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Project Expansion Proposal: coditect-dev-context → Comprehensive Context Intelligence Platform

Date: November 26, 2025 Status: Proposal for Review Impact: Transform stub → Production SaaS platform

Executive Summary​

Current State: coditect-dev-context is a stub providing basic session state persistence (workspace, files, cursor).

Proposed State: Expand into comprehensive Context Intelligence Platform that provides:

  1. Session State (existing stub) - Current workspace/file state
  2. Historical Intelligence (NEW) - AI conversation search, Git analytics, team metrics
  3. Hybrid Deployment (NEW) - Works standalone OR integrated with CODITECT platform

Market Opportunity: $26B GenAI developer tools market + CODITECT ecosystem expansion

Investment: $120K development (14 weeks) + $200/month infrastructure

Revenue Potential: $1.2M ARR Year 1 (standalone) + CODITECT upsell revenue

Current vs. Proposed Scope​

Current Scope (Stub Implementation)​

What It Does:

  • Captures workspace state (open files, cursor position)
  • Saves/restores session context
  • Syncs to cloud (planned)
  • Prevents "catastrophic forgetting" for current session

Technology: Python, FastAPI, JSON/YAML storage

Status: Early development, basic functionality

Market: CODITECT ecosystem only

Proposed Expansion​

Add Historical Intelligence:

  • AI Conversation History: Store all Claude Code, Copilot, Cursor conversations
  • Git Analytics: Track commits, correlate with conversations
  • Semantic Search: Hybrid search (keyword + vector) across conversation history
  • Team Metrics: Productivity insights, AI impact analysis
  • Knowledge Base: Team wiki built from conversation history

Add Hybrid Deployment:

  • Standalone Mode: SaaS product for any AI coding assistant user
  • CODITECT Mode: Integrated Django app within CODITECT platform
  • Pluggable Architecture: 85% shared core, 15% integration layer

Technology Additions:

  • PostgreSQL (relational data) + Weaviate (vector search)
  • Django REST Framework (CODITECT mode) + FastAPI (standalone mode)
  • Celery (background tasks) + Redis (caching)
  • Prometheus + Grafana (monitoring)

Market: $26B GenAI tools market + CODITECT ecosystem

Strategic Rationale​

1. Natural Evolution​

Session State (current) + Historical Intelligence (proposed) = Complete Context Picture

Example user workflow:

  1. Morning: Restore yesterday's workspace (existing feature)
  2. During Development: Conversations with Claude Code are captured (NEW)
  3. Afternoon: Search "How did we decide on PostgreSQL?" → finds conversation from 2 months ago (NEW)
  4. Evening: View team productivity dashboard showing AI impact (NEW)
  5. Next Day: Restore today's workspace → includes context from historical searches (synergy!)

2. Market Opportunity​

Total Addressable Market (TAM): $26B by 2030

  • AI coding tools: $26B (27.1% CAGR)
  • Developer productivity tools: $99B (23.2% CAGR)

Serviceable Addressable Market (SAM): $1.2B/year

  • 15M GitHub Copilot users × 76% AI adoption = 11.4M developers
  • Average 10-50 dev teams = 500K teams
  • $199/month average = $1.2B annual

Serviceable Obtainable Market (SOM): $1.2M ARR Year 1

  • 0.1% market share = 500 customers
  • $199/month average = $1.19M ARR

3. Competitive Positioning​

Current Market Gap: No product combines:

  • AI conversation capture & search
  • Git commit analytics
  • Conversation-to-commit correlation
  • Team knowledge base from AI interactions

Closest Competitors:

  • LinearB/Pluralsight Flow: Git analytics only (no AI conversations)
  • GitHub Copilot/Cursor: Stores conversations but no search/analytics
  • CODITECT: Has session state but no historical intelligence

Our Differentiation:

  1. Only platform that links AI conversations → Git commits
  2. Semantic search across conversation history (hybrid keyword + vector)
  3. Team knowledge graph built from AI interactions
  4. Hybrid deployment (standalone OR integrated)

4. CODITECT Synergy​

Benefits to CODITECT Platform:

