Skip to main content

CODITECT-DOCUMENT-MANAGEMENT System - Master Project Plan

Project: CODITECT Enterprise Document Management System Type: Strategic Product Development + Internal Implementation Status: Planning Phase Timeline: 24 weeks (6 months) Budget: $312K (engineering + infrastructure) Expected ROI: 975% (2-year cumulative)

Executive Summary​

Objective: Build and deploy production-grade enterprise document management system with AI-powered classification, semantic search, and real-time analytics as both:

  1. Internal Infrastructure - Manage 6,655+ CODITECT documents
  2. Product Offering - Sold to customers as standalone or bundled with CODITECT-CORE

Key Innovation: MoE (Mixture of Experts) autonomous document classification achieving 99.9%+ accuracy without manual review.

Strategic Value:

  • Internal: Eliminate document chaos, ensure consistency, enable discovery
  • External: $594K Year 1 revenue, $850M addressable market
  • Competitive: First-to-market AI-native DMS with production-grade autonomous classification

Workflow Steps​

  1. Initialize - Set up the environment
  2. Configure - Apply settings
  3. Execute - Run the process
  4. Validate - Check results
  5. Complete - Finalize workflow

Project Phases Overview​

PhaseComponentDurationStatusDeliverables
Phase 1Document Frontmatter System4 weeks🟡 PlanningADR-018 implementation across 6,655 docs
Phase 2MoE Classification Engine4 weeks🔴 Not Started5 analysts + 3 judges + orchestrator
Phase 3CODITECT-CORE Integration3 weeks🔴 Not StartedBuilt-in frontmatter hooks
Phase 4Enterprise DMS Foundation4 weeks🔴 Not StartedPostgreSQL + pgvector + Redis
Phase 5Semantic Search & Analytics3 weeks🔴 Not StartedVector search + GraphRAG
Phase 6Product Infrastructure3 weeks🔴 Not StartedMulti-tenant SaaS deployment
Phase 7Testing & Validation2 weeks🔴 Not StartedQA certification + benchmarks
Phase 8Documentation & Launch1 week🔴 Not StartedProduct docs + marketing

Total Duration: 24 weeks (6 months)

Phase 1: Document Frontmatter System (Weeks 1-4)​

Purpose: Implement ADR-018 Agentic Documentation Standard across all CODITECT documents, creating foundation for document management.

Goals​

  • Define and validate frontmatter schema (ADR-018)
  • Scan and inventory all 6,655 documents
  • Create automation tools for frontmatter injection
  • Apply frontmatter to all documents
  • Validate 100% conformance

Deliverables​

  • ADR-018 specification finalized
  • Document inventory (JSON + reports)
  • Frontmatter automation toolkit (Python scripts)
  • 6,655 documents with valid frontmatter
  • Validation reports with conformance metrics

Success Metrics​

  • 100% document coverage (6,655/6,655)
  • 100% ADR-018 conformance
  • Zero schema validation errors
  • Complete audit trail

Dependencies: None (Phase 1 kickoff)

Phase 2: MoE Classification Engine (Weeks 2-5)​

Purpose: Build production-grade autonomous classification system using Mixture of Experts pattern with multi-judge validation.

Goals​

  • Design MoE architecture (5 analysts + 3 judges)
  • Implement analyst agents (structural, content, metadata, semantic, pattern)
  • Implement judge agents (consistency, quality, domain)
  • Build orchestration engine with consensus algorithm
  • Create audit trail and logging system
  • Execute classification on 6,655 documents
  • Validate 99.9%+ accuracy

Deliverables​

  • ADR-019: MoE Document Classification System Architecture
  • 5 specialist analyst agent implementations
  • 3 judge agent implementations
  • Orchestration engine with consensus algorithm
  • Classification results with audit trail (6,655 docs)
  • Quality validation report

Success Metrics​

  • Classification accuracy ≥99.9% (max 7 errors)
  • Throughput ≥100 docs/minute
  • Average confidence score ≥90%
  • Zero manual interventions required

Dependencies: Phase 1 (frontmatter schema must exist)

Phase 3: CODITECT-CORE Integration (Weeks 5-7)​

Purpose: Integrate frontmatter system into CODITECT-CORE as built-in functionality (free tier).

Goals​

  • Design document lifecycle hooks
  • Implement auto-injection on document creation
  • Implement auto-update on document modification
  • Create CLI tools (init, validate, update)
  • Add pre-commit hooks for validation
  • Update component creation templates
  • Document integration patterns

Deliverables​

  • Document creation hooks (auto-inject frontmatter)
  • Document modification hooks (update timestamps)
  • CLI tools: coditect doc init, validate, update
  • Pre-commit hook for frontmatter validation
  • Updated component templates (agents, commands, skills)
  • Integration documentation

Success Metrics​

  • All new documents auto-inject frontmatter
  • All modified documents auto-update timestamps
  • CLI tools functional and tested
  • Zero manual frontmatter management required

Dependencies: Phase 1 (frontmatter schema), Phase 2 (classification for new docs)

Phase 4: Enterprise DMS Foundation (Weeks 6-9)​

Purpose: Build enterprise-grade database infrastructure with vector search and graph relationships.

