AI integration.
We put AI to work inside your product or process, in search, drafting, classification or support, and test it against your real data before it goes live. For teams that want a feature that works, not a demo.
01 — Problem and outcome
AI is easy to demo and hard to trust.
A prototype that impresses in a meeting can fail on real customer data. Without testing you get wrong answers, unclear costs and a feature nobody relies on.
AI that does one job well and shows its work.
A feature your team or customers use every day, with accuracy measured on your own examples, costs known in advance, and a handoff to a person when the model is unsure.
02 — The detail
Use-case scoping
We pick the one job where AI pays off and define what good enough means before anything is built.
Data review
A look at the data the model will see: quality, privacy, and what should never leave your systems.
Build and integration
The AI feature built into your existing product or workflow, with the model provider chosen for your accuracy, speed and cost needs. Training new models from scratch is not part of this service.
Testing on real examples
An evaluation set built from your own cases, so accuracy is a number rather than a feeling.
Guardrails and fallback
Limits on what the model can say or do, and a route to a human when it is unsure.
Cost and monitoring
A running-cost estimate up front, and monitoring after launch so quality does not drift unnoticed.
One week to pin down what gets built, what it costs and when it ships. You get it in writing.
Week 1 · fixed price, fixed dateWeekly demos on a real URL, not screenshots. You see progress and can change your mind while it is still cheap.
Weekly demo on a real URLWe launch, watch the first days and hand over documentation so nobody is left guessing.
Launch, monitoring, handover docsStay on a retainer for changes and support, or take the repo and run with it. Your call.
Retainer or repo handoffUsually not. Most business uses work with a pre-trained model plus your documents and examples. If your case needs more, we tell you in scoping.
We choose providers and settings that do not train on your data, and we write down exactly where your data goes before building.
Sometimes it will. That is why we measure accuracy on your own examples, limit what the feature can do, and hand off to a person when it is unsure.
Fixed price. In the first week we scope the work, write down what is included and what it costs, and agree a launch date. If the scope changes later, we price the change before doing it, not after.
The finished product live, the code repository and handover documentation so another developer could pick it up. Stay on a retainer for changes, or take it in-house. Your call.
03 — Example build
An AI chatbot built into a live agency website.
Talk through your version.
Tell us what you have and what you want it to do. On a first call we ask questions, tell you honestly whether we are the right fit, and outline a scope.