We are hiring across engineering, design and delivery.
See open rolesAdoption is decided by location. A summarisation tool in a separate portal gets used for a fortnight; the same capability inside the helpdesk, on the ticket, gets used every day.
We integrate AI where the work already happens — and handle the parts that pilots skip: what happens when the provider is down, how spend is capped, what is logged for review, and how a person overrides an output without leaving the screen.
Where in the existing process the capability belongs, and who acts on it.
OpenAI, Anthropic, Google or self-hosted, chosen on latency, cost and data sensitivity.
Inside your CRM, helpdesk or product — not a separate tool with its own login.
What happens when the provider is slow, down or refuses. Designed, not discovered.
Caching, budgets and per-user limits, so a loop cannot produce a five-figure bill.
What was asked, what came back, and who accepted it — for review and for compliance.
If one of these sounds like your situation, it is worth a conversation.
Staff copying text into a chatbot and pasting the answer back. It works and it does not scale.
Customers expect summarisation or drafting, and you need it built properly.
Sensitive material with rules about where it may be processed. There are options.
The difference between a model that helps and one that quietly misleads.
We measure how well the current process does first. Surprisingly often it is the thing to beat, and sometimes it wins.
Held-out, time-split where time matters. An accuracy figure from the training set is not a result.
Confident cases go through; uncertain ones go to a person. That boundary is set by you, not by us.
Data drifts, behaviour changes, accuracy decays. You get the alerting, not just the model.
Machine learning, generative AI, automation and analytics — applied to problems where the numbers justify it, and measured after launch.
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Deliberately front-loaded with the question of whether to proceed at all.
We look at your actual data, establish the baseline, and give you a straight answer on whether this is worth building. Some of these end in "do not".
A working model measured against the baseline on held-out data, with the error cases in front of you rather than averaged away.
Integration into the system where the decision is actually made, with the human-review boundary, logging and rollback in place.
Drift detection, periodic re-evaluation, and retraining when the numbers say so rather than on a calendar.
Tell us what you are trying to do. If AI integration is not the answer, we will say so.
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