What “AI product build” means
An AI product build is a full-stack engagement where we take responsibility for the entire delivery: concept refinement, architecture, engineering, LLM prompt and eval work, deployment, and handover. You get a live, working product at the end of each sprint — not a prototype, not a wireframe.
This is the right engagement if you have a problem worth productising and you want it in front of users fast. Not if you want six weeks of discovery and a specification document.
How the engagement works
Sprint one: prove the idea (weeks 1–2)
We start with the smallest thing that proves the core idea works. One workflow, one user flow, deployed and accessible. This is deliberately narrow — it lets you validate assumptions with real users before we build more. At the end of week two, you have something to show, not something to review.
Sprint two onward: earn each feature (weeks 3–8)
Each subsequent sprint adds one feature that earns its keep — something that directly improves retention, conversion, or core workflow efficiency. We don’t add features because they were on the original list; we add them because the previous sprint told us they matter.
Typical sprint cadence: daily written updates, weekly live demo, one call per week if you want it. We tell you when something isn’t working before you have to ask.
What you get at the end
- A live product — deployed, accessible to real users, accepting payments if relevant.
- Source code — clean repo with README and architecture notes. You own it.
- Deployed infrastructure — Docker-based, on your hosting or ours until you’re ready.
- Runbooks — how to deploy updates, roll back, monitor costs, and handle common failure modes.
- Eval suite — a set of representative test inputs so you can catch LLM regressions before they hit users.
The stack we build on
We build on the same stack we run for our own 35-product portfolio. No surprises, because we’ve already hit the edge cases:
- LLMs: Anthropic Claude (default) routed via LiteLLM, with fallback to Gemini or OpenAI where the task warrants it.
- Backend: Python FastAPI or Node.js Fastify, depending on the integration surface.
- Frontend: Next.js (web), React Native + Expo (mobile).
- Data: PostgreSQL + Redis.
- Billing: Stripe — subscriptions, usage-based, or one-time. Wired in from day one.
- Deployment: Docker on your cloud provider, or self-hosted if data sensitivity demands it.
What this engagement is not
It’s not a retainer. It’s not open-ended. Each sprint has a fixed scope and a fixed price. If the project outgrows the original framing, we rescope with you — we don’t quietly add hours.
It’s also not a “build to spec” relationship. We push back when the spec is wrong, suggest alternatives when we find them, and tell you when a feature isn’t worth building before you pay for it.
Frequently asked questions
How long does a product build take?
First working demo in 1–2 weeks. First paying customer in 4–8 weeks for most scopes. Larger products with multiple user roles or integrations take 10–14 weeks.
What do I need to bring to get started?
A clear problem and a sense of who the customer is. Two paragraphs is enough. We don’t need a spec, a design, or a technical background from you.
Do I own the code?
Yes. You own the source code, the infrastructure config, and the deployed product. Full handover at the end of each sprint.
Can you build mobile apps?
Yes — React Native + Expo for cross-platform iOS and Android. We’ve shipped several (FocusGuard, FakeCall, ReceiptSnap AI). Mobile adds 1–2 weeks to initial scope.
What if the first sprint isn’t what I expected?
We scope sprint one to be small enough that it’s low risk. If the demo isn’t right, we discuss it honestly before committing to sprint two. You don’t owe us a second sprint.