Services
An AI feature is a product problem wearing a model's clothes. What happens when it is wrong, what it costs per user, and who can safely change it next year are the questions that decide whether it ships — so we answer those first.
The prototype convinced everyone. These four questions had not been asked yet.
The interface assumes the output is correct, so the first confident mistake reaches the user with no way to flag it, correct it, or fall back.
Inference spend scales with usage, and the pricing page went live before anyone did that arithmetic at realistic volume.
Exploratory code quietly becomes the shipping code, and within months nobody will touch it without expecting something to break.
With no evaluation set there is no way to say whether a change improved the feature or silently degraded it — only that it still runs.
Product engineering discipline applied to a component that is only probably right.
Confidence shown where it matters, correction a click away, and a defined fallback for when the model has nothing useful to offer.
Cost per user projected at volumes you actually expect, so the commercial model survives the feature succeeding.
Typed, tested, reviewed, in your repository and your release process. No notebook left on the critical path.
A scored evaluation set gates each release, and the same measures keep running afterwards so regressions surface on a chart, not in a ticket.
Two representative slices of a typical build.
New capability designed into the interface you already ship, with confidence, correction, and fallback treated as part of the UX rather than bolted on after.
A scored evaluation set every change must clear, staged rollout with rollback, and dashboards on quality, latency, and cost once it is live.
From concept to a launched product someone can operate.
New capability inside the product you already ship.
Interfaces that survive swapping the model underneath.
Versioned releases for product features, with a rollback that works.
Shared infrastructure once several features need the same thing.
Notebooks and AI-generated code made safe to change and ship.
A scored set that defines good enough, and enforces it.
Data boundaries and tenant reach designed before code.
The awkward questions first, the interesting part second.
What the feature is for, and what the product does on the day the model is confidently wrong. The second answer shapes the design more than the first.
Inference cost per user at realistic volume, before anything is promised to a pricing page or a board.
Agreeing what good enough means comes first, so every later change can be measured instead of argued about.
Rollout behind a flag with rollback ready, then quality, latency, and cost tracked continuously with a retraining path your team owns.
Directional outcomes from comparable builds — not contractual targets.
Code your team owns
Tested code in your repo and stack, with a runbook that matches it.
A unit cost you knew first
Inference cost per user modelled before pricing was committed.
Releases you can measure
An evaluation gate that says whether a change helped or hurt.
Whichever the problem justifies. Most products ship sooner and perform better on a hosted model with good retrieval around it. A bespoke model earns its cost only when you hold proprietary data and have hit a real accuracy ceiling, and we will say so when you have not.
Yes, and most of our work is exactly that — adding AI to a codebase that already has users. We work to your stack, your review process, and your release cadence rather than importing ours.
A scored evaluation set, agreed before the build, defines good enough and gates every release. The same measures keep running after launch alongside latency and cost, because an AI feature nobody is measuring degrades quietly and the first report comes from a customer.
It depends on how much of the product the feature touches and how ready the data is. What we will not do is quote before understanding the failure case, because designing for wrong answers is usually the part that moves the estimate.
You do — the repository, the model, the evaluation set, and a runbook, plus the handover to whoever will maintain it. If a feature can only be changed by the people who built it, we have handed you a dependency rather than a product.
Often, and it is increasingly what people ask for. A working demo assembled quickly is a real asset — it proves the idea. Making it safe to change means tests, types, boundaries, and an evaluation set, which is usually faster than starting again.
Data boundaries are designed before code: what leaves your environment, what is retained, and what one tenant can ever reach. Where the honest answer has to be that nothing leaves, we run open-weight models inside your own infrastructure.
Bring the user problem. We will give you an honest read on feasibility, cost per user, and effort.