  • Competitive Advantage: Feature no other AI platform offers
  • Retention: Users invested in conversation history stay longer
  • Upsell Opportunity: Tier-based features (basic search → semantic search → custom embeddings)
  • Data Network Effect: More conversations = better knowledge base = more value

Integration Value:

  • Reuse existing Django multi-tenant infrastructure (60% code reuse)
  • Shared authentication (no duplicate logins)
  • License management integration (feature flags per tier)
  • Zero additional infrastructure cost (use existing PostgreSQL)

Proposed Architecture​

Hybrid Design​

Core (85% of codebase):

coditect-dev-context/
├── core/                      # Shared core (standalone + CODITECT)
│   ├── models/               # Conversation, Commit, User, Org
│   ├── search/               # Hybrid search (PostgreSQL + Weaviate)
│   ├── analytics/            # Team metrics, productivity insights
│   └── correlation/          # Conversation-to-commit linking

Integration Layer (15% of codebase):

├── standalone/               # Standalone mode
│   ├── auth/                # JWT, OAuth2
│   ├── billing/             # Stripe integration
│   └── api/                 # FastAPI endpoints
│
└── coditect/                 # CODITECT integration
    ├── django_app/          # Django models/views
    ├── license_hooks/       # Feature flags per tier
    └── tenant_sync/         # Share org_id with CODITECT

Technology Stack​

Relational Database: PostgreSQL 15 + TimescaleDB

  • Conversations, messages, checkpoints
  • Commits, repositories
  • Users, organizations
  • Multi-tenant isolation (RLS)

Vector Database: Weaviate

  • Semantic search across conversations
  • Hybrid search (keyword + vector)
  • Multi-tenant isolation (tenant-aware classes)

API Layer:

  • Standalone: FastAPI (fast, async, modern)
  • CODITECT: Django REST Framework (60% code reuse)

Background Tasks: Celery + Redis

  • Conversation embedding generation
  • Git commit sync
  • Analytics calculation

Monitoring: Prometheus + Grafana

  • API latency, error rates
  • Search performance
  • Multi-tenant usage metrics

Implementation Roadmap​

Phase 1: Core Platform (Weeks 1-8)​

Week 1-2: Database & Models

  • PostgreSQL schema (conversations, commits, users, orgs)
  • Multi-tenant isolation (RLS policies)
  • Django/SQLAlchemy models
  • Database migrations

Week 3-4: API Layer

  • REST endpoints (CRUD for conversations, commits)
  • Authentication (JWT for standalone, Django auth for CODITECT)
  • Rate limiting (per-user, per-org)
  • API documentation (OpenAPI/Swagger)

Week 5-6: Git Integration

  • GitHub webhook ingestion
  • GitLab integration
  • Commit-to-conversation correlation (timestamp + semantic)
  • Repository sync

Week 7-8: Basic Search

  • PostgreSQL full-text search
  • Filters (date, repository, author)
  • Pagination and sorting
  • API integration

Deliverable: Working API with conversation storage, Git integration, keyword search

Phase 2: Semantic Search (Weeks 9-12)​

Week 9-10: Vector Database

  • Weaviate deployment (managed cloud or self-hosted)
  • Conversation embedding pipeline (OpenAI text-embedding-3-large)
  • PostgreSQL → Weaviate sync (Celery tasks)
  • Multi-tenant isolation (tenant-aware collections)

Week 11-12: Hybrid Search

  • Reciprocal Rank Fusion (RRF) algorithm
  • Configurable alpha weighting (keyword vs. semantic)
  • Search result ranking optimization
  • Performance tuning (<100ms p95)

Deliverable: Production-ready semantic search with <100ms latency

Phase 3: Analytics & UI (Weeks 13-16)​

Week 13-14: Analytics Engine

  • Team velocity metrics (commits/day, PRs/week)
  • AI impact analysis (AI-assisted vs manual)
  • Conversation patterns (most discussed topics)
  • Productivity trends (week-over-week, month-over-month)

Week 15-16: Web Dashboard

  • React/Vue frontend
  • Conversation browser with search
  • Git activity timeline
  • Team analytics dashboard
  • Repository health metrics

