AI Engineering
AI features built into real products, held to the same standard as everything else we ship.
Most AI work fails in the same place: a demo that impresses in a meeting and then falls apart on real data, real volume and real edge cases. We treat an AI feature as a product feature - it gets a written specification, a defined success measure, and a way to tell whether it is actually working once it is live. If a problem is better solved with ordinary code, we say so rather than reaching for a model because it is the thing everyone is asking for.
In practice that covers assistants and chat interfaces grounded in your own documents and data rather than a general model's guesses, pulling structured information out of documents, forms and email, search and recommendations that follow intent rather than matching keywords, and classification and summarisation for work currently done by hand. We integrate established model providers rather than training from scratch, because for almost every business problem that is the faster, cheaper and more maintainable answer.
The engineering around the model decides whether it works: evaluation sets so a change can be proven better rather than assumed better, fallbacks for when a provider is slow or down, cost controls so spend stays predictable as usage grows, and logging that lets you audit what the system actually did. We are explicit about what a model cannot reliably do, and we design the human review step in where being wrong would matter.
Your data stays yours. We are clear from the start about what is sent to which provider, what is retained and what stays inside your own systems, so that is on record before anything is built rather than discovered afterwards.
What this covers
- Assistants and chat interfaces Grounded in your own documents and data, not a general model's guesses.
- Document and form extraction Pulling structured information out of documents, forms and email.
- Search and recommendations Results that follow what someone meant, not just what they typed.
- Evaluation and monitoring A way to tell whether the feature is still working once it is live.
Frequently asked questions
How long does a typical project take?
Most projects run 4 to 16 weeks depending on scope, agreed and written down before work starts so there are no surprises partway through.
What does it cost?
It depends on scope - fixed-price for a defined build, or a retained monthly arrangement for ongoing work. See the pricing section on our services page for the general shape of each option.
Do we own the code and assets at the end?
Yes. Every engagement ends with full source code handover, documentation, and a support window - nothing is held back to keep you dependent on us.
Curious what this looks like in practice? See our projects for real examples, or read more about how we work.
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