AI that makes
one decision better.
A demo proves a model can produce an answer. A product has to produce it in under two seconds, at a cost that survives ten thousand users, and behave sensibly on the day the model is wrong or the API is down. That distance is the whole job, and it is where most AI features quietly fail. We build for the second case from the start.
What we build
Personalisation with judgment
Software that adapts to the person using it — a plan that changes after a bad week, a feed that reflects what someone actually does rather than what they clicked once.
Assistants inside a workflow
An assistant that can see the user's real context and take real actions, placed where the work happens instead of in a floating window beside it.
Documents and unstructured data
Extraction, classification, and search over contracts, invoices, tickets, and transcripts, with the citation trail a human reviewer needs to trust it.
Retrieval pipelines (RAG)
Grounding answers in your own data, with evaluation to prove that a change to a prompt made things better rather than merely different.
On-device intelligence
Core ML for the cases where the data should never leave the phone — lower latency, no per-call cost, and a privacy story you can put in writing.
What's included
- ✓Model selection driven by cost, latency, and quality — not by brand
- ✓Prompt and pipeline evaluation, so quality is measured rather than guessed
- ✓Fallbacks and graceful degradation when the model or the API fails
- ✓Token and cost monitoring, with a projection before you commit
- ✓Privacy review of exactly what data leaves your systems, and what does not
- ✓A written honest assessment of where AI does not help your product
Questions
Will our data be used to train someone's model?
Not if it is set up correctly. We use enterprise API terms that exclude training on your data, document exactly what is sent, and keep anything sensitive on-device or self-hosted where that is the right answer.
How much does running an AI feature cost?
It depends on the model, the prompt size, and how often it runs — so we project it against your real usage before you commit, and design around caching and smaller models where they perform just as well.
What if AI is not right for our product?
Then we say so, and we would rather say it in the first call than in month four. A good deal of what gets asked for as AI is solved better and cheaper by a query, a rule, or a better interface.
Other services
Start a project
Tell us about the ai work you have in mind. A written scope, a fixed price, and a delivery date follow the first call.
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