Deliverable: Complete platform with UI ready for beta testing

Phase 4: CODITECT Integration (Weeks 17-18)​

Week 17: Django Integration

  • Convert core to Django app
  • Extend existing CODITECT models (add org_id FK)
  • Integrate with CODITECT authentication
  • License management hooks (feature flags per tier)

Week 18: Testing & Polish

  • Integration tests (standalone + CODITECT modes)
  • Performance testing (10K users)
  • Security audit (multi-tenant isolation)
  • Documentation

Deliverable: Hybrid platform deployable in both modes

Cost Analysis​

Development Costs​

Team:

  • 1 Full-Stack Engineer (Django/FastAPI/PostgreSQL) - $150K/year = $50K (14 weeks)
  • 1 ML Engineer (embeddings/vector search) - $140K/year = $47K (14 weeks)
  • 1 DevOps Engineer (part-time) - $120K/year = $23K (14 weeks)

Total Development: $120K (14 weeks, 3 engineers)

Infrastructure Costs (Monthly)​

Standalone Mode:

  • PostgreSQL (AWS RDS or GCP Cloud SQL): $100/month
  • Weaviate Cloud (managed): $150/month
  • Redis (caching): $20/month
  • Application servers (3x instances): $60/month
  • Load balancer: $20/month
  • Monitoring (Datadog or Prometheus): $50/month
  • Total: $400/month

CODITECT Integration Mode:

  • Weaviate Cloud (only new infrastructure): $150/month
  • Monitoring: $50/month
  • Total: $200/month (reuse existing CODITECT PostgreSQL, Redis, servers)

Operational Costs (Annual)​

Year 1:

  • Development: $120K (one-time)
  • Infrastructure: $200/month × 12 = $2,400
  • Support & Maintenance: $30K
  • Marketing & Sales: $20K
  • Total: $172,400

Year 2+:

  • Infrastructure: $2,400-5,000/year (scales with users)
  • Support & Maintenance: $40K
  • Total: $42,400-45,000/year

Revenue Projections​

Pricing Strategy (Standalone)​

Tier Structure:

  • Starter: $49/month (up to 10 users, 50K messages, keyword search only)
  • Pro: $199/month (up to 50 users, 500K messages, semantic search, Git correlation)
  • Enterprise: $999+/month (unlimited, custom embeddings, SSO, HIPAA)

Revenue Scenarios​

Conservative (Year 1):

  • 300 Starter customers × $49 = $14,700/month
  • 150 Pro customers × $199 = $29,850/month
  • 10 Enterprise customers × $999 = $9,990/month
  • Total: $54,540/month = $654,480 ARR

Moderate (Year 1):

  • 400 Starter × $49 = $19,600/month
  • 250 Pro × $199 = $49,750/month
  • 20 Enterprise × $999 = $19,980/month
  • Total: $89,330/month = $1,071,960 ARR

Aggressive (Year 1):

  • 500 Starter × $49 = $24,500/month
  • 350 Pro × $199 = $69,650/month
  • 30 Enterprise × $999 = $29,970/month
  • Total: $124,120/month = $1,489,440 ARR

ROI Analysis​

Year 1 (Moderate Scenario):

  • Revenue: $1,071,960
  • Costs: $172,400
  • Profit: $899,560
  • ROI: 522%

Year 2 (2x growth):

  • Revenue: $2,143,920
  • Costs: $45,000
  • Profit: $2,098,920
  • Cumulative ROI: 1,318%

Break-Even: Month 2-3 (aggressive growth), Month 4-5 (moderate growth)

Risk Assessment​

Technical Risks​

Risk 1: Vector Search Performance

  • Likelihood: Medium
  • Impact: High (core feature)
  • Mitigation: Use proven Weaviate Cloud, benchmark early, optimize indexes

Risk 2: Multi-Tenant Data Leakage

  • Likelihood: Low
  • Impact: Critical (security breach)
  • Mitigation: PostgreSQL RLS, comprehensive security testing, third-party audit

Risk 3: Conversation-to-Commit Correlation Accuracy

  • Likelihood: Medium
  • Impact: Medium (feature quality)
  • Mitigation: Multi-signal approach (timestamp + semantic + explicit tags), 80%+ target accuracy

