Executive Summary — WO System for CODITECT (Updated)
Status: Go (Conditional) | Version: 2.0 | Date: 2026-02-13
Audience: CTO / VP Engineering / Investors
Problem Statement
Every modification to a validated system in regulated industries — from upgrading a lab workstation's operating system to recalibrating a clinical instrument — requires formal change control documentation. Today, this process is manual, paper-intensive, and disconnected from the actual technical work. A single Windows 10 → 11 upgrade on a lab workstation generates 6+ work orders, requires System Owner and QA approval with electronic signatures, and takes 15-45 days to complete through change control — even though the technical work takes 2-3 days.
AI agents can already write code, configure systems, and automate deployments. But in regulated environments, they cannot act without documented change control. Without a compliance-native work order system, AI agents are locked out of the $3.5B bioscience change control market.
Solution
CODITECT's Work Order (WO) system is a compliance-native change control engine that:
- Generates work orders automatically when AI agents identify changes needed on validated systems
- Decomposes complex changes into Master/Linked WO hierarchies that map directly to CODITECT's orchestrator-workers pattern
- Enforces 21 CFR Part 11 with database-level audit trails, electronic signatures, and separation of duties — structurally, not procedurally
- Orchestrates 7 specialized agents (Asset Management, Scheduling, Experience Matching, QA Review, Vendor Coordination, Documentation, WO Orchestrator) with deterministic model routing
- Preserves human authority at approval gates — no autonomous agent can approve regulatory changes
Architecture Validation (Enhanced)
The full specification now includes production-ready depth across four critical dimensions:
| Dimension | Specification Depth | Readiness |
|---|---|---|
| Data Model | 20+ normalized entities (Prisma schema), polymorphic Party model, ChangeItem registry, full JobPlan requirements graph | Implementation-ready |
| State Machine | 9 states, 8 transition types, composable guard functions per transition, Master/Linked aggregation rules | Implementation-ready |
| RBAC | 8 roles, 40+ permission entries, 5 hard separation-of-duty rules, RLS multi-tenancy, agent identity model | Implementation-ready |
| Agent Architecture | 7 agent nodes, 15+ typed message contracts, circuit breaker configs, LangGraph graph definition, token budget projections | POC-ready |
| API Surface | Full OpenAPI 3.1 spec — CRUD for WOs, JobPlans, Schedules, Approvals, E-Signatures, guard-aware transitions | Implementation-ready |
| E-Signature Flow | Part 11-compliant 2-phase approval with signer identity, meaning, timestamp, auth context | Implementation-ready |
Market Opportunity
| Metric | Value |
|---|---|
| Primary TAM (Change Control + CSV) | $3.5B by 2028 |
| SAM (Accessible regulated segments) | $1.9B |
| SOM (3-year target) | $28.8M ARR |
| Competitive white space | High compliance + High AI capability quadrant is empty |
No existing vendor occupies the intersection of autonomous AI agents and regulated change control. Veeva, MasterControl, and TrackWise have compliance depth but zero AI agent integration. Cursor and GitHub Copilot have AI capability but zero compliance infrastructure. CODITECT targets the only unoccupied quadrant.
Quantified Value Proposition
| Metric | Before | After | Impact |
|---|---|---|---|
| Change control cycle time | 15-45 days | 3-8 days | 70-80% reduction |
| CSV documentation effort | 120-400 hrs/system | 20-60 hrs/system | 80-85% reduction |
| Audit findings per inspection | 3-8 | 0-2 | 60-75% reduction |
| Compliance staff productivity | 40% proactive | 80% proactive | 2× improvement |
| Average ACV potential | N/A | $240K | New revenue stream |
| Token cost savings (model routing) | Baseline | -60% | 60% cost reduction |
Unit Economics (Mature State)
| Metric | Value |
|---|---|
| Average ACV | $240K |
| Gross margin | 78% |
| CAC (blended) | $45K |
| LTV (5-year) | $840K |
| LTV:CAC | 18.7× |
| Payback period | 7 months |
| Net revenue retention | 140% |
Revenue Trajectory
| Year | Customers | ARR | Gross Margin |
|---|---|---|---|
| Y1 | 10 | $1.2M | 65% |
| Y2 | 45 | $8.1M | 72% |
| Y3 | 120 | $28.8M | 78% |
Risks & Mitigations
| Risk | Severity | Mitigation |
|---|---|---|
| FDA acceptance of AI-generated change control | High | Human checkpoints preserved at all approval gates; proactive FDA engagement |
| Enterprise sales cycle length (6-9 months) | Medium | Lighthouse strategy with mid-tier biotech; product-led growth |
| Incumbent QMS vendor adds AI agents | High | 18-month head start; regulatory moat; $200K-$2M switching costs per customer |
| Token cost volatility | Medium | Multi-model routing; hedging across Anthropic, OpenAI, open-source |
| Credential exposure in Job Plans | Critical | Vault integration (blocking prerequisite) |
Blocking Prerequisites
Three conditions must resolve before regulated deployment:
- Vault integration for Job Plan credentials — no secrets in PostgreSQL JSONB
- DAG cycle detection on WO dependency graphs — prevents orchestration deadlocks
- Partial completion policies — requires customer input per regulatory domain
Recommendation
Go — Conditional on the three blocking prerequisites above.
The WO system is not an optional feature. It is the compliance gateway that transforms CODITECT from "another AI code tool" into "the only platform that can autonomously develop software for regulated industries." The $3.5B primary TAM is accessible, the competitive white space is real, and the architecture is validated at production-ready depth. Build it first — it's the moat.