We are hiring across engineering, design and delivery.
See open rolesThe question is never "can AI do this". It is whether it does it better than what you do now, by enough to justify building and running it.
So we measure the current process first. Then we build the smallest thing that beats it, put it where the work actually happens rather than in a separate tool nobody opens, and monitor it afterwards — because accuracy at launch tells you nothing about accuracy in a year.
Your real data, the current baseline, and a straight answer on whether to proceed.
Hosted API, open-weight or classical ML — argued on cost, latency and data sensitivity.
Measured on held-out data, with the failure cases shown rather than averaged away.
Built into the system where the decision is made, with fallbacks when the model is unsure.
Confidence thresholds you set, routing uncertain cases to a