Market Risks​

Risk 4: Competition from GitHub/Anthropic

  • Likelihood: Medium
  • Impact: High (market entry)
  • Mitigation: First-mover advantage, hybrid deployment (can integrate with their tools), unique correlation feature

Risk 5: Developer Adoption

  • Likelihood: Medium
  • Impact: High (revenue)
  • Mitigation: Free tier, easy export/import, integrations with popular tools (Cursor, Copilot)

Operational Risks​

Risk 6: Scaling Infrastructure

  • Likelihood: Low (good problem to have)
  • Impact: Medium (cost spike)
  • Mitigation: Kubernetes auto-scaling, usage-based pricing, gradual rollout

Success Metrics​

Phase 1 (Weeks 1-8): Core Platform​

  • API endpoints functional (100% uptime)
  • 10,000 conversations stored
  • Git webhook integration working (GitHub + GitLab)
  • Keyword search <100ms p95 latency
  • 3 beta customers onboarded
  • Vector database operational (99.9% uptime)
  • Hybrid search working (keyword + semantic)
  • <100ms p95 search latency
  • 10 beta customers onboarded
  • User feedback: 8/10 search relevance

Phase 3 (Weeks 13-16): Analytics & UI​

  • Web dashboard deployed
  • Team analytics functional (velocity, AI impact)
  • 50 beta customers onboarded
  • $50K MRR achieved
  • NPS score: 40+

Phase 4 (Weeks 17-18): CODITECT Integration​

  • Django integration working
  • CODITECT users can access via existing login
  • License management integrated (feature flags)
  • Integration tests passing (both modes)

Year 1 Goals​

  • 500 paying customers ($1M+ ARR)
  • 99.9% uptime SLA
  • <100ms p95 API latency
  • 80%+ conversation-to-commit correlation accuracy
  • SOC 2 Type II certified

Decision Required​

Option A: Approve Expansion ✅ (Recommended)​

Proceed with:

  • Full expansion into comprehensive context intelligence platform
  • Hybrid architecture (standalone + CODITECT integration)
  • 14-week implementation timeline
  • $120K development budget

Next Steps:

  1. Approve budget and timeline
  2. Allocate engineering team (2 full-time, 1 part-time)
  3. Begin Phase 1 (Week 1: Database & Models)
  4. Identify 3-5 beta customers for early feedback

Option B: Standalone Only​

Proceed with:

  • Build standalone SaaS product only
  • Skip CODITECT integration
  • 12-week timeline, $96K budget

Trade-offs:

  • Faster to market (2 weeks sooner)
  • Lower development cost ($24K savings)
  • But: Miss CODITECT synergy, limited to external market

Option C: CODITECT Only​

Proceed with:

  • Build as CODITECT-integrated feature only
  • No standalone mode
  • 10-week timeline, $80K budget

Trade-offs:

  • Simplest implementation (no dual deployment)
  • Lowest cost ($40K savings)
  • But: Limited to CODITECT users, miss $26B market

Option D: Defer Expansion​

Keep as stub:

  • Session state only (current scope)
  • No historical intelligence or search
  • Minimal investment

Trade-offs:

  • No additional cost
  • But: Miss market opportunity, no competitive advantage

Recommendation​

APPROVE OPTION A: Full Expansion with Hybrid Architecture

Rationale:

  1. Market Opportunity: $26B market + CODITECT ecosystem = dual revenue
  2. Technical Feasibility: Proven architecture (PostgreSQL + Weaviate), 60% code reuse
  3. Competitive Advantage: Only platform linking AI conversations + Git commits
  4. Strong ROI: 522% Year 1, break-even Month 2-3
  5. Strategic Value: Differentiates CODITECT platform, independent product potential
  6. Manageable Risk: Technical risks mitigated, market validated ($26B TAM)

Investment: $120K development + $200/month infrastructure Return: $1M+ ARR Year 1, $2M+ Year 2 Timeline: 14 weeks to production-ready platform

Status: Awaiting approval Next Review: Upon approval, begin Week 1 implementation Owner: AZ1.AI INC / CODITECT Team