A Delivery Engagement

Spec-Driven Development

Ship at AI speed — with an audit trail your future self will thank you for.

You write the specs. AI agents build the code. Every line of production code traces back to intent you approved — not to a chat session nobody can reconstruct.

Your specsAgents buildProduction

Proven in production: Designed and ran this model for a Fortune-100 automotive OEM — two applications, ~10× leverage on human effort.

The Problem

Your AI is already writing code. Who governs it?

With AI in the loop, the real choice was never AI vs. hand-coding. It’s vibecoding vs. spec-driven.

VIBECODINGSPEC-DRIVEN
Source of truthChat history and developer memoryA versioned, reviewed specification
ConsistencyVaries by prompt, session, and personOne constitution — same gates, every run
Audit trailCan't reconstruct why code existsBusiness intent → model → code → test
ScaleBottlenecks on whoever prompted itNew contributors inherit the rules
GovernanceLittle to noneHuman checkpoints + automated gates

Vibecoding produces working code — and no durable asset. Six months on, nobody can say why a behavior exists.


The Model

You own intent. Agents own labor.

YOU AUTHOR · with my help
Business specPlain-language intent + Given/When/Then acceptance criteria
Visual modelA committed prototype + design intent — the visual law
Domain model & API contractOne source of truth; the API contract is generated from it
ConstitutionA short set of binding rules every build must obey
AGENTS BUILD
Frontend + backendReact/TypeScript UI and API services, end to end
Tests & docs includedAuth, error handling, layering — built in, not bolted on
Gates run by the agentThe agent verifies its own work against your specs
"Nothing is made up"Unclear spec? The agent stops and asks.
Every feature runs the same four stages:specifyplantasksimplement

Governance

Agent speed. Human-defined correctness.

TIER 1 · BUILD-TIME GUARDRAILS
Binding specsAcceptance criteria are requirements; contracts can't be contradicted
Human checkpointsThe run pauses at defined points — no code until you approve the intent
Conformance checksArchitecture and model conformance verified mechanically, every run
Fidelity checksVisual parity to the design; requirement-to-test traceability
TIER 2 · EVERY AGENT PR CLEARS THE SAME BAR AS HUMAN CODE
AI code reviewA second model reviews every pull request
Quality & security scanStatic analysis on every change
License & dependency checkNo surprise licenses in your stack
API security conformanceContract-level security checks

Wired to your stack — these map to whatever scanners and review tools you already run. Nothing ships that your specs and your gates didn’t approve.


Proof

This runs in production today.

Designed and ran this operating model for an engineering platform at a Fortune-100 automotive OEM — two production applications, built by agents, governed by specs.

≈10×
leverage on human effort
28,600
lines of production code
3,000
lines of spec govern it all
300
acceptance criteria traced
0
architecture violations
Application 1 · Web UI
9 business specs → 12 live screens
React / TypeScript · workflow management, authoring, publishing, dashboards — pixel-parity to the committed design.
Application 2 · API services
5 business specs → 19 endpoints
FastAPI / SQLAlchemy · domain-model-driven, generated API contract, full audit trail from intent to test.

Not a pilot. Not a demo. This is how both applications are built and shipped, feature after feature.


The Engagement

Three phases. A real feature ships in the pilot.

01
FOUNDATION
Weeks 1–2
  • ·Constitution written for your product and stack
  • ·Spec templates + worked examples for your team
  • ·Pipeline scaffold in your repo, gates configured
  • ·Pilot feature selected from your real backlog
02
PILOT
Weeks 3–5
  • ·Your first feature: spec → plan → tasks → implement
  • ·Human checkpoints live — you approve intent, not diffs
  • ·PR gates enforced on the agent's pull requests
  • ·Feature lands in production, fully traced
03
SCALE
Week 6+
  • ·Your team trained to author specs independently
  • ·Backlog throughput at machine speed
  • ·Playbook + runbook handed over — documented, versioned
  • ·Optional retainer for reviews and pipeline evolution

Fixed-scope foundation + pilot, then scale on your terms. If the pilot doesn’t ship, you keep every artifact anyway.


Deliverables

What you walk away with

Everything lives in your repos, under your accounts. The system doesn’t depend on me.

Constitution & spec templates
Versioned rules + templates your team authors against from day one
Working spec→code pipeline
The four-stage agent pipeline, running in your CI, on your repos
Quality gates, enforced
Checkpoints and PR gates wired so a red gate blocks a merge
Domain model + API contract
One source of truth; the contract is generated, never hand-drifted
A team that can run it
Your PMs and designers trained to author specs without me
Full traceability
Every shipped line answers: which spec, which criterion, which test

The Ask

The deliverable isn’t code.

It’s a code factory you own.

Six weeks from kickoff, a real feature from your backlog is in production — specified by your team, built by agents, cleared through gates you control. Everything after that is throughput.

Start with a 45-minute working sessionWe pick the pilot feature from your actual backlog
Get a fixed-scope proposal in 48 hoursFoundation + pilot, one price, defined exit criteria
Ship, then decideScale phase only if the pilot earns it
Start the conversationDownload the deck (PDF)