AI product engineering
We build language-model features into products that already have users — retrieval over your own data, agents that take real actions, extraction and classification pipelines.
Book a technical callOverview
The interesting part of an AI feature is not the prompt. It is everything around it.
Evaluation suites, so you can tell whether a change actually helped rather than guessing from three examples. Fallbacks for when the model is slow, wrong, or down. Cost ceilings enforced in code rather than discovered on an invoice. Observability that shows you what users really asked for, not what you assumed they would.
We build that scaffolding on the first pass. Retrofitting it after launch is how AI features quietly get switched off six weeks later — and it is the single most common reason a promising demo never becomes a product.
What we build
- Retrieval over your own documents, databases and internal APIs
- Agentic workflows with tool use and human approval steps
- Structured extraction and classification pipelines over messy input
- Evaluation harnesses and regression suites that gate model changes
- Streaming interfaces in React 19, with honest loading and error states
- Prompt and model version control, with a rollback that takes seconds
- Token budgets, rate limits and caching, enforced server-side
- Provider abstraction, so changing model is a config change and not a rewrite
What we commit to
Written into the engagement, not implied.
- Streaming from the first sprint
- Sprint 1
- Evaluation suite must pass at release
- Gated
- Cost ceiling agreed in writing
- In writing
- Deterministic fallback when the model is down
- Fallback
Three phases, and you can stop after the first.
The assessment is priced and delivered on its own. If the answer is that you should not build this, you have paid for one phase and saved the other two.
Assess
We take the workflow apart and find where a model earns its cost and where it does not. You get a written scope, an evaluation plan and a cost model — yours to keep, and legible to someone who has to approve the budget.
Build
Two-week increments against the evaluation suite agreed in phase one. Every release either moves the score or explains why it did not. The scaffolding ships with the feature, not after it.
Operate or hand over
We keep running it under an agreed service level, or we hand it over with runbooks, dashboards and a trained team. Both are real options — we do not make the handover deliberately painful.
A good half of the AI work we are asked to quote should be a database query, a rules engine, or a better form.
We will say so in the assessment, at the same price. It costs us a build and saves you a system you would have had to keep feeding.
- A chatbot over content nobody reads
- Summarising records a person already skims in four seconds
- Natural-language search across six well-labelled filters
- Anything where a wrong answer is expensive and unreviewed
About this service.
Questions about this service, kept separate from the ones answered on the home page.
Not necessarily. We scope the data boundary first — what leaves your systems, what stays, and what can run on models hosted inside the EU or on your own infrastructure. That decision is made before any code is written, not after.
The evaluation suite tells you which. Model upgrades run against it before they reach users, and a regression means the old version stays. This is the main reason we insist on building it in phase two rather than later.
Yes. Most engagements run alongside an existing team rather than replacing one — we fit into your repository, review process and release cadence instead of asking you to adopt ours.
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