ADPA
Governing AI-Driven Decisions Through Traceability and Audit
"A platform I built to transform AI-generated documents into structured, validated, and auditable decision systems."
Most AI tools generate content.
ADPA makes that content verifiable, governed, and usable in real enterprise environments.
It combines:
- AI-driven document generation
- Entity extraction and traceability
- Validation and audit pipelines
Into a system aligned with PMBOK, BABOK, and DMBOK governance practices.
The Problem I Solved
Modern AI systems can generate documents—but they introduce risk:
- ✕ No traceability from output → source
- ✕ No validation of correctness or completeness
- ✕ No audit trail for decisions
- ✕ No alignment with governance frameworks
Enterprises don’t just need AI that works.
They need AI they can prove is correct and compliant.
My Approach
I designed ADPA around a simple but powerful principle:
Instead of treating documents as static text, ADPA:
- Extracts structured entities (risks, stakeholders, requirements, etc.)
- Links them in a governed registry model
- Validates them using deterministic rules (AEV)
- Produces audit-ready outputs with full traceability
Key Capabilities
✅ Governed Document Generation
- AI-generated documents aligned with PMBOK / BABOK / DMBOK
- Template-driven with structured inputs
- Multi-provider AI orchestration (OpenAI, Google, etc.)
✅ Entity Extraction & Registry
- Documents converted into structured entities
- Linked across lifecycle (requirements, risks, stakeholders)
- Enables traceability across decisions
✅ Validation & Audit Pipeline
- Multi-layer validation (schema, semantic, completeness)
- Quality scoring and audit logs
- Drift detection against baselines
✅ Drift Detection & Resolution
- Detects deviation from expected project state
- Flags inconsistencies across documents and entities
- AI-assisted resolution workflows
✅ Enterprise Integration Layer
- Confluence, SharePoint, GitHub
- API-driven architecture (Express + Next.js)
- Real-time updates via WebSockets
Architecture Overview
ADPA is built as a full-stack enterprise platform:
- Frontend: Next.js (admin portal + onboarding flows)
- Backend: Express.js (modular API)
- Database: PostgreSQL (Supabase)
- Cache/Queue: Redis (Railway)
- AI Layer: Multi-provider orchestration
- Observability: Langfuse tracing
Onboarding Model
ADPA uses a dual-entry model:
1. Assess (Public Entry)
Users upload documents → receive maturity score, gap analysis, and initial insights.
2. Govern (Core Platform)
That same data becomes a structured project, fully traceable entities, and audit-ready documentation.
3. Operate (Scale)
Continuous document generation under governance with real-time validation and monitoring.
💡 Key Insight: The same pipeline powers onboarding and enterprise governance.
What I Focused On (Engineering Perspective)
- Designing systems that scale beyond 10,000+ entities per project
- Building deterministic validation layers on top of probabilistic AI
- Creating traceability between documents, entities, and decisions
- Ensuring alignment with real-world governance frameworks
Current Status
- Production-grade architecture established (ADPA V7 registry)
- Core pipeline (Document → Entity → Audit) is operational
- Active refactoring and stabilization phase
- Extending toward AI system governance, compliance artifact generation, and agent-based workflows
Why This Matters
With regulations like the EU AI Act coming into force (2026), organizations must track how AI outputs are created, validate their correctness, and provide audit evidence.
ADPA is designed to make that possible—by default.
Personal Reflection
ADPA represents my shift from building applications → to building governance systems for AI-driven environments.
It combines software engineering, data structuring, and enterprise governance thinking into a single architecture.
System Architecture & Flows
ADPA transforms raw AI-generated content into structured, validated, and governed decision systems through a layered architecture.