Edge cases caught before they ship.
Unit, integration, and E2E coverage from the first PR means the bugs get found in code review — not in a support ticket six weeks after launch. Coverage is tracked in CI and reported per pull request.
How we work · Quality framework
Definition of Done is a
Definition of Done is a
feature, not a checklist.
Quality is what your CFO sees on day one of operating the system — not what your QA lead sees on the day of launch. Here's how we hold the bar.
—— Why this page exists
Quality from commit one. Not launch day.
We treat quality as a feature of every phase — not a gate at the end. Testing, evals, and ownership are wired into the build from the first PR, so by launch day, the system is already operating the way it will on day ninety.
A runbook that makes the next engineer productive.
Deploy steps, rollback procedures, on-call playbooks, and incident runbooks ship with the system — so the next engineer is productive in a day, whether they're yours or someone else's.
AI quality that holds after launch.
Every AI component ships with evals and drift monitoring. When an upstream model updates or your data distribution shifts, the system catches it in hours — not weeks of silent degradation.
—— Code ownership
You own everything. From day one. In writing.
This is the line we don't blur. Every artifact we produce is yours, transferred to your accounts, in formats you can read, with the keys to operate it. There is no "RFS platform" you have to license. If you decide to take it in-house or move to a different vendor, nothing about our commercial relationship makes that decision harder.
Source code in your GitHub org from commit one.
Architecture diagrams in editable format — not flattened PDFs.
Infrastructure-as-code — Terraform, CloudFormation, or equivalent.
Deployment scripts & CI/CD pipelines wired into your repos.
All credentials, secrets, and accounts provisioned in your org.
Prompts & system messages in source control, versioned with the code.
Eval suites for every AI component — you inherit them and extend them.
Runbooks & on-call playbooks for deploy, rollback, and incident.
Architecture decision records — every meaningful trade-off, in writing.
Data models, schemas, and migration history live in your repo.
Brand assets & design system in editable Figma — not PNG exports.
All IP, including derivative works — signed and assigned in the SOW.
—— Testing standards
Tests you can read. Coverage you can defend.
Testing isn't theatre. The standards below are the minimum we ship at — on every production engagement, regardless of size or budget.
Unit tests
Every non-trivial function gets unit coverage. We aim for 80%+ on business-logic paths, with explicit carve-outs for code wired through to a third-party SDK. Coverage is tracked in CI and reported per PR.
Integration tests
API contracts, database queries, and service-to-service calls get integration coverage. These run in CI against real test infrastructure (not mocked). The tests document the contract better than any spec doc.
End-to-end tests
Critical user journeys — login, primary workflow, payment, AI inference — get E2E coverage in a headless browser against a deployed preview. Catches the regressions that unit tests miss.
Security & static analysis
Dependency scanning, SAST on every PR, secrets detection in pre-commit hooks, and a manual security review before launch. For SOC 2 or HIPAA scope, we layer in pen-test prep and audit logging from the start.
—— AI-specific quality
Four extra disciplines on every AI build.
AI components fail differently than regular software. Static tests don't catch model drift. Unit tests don't catch hallucinated answers. We layer four AI-specific disciplines on every engagement that touches a model.
Model evaluations
An eval suite ships with the system. Every prompt, every agent, every model-touching path has a versioned eval set. Regressions show up in CI before they reach prod — and you inherit the suite forever.
Drift detection
Production behavior is monitored against the evals. When the model provider ships an update, when your data distribution shifts, when prompts get edited — we know in hours, not weeks.
Guardrails
Input validation, output filtering, and tool-call boundaries are wired in from day one — not retrofitted after launch. PII handling, prompt-injection defenses, and refusal rates are tested and tuned in eval.
Audit logging
Every model interaction is logged with full context: prompt, model version, tools invoked, output, latency, cost. Searchable, exportable, retention-policied. Compliance teams love this.
—— The handoff
What you walk away with on day one of operating it yourself.
Eight artifacts get delivered into your accounts at launch. None of them depend on us being there to make sense of them. Your next engineer is productive in a day.
Production codeIn your GitHub, running in your cloud, with you as admin.
Architecture docsSystem diagrams, ADRs, data flows, sequence diagrams.
RunbookHow to deploy, roll back, debug, and respond to incidents.
Eval suiteVersioned eval set covering every AI path in the system.
Deployment scriptsIaC, CI/CD, environment configs — everything reproducible.
Test suitesUnit, integration, and E2E running green in CI.
Monitoring & alertingDashboards, alerts, SLOs, and on-call playbooks.
Security packetThreat model, dependency report, secrets handling docs.
—— Common questions
What teams ask first.
Where the quality bar lives in the SOW, what happens to ownership if you change vendors, and how the eval suite holds up after we're gone.
Is the "you own everything" language actually in the contract?
Yes. The IP assignment is a clause in the SOW, not a sales-page promise. It covers source code, prompts, evals, IaC, ADRs, design files, and derivative works. We sign it on every engagement before kickoff — and it survives any change in our commercial relationship.
What if we decide to move to a different vendor mid-engagement?
You already own the work. Code, docs, runbooks, evals — all live in your accounts. We run a structured hand-off: architecture walkthrough with the next vendor or your team, two weeks of overlap, and an open invoice for any post-handoff questions. No vendor-lock language, no platform fees, no penalties.
How does eval coverage hold up after you're no longer on the project?
Every eval lives in your repo, versioned with the prompt or agent it tests. CI runs them on every PR — there's no "Rocket Farm dashboard" you need access to. Your next engineer can add evals the same way they'd add unit tests. The framework is the one your team already knows.
What does test coverage actually mean on an RFS engagement?
Unit + integration + E2E coverage on every production engagement. The unit target is 80%+ on business-logic paths, with carve-outs for third-party SDK glue. Integration tests cover every external contract. E2E covers the critical user journeys. All three run in CI on every PR — and ship with the runbook so your team can keep the bar after launch.
Do you handle SOC 2 / HIPAA / regulated scope?
Yes. For regulated scope we layer in audit logging from day one, pen-test prep before launch, threat modeling per release, and a security packet at handoff. We've shipped HIPAA-aligned and PCI-scope systems. The compliance posture is scoped in the SOW — not bolted on at the end.
What happens if a critical bug ships to production after launch?
Every launch ships with a defined warranty window. Inside the window: regression bugs are on us — no change order, no contract renegotiation. Outside the window: the runbook your team inherited is the same one our on-call team uses. We can stay on retainer if you want belt-and-braces — but the system is fully operable without us.
—— Field notes
What we’re writing about.
Field notes from the studio — what we’re learning about AI products, agent UX, and the messy reality of shipping software in 2026.
