuGraph handles multiplatform entitlement management cleanly across native mobile, desktop JVM, and server environments:

Mobile Platforms (Android & iOS): Native integration using the RevenueCat Mobile SDK (purchases-flutter / purchases-kmp) for handling Google Play In-App Billing and Apple App Store Subscriptions. Surface de validación Shipaton (Fases A–C).
Desktop JVM (Windows/macOS/Linux): Entorno operativo principal (Fase D). Compras vía web checkout (Stripe/RevenueCat Web Billing) — compra Desktop TBD (DEC-002). El estado de entitlement se verifica con la RevenueCat REST API v1 (GET /v1/subscribers/{app_user_id}) y se comparte sobre el núcleo con Mobile.
Entitlement Verification & JWT Caching: Upon successful subscription verification, the backend issues an authenticated JWT token containing the entitlement: "pro_tier" claim, cached locally for up to 7 days to support offline validation.

Key Performance Indicators (KPIs)

DocuGraph tracks three primary business and technical metrics:

Monthly Active Workspaces (MAW): Target 10,000 active documentation workspaces within 12 months.
Pro Conversion Rate: Target >5% conversion from Free tier users to Pro tier subscribers within 30 days of onboarding.
AI Task Execution Speedup: Target a 40% reduction in token consumption and AI agent context iteration time during feature implementation.

User Story Roadmap Mapping

The business vision is operationalized through six core User Stories in Phase 1:

US-01_login.md (US-01): Secure user authentication and session management.
US-02_create_project.md (US-02): Project creation, Git repository linking, and YAML frontmatter schema setup.
US-03_view_graph.md (US-03): Interactive node-edge graph visualization and filtering canvas.
US-04_get_task_context.md (US-04): MCP context bundle extraction for AI agent task implementation.
US-05_validate_traceability.md (US-05): Automated graph integrity, broken reference audit, and cycle detection.
US-06_revenuecat_pro.md (US-06): RevenueCat paywall integration, subscription management, and Pro entitlement unlocking.

Traceability Index

Upstream Dependencies:
- README.md (README) — Project Architecture & Methodology.
Downstream User Stories:
- US-01_login.md (US-01)
- US-02_create_project.md (US-02)
- US-03_view_graph.md (US-03)
- US-04_get_task_context.md (US-04)
- US-05_validate_traceability.md (US-05)
- US-06_revenuecat_pro.md (US-06)
Related Guidelines:
- AGENTS.md (AGENTS) — AI Agent Protocol.