Custom AI Applications

Full-stack AI products, built to be handed over.

End-to-end AI products — frontend, backend, model integration, deployment — for teams that need a working application rather than a proof of concept. Built on Next.js and FastAPI, deployed with documentation, and handed over with the source.

Typical timeline: 6–14 weeks for a first version, depending on scope

When this is the right call

Problems this solves

In the words people actually use when they get in touch, rather than in the words the technology uses.

  • You have a validated idea and no engineering capacity to build it
  • A prototype exists and cannot survive real users
  • An internal tool is three spreadsheets and a person who understands them
  • You need AI inside an existing product, not beside it

Deliverables

What you get

  • A deployed application with a real URL and real users
  • Source code, in your repository
  • Deployment and operations documentation
  • Authentication, data handling and rate limiting appropriate to the use case
  • A cost model for running it

Approach

How it is built

  • Next.js and TypeScript on the front, FastAPI and Python on the back
  • Postgres by default; a vector index only where retrieval genuinely needs one
  • Docker, with deployment reproducible from the repository
  • Cost and rate controls before launch, not after the first bill

Process

How it runs

  1. 1

    Scope and cut

    What version one must do, and what it deliberately will not.

  2. 2

    Architecture

    Written down, with the trade-offs stated, before code is written.

  3. 3

    Build in slices

    Something usable early, reviewed as it goes.

  4. 4

    Harden

    Auth, limits, error states, the paths that only break in production.

  5. 5

    Deploy and hand over

    Live, documented, and yours.

What it costs

Scope and price agreed on a short call — no obligation, and you get a written figure before anything starts.

Get a figure

What I need from you

  • A decision-maker available weekly
  • Clear scope for version one, and the discipline to defend it
  • Accounts for the services it depends on
  • Someone who will own it after handover

Not included

  • Open-ended scope — the fixed cut is what keeps this deliverable
  • Native mobile applications or app-store publishing
  • Ongoing feature development, unless we agree a retainer
  • Hosting, model and third-party costs, which stay on your accounts

Custom AI Applications — questions people ask

Do we own the code?

Yes. It lives in your repository from early on, not at the end.

What if we need changes after launch?

The handover documentation is written so another developer can pick it up. If you would rather I continued, that is a retainer we agree separately.

Why fixed scope?

Because open-ended AI projects are how six-week builds become six-month ones. Cutting version one deliberately is the single biggest predictor of whether it ships.

Can you work with our existing codebase?

Often, yes. I will read it first and tell you honestly whether extending it or building alongside it is the better call.

What stack will you use?

Next.js, TypeScript, FastAPI, Postgres and Docker unless there is a specific reason to differ — which there sometimes is, and I will say so.

How do we control model costs?

Caching, rate limits, model selection per task, and a cost dashboard. The estimate comes before the build, not after.

Start here

Tell me what you are trying to do

You get a written figure and a scope before anything begins. If this is not the right service for the problem, I will say which one is — or that none of them are.

Roughly what you have already, what a good outcome looks like, and any budget or deadline you are working to — that is enough for a first reply with a real figure in it.