AI Product Building
An AI product taken from blank page to live users, including all the parts that are not the model and turn out to be most of the work.
Zero to launched, with real users.
Design, build, and deploy an AI product end to end. Prototype in weeks, production infrastructure behind it, and a launch that holds up under load.
You probably need this if
You have a prototype that impresses in a demo and falls over at ten users.
The idea is clear and there is nobody free to build it.
The model works. Auth, billing, rate limits and support do not exist yet.
You need to be live before a funding round, or before a season.
What the engagement includes
Scope and design
What it does, who for, and deliberately what it does not do. It is cheaper to argue with a document than with a build.
The build
Front end, back end, the model layer and the infrastructure under it, shipped in weeks rather than quarters.
Production hardening
Rate limits, retries, streaming, caching, observability, and the cost controls that stop a viral week becoming a bill you cannot pay.
Launch
Analytics, onboarding, a support path, and somebody on hand for the first fortnight when it matters most.
What you end up holding
- The product, live, on your own accounts
- Source, documented, in your repository
- A runbook for the things that break at 2am
- Handover to your team, or continued operation by ours
Asked often enough to answer here
- How fast is “weeks”?
- A focused first version is usually four to eight, depending on integrations. We tell you which at scoping, and we would rather cut features than the date.
- Who owns the code?
- You do, from the first commit, in your own repository.
- What if it changes direction halfway?
- Most do. We build in slices, so a change of direction does not throw away the part that already works.
Tell us what the work looks like now.
A short conversation is usually enough to say whether this is a week of work or a quarter, and what it would cost.