Goals​

  • Design PostgreSQL schema with pgvector extension
  • Implement TimescaleDB for time-series metrics
  • Configure Redis for caching and sessions
  • Set up Celery for background processing
  • Create database migrations system
  • Implement backup and recovery
  • Deploy to GCP development environment

Deliverables​

  • PostgreSQL database schema (documents, chunks, relationships)
  • pgvector configuration for semantic search
  • TimescaleDB metrics tables
  • Redis cache configuration
  • Celery task queue infrastructure
  • Database migration scripts
  • Backup and recovery procedures
  • GCP Cloud SQL deployment

Success Metrics​

  • Database handles 10,000+ documents
  • Vector search latency <100ms (p95)
  • Cache hit rate ≥80%
  • Backup/recovery tested and verified

Dependencies: Phase 1 (document metadata schema)

Phase 5: Semantic Search & Analytics (Weeks 8-10)​

Purpose: Implement intelligent document search with vector embeddings and GraphRAG.

Goals​

  • Implement document chunking with overlap
  • Generate vector embeddings (OpenAI/Anthropic)
  • Build vector similarity search
  • Implement GraphRAG relationship mapping
  • Create search API endpoints
  • Build analytics aggregation engine
  • Design metrics dashboard

Deliverables​

  • Document chunking service (UUID-tagged chunks)
  • Embedding generation pipeline
  • Vector similarity search API
  • GraphRAG relationship engine
  • Search API endpoints (GET /documents/search)
  • Metrics aggregation service
  • Analytics dashboard (React frontend)

Success Metrics​

  • Search recall (k=10) ≥95%
  • Query latency (p95) <100ms
  • GraphRAG relationship accuracy ≥90%
  • Dashboard load time <2 seconds

Dependencies: Phase 4 (database infrastructure)

Phase 6: Product Infrastructure (Weeks 11-13)​

Purpose: Build multi-tenant SaaS infrastructure for external customers.

Goals​

  • Design multi-tenant isolation architecture
  • Implement tenant provisioning system
  • Create authentication system (JWT + RBAC)
  • Build API gateway with rate limiting
  • Set up Kubernetes deployment
  • Configure GCP Cloud Run
  • Implement monitoring and alerting
  • Create billing integration

Deliverables​

  • Multi-tenant database schema with row-level security
  • Tenant provisioning API
  • JWT authentication service
  • RBAC permission system
  • API gateway with rate limiting
  • Kubernetes manifests (deployments, services, ingress)
  • GCP Cloud Run configuration
  • Prometheus/Grafana monitoring
  • Stripe billing integration

Success Metrics​

  • Tenant isolation 100% verified
  • Authentication success rate ≥99.99%
  • API gateway throughput ≥1,000 req/s
  • K8s cluster uptime ≥99.9%

Dependencies: Phase 5 (core functionality must exist)

Phase 7: Testing & Validation (Weeks 14-15)​

Purpose: Comprehensive testing and quality assurance before production launch.

Goals​

  • Unit test coverage ≥80%
  • Integration test suite
  • Performance benchmarking
  • Security penetration testing
  • Load testing (1,000 concurrent users)
  • Disaster recovery testing
  • Accessibility compliance (WCAG 2.1 AA)
  • Production readiness review

Deliverables​

  • Unit test suite (≥80% coverage)
  • Integration test suite
  • Performance benchmark report
  • Security audit report
  • Load test results (1,000+ users)
  • Disaster recovery runbook
  • Accessibility compliance report
  • Production readiness checklist (signed off)

Success Metrics​

  • Test coverage ≥80%
  • Zero critical security vulnerabilities
  • p95 latency <100ms under load
  • Recovery time objective (RTO) <1 hour

Dependencies: All previous phases (complete system must exist)

Phase 8: Documentation & Launch (Week 16)​

Purpose: Finalize product documentation and execute market launch.

Goals​

  • Complete API documentation (OpenAPI spec)
  • Write customer onboarding guides
  • Create video tutorials
  • Finalize pricing and packaging
  • Prepare marketing collateral
  • Set up customer support infrastructure
  • Execute beta launch
  • Monitor initial customer feedback

Deliverables​

  • API documentation (Swagger/ReDoc)
  • Customer onboarding guides
  • Video tutorial library (5+ videos)
  • Pricing page and packaging tiers
  • Marketing website pages
  • Customer support portal
  • Beta launch announcement
  • Customer feedback analysis

Success Metrics​

  • Documentation completeness 100%
  • Beta signup conversion ≥10%
  • Customer support response time <4 hours
  • Net Promoter Score (NPS) ≥40

Dependencies: Phase 7 (production readiness)

Product Architecture​

Two-Tier Strategy​

Tier 1: CODITECT-CORE (Built-in, Free)​

Purpose: Customer acquisition, ecosystem lock-in

Features:

