Service 03
V1 Product Development
Go from zero to a live product in four weeks.
Service 03 · V1 Product Development
We build the AI product as a real product (auth, infra, evals, on-call runbook), so it gets the same engineering rigor as the rest of your stack. The on-call engineer at 3am can read the runbook. The compliance reviewer can read the audit logs. The user doesn’t notice anything weird.
OutcomeProduction AI applications, built end-to-end by the same team: frontend, backend, AI, infrastructure.
Who V1 Product Development is for
03 profiles- 01 / 03
Founders shipping a V1 of an AI-integrated product and need a team that can own frontend, backend, and AI together.
- 02 / 03
Companies extending an existing product with AI features and need engineering parity, not a side project.
- 03 / 03
Healthcare and regulated teams who need HIPAA-grade build practices from day one, not retrofitted.
How V1 Product Development works
04 steps- Step 01
Draw the v1 line
What ships in v1, and what waits
Translate the product requirement into a buildable spec, pick the stack with operating cost and team familiarity in mind, and draw a hard line between what ships in v1 and what waits. The estimate-critical questions (data custody, compliance regime, third-party integrations) get resolved before the build, not during it.
- Step 02
Build in shippable slices
In production by week three or four
End-to-end vertical slices (frontend, backend, AI, infra), so something real is in production by week three or four, not just scaffolding.
- Step 03
Safeguards inside the first release
Auth, evals, pipeline, and the 3am runbook
Auth, observability, evals on AI surfaces, deployment pipeline, and the runbook the on-call engineer will need at 3am. Where the product touches people (clinical content, intake conversations), the guardrails and scope limits are specified before the build and validated with live participants before any real user.
- Step 04
Hand-off or operate
Documentation and pairing, or ongoing build
Either transition to your team with documentation and pairing, or stay on as an extension of the team for ongoing build.
What you get
04 deliverables- D-01
Production application across the full stack: frontend, backend, AI, infra.
- D-02
Deployment pipeline, auth, observability, and runbook.
- D-03
Eval coverage on AI features with regression gates.
- D-04
Hand-off package or ongoing partnership, your choice.
Where we've shipped this
06 engagementsFrequently asked questions
05 questionsHow is AI product engineering different from hiring AI engineers?
Most AI features ship as a side project bolted onto a real product. We build the AI product as a real product, with auth, infra, evals, runbooks, and the same engineering rigor as the rest of your stack. Hiring an AI engineer gets you AI code. Hiring us gets you a shipped, on-call-ready product.
Four weeks to a live product. How?
By deciding what does not ship. One team covers design, engineering, AI and infrastructure, so nothing waits on a hand-off between vendors, and the scope is cut to the slice that has to exist for the product to be used at all. A digital healthcare startup went from a standing start to V1 across web, iOS and iPadOS in four weeks: clinical scribe, voice intake and a HIPAA-compliant telehealth platform. It now handles 500+ consultations a month across 8+ specialties. Larger scopes take longer, and we say so before the contract, not after.
Do you build the frontend too, or only the AI parts?
Frontend, backend, AI, infrastructure, deployment, observability. The whole stack. Most AI work breaks at the seams between the model and the rest of the product, and a team that only builds the AI parts cannot fix those seams. We are full-stack on purpose, not because we ran out of specialists.
Who owns the code at the end of the engagement?
You. Source code, infrastructure-as-code, deployment pipelines, and runbooks live in your accounts and repos from day one. There is no proprietary platform you have to keep us around to maintain. Handoff is a defined window, typically 30 days of post-handoff support, so stabilization is normal work, not a cliff.
Can you work with our existing engineering team, or do you take it over?
We work alongside the existing team in almost every engagement. The build is shared work, with code reviews going both directions. Your engineers learn the AI-specific patterns by reading and reviewing real production code, not from a workshop. By the end, your team can run the system without us.
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