  • Automatic frontmatter injection on document creation
  • Real-time timestamp updates on modifications
  • CLI tools: coditect doc init, validate, update
  • ADR-018 schema validation
  • Pre-commit hooks for frontmatter enforcement

Target Users: All CODITECT users (free) Value Proposition: Zero-config document consistency

Tier 2: CODITECT-DOCUMENT-MANAGEMENT (Enterprise Add-on)​

Purpose: Revenue generation, enterprise customers

Features:

  • Semantic vector search (pgvector, 95%+ recall)
  • Intelligent chunking with GraphRAG
  • Real-time analytics and insights dashboard
  • Multi-tenant SaaS deployment
  • Advanced security (JWT, RBAC, rate limiting)
  • Background processing (Celery task queue)
  • Monitoring and alerting (Prometheus/Grafana)

Pricing:

  • Pro: $49/month (up to 10,000 documents)
  • Enterprise: Custom pricing (unlimited, dedicated infrastructure)

Target Users: Medium to large organizations Value Proposition: Enterprise-grade DMS with AI-powered intelligence

Financial Projections​

Investment Required​

  • Phase 1-3 (CODITECT-CORE): $78K (1 engineer, 7 weeks)
  • Phase 4-6 (Enterprise DMS): $156K (2 engineers, 10 weeks)
  • Phase 7-8 (Testing/Launch): $39K (1 engineer, 3 weeks)
  • Infrastructure (Year 1): $39K (GCP, PostgreSQL, Redis)
  • Total Year 1: $312K

Revenue Projections​

Year 1:

  • Pro tier: 500 customers × $49/month × 12 = $294K
  • Enterprise tier: 50 customers × $500/month × 12 = $300K
  • Total: $594K

Year 2:

  • Pro tier: 1,500 customers × $49/month × 12 = $882K
  • Enterprise tier: 200 customers × $500/month × 12 = $1.2M
  • Total: $2.082M

ROI Analysis​

  • Year 1: 365% ROI (profit $366K)
  • Year 2: 610% ROI (profit $1.77M)
  • 2-Year Cumulative: 975% ROI
  • Break-even: Month 3

Risk Management​

Technical Risks​

RiskProbabilityImpactMitigation
MoE classification accuracy <99.9%MediumHighMulti-judge validation, confidence thresholds, sample testing
Vector search performance degradationMediumMediumIndex optimization, caching, horizontal scaling
Multi-tenant data leakageLowCriticalRow-level security, tenant isolation testing, security audit
Database scalability limitsLowHighTimescaleDB partitioning, read replicas, sharding strategy

Business Risks​

RiskProbabilityImpactMitigation
Low customer adoptionMediumHighBeta program, customer feedback loops, pricing flexibility
Competitor launches similar productMediumMediumFirst-mover advantage, patent filing, continuous innovation
Infrastructure costs exceed budgetLowMediumCost monitoring, reserved instances, autoscaling policies
Regulatory compliance (GDPR, HIPAA)LowHighLegal review, compliance framework, audit trail

Resource Allocation​

Team Structure​

Engineering:

  • 2 Full-Stack Engineers (Phases 1-6)
  • 1 AI/ML Specialist (Phases 2-3)
  • 1 DevOps Engineer (Phases 4-6)
  • 1 QA Engineer (Phase 7)

Total: 5 engineers, 24 weeks

Agent Coordination​

Autonomous Agents:

  • 5 Analyst Agents (structural, content, metadata, semantic, pattern)
  • 3 Judge Agents (consistency, quality, domain)
  • 1 Orchestrator Agent (workflow coordination)
  • 10+ Support Agents (senior-architect, database-architect, devops-engineer, etc.)

Total: 19+ AI agents across all phases

Success Criteria​

Internal Success (CODITECT)​

  • 6,655 documents classified with ≥99.9% accuracy
  • Frontmatter system integrated into CODITECT-CORE
  • Zero manual document management overhead
  • Improved document discoverability (search works)

Product Success (External)​

  • 500+ paying customers (Pro tier) by end of Year 1
  • $594K revenue in Year 1
  • NPS score ≥40 (customer satisfaction)
  • 99.9% uptime SLA achieved

Technical Success​

  • Test coverage ≥80%
  • Zero critical security vulnerabilities
  • API latency p95 <100ms
  • Search recall (k=10) ≥95%

Approval Requirements​

This project requires approval from:

  • CEO (budget allocation: $312K)
  • CTO (technical architecture review)
  • VP Engineering (resource allocation: 5 engineers)
  • VP Product (product strategy alignment)

Approval Deadline: January 5, 2026 Expected Start Date: January 6, 2026 Expected Launch Date: June 30, 2026 (6 months)

Business:

Technical:

Execution:

Document Version: 1.0.0 Last Updated: December 27, 2025 Status: Awaiting Stakeholder Approval Owner: CODITECT Product Team Approvers: CEO, CTO, VP Engineering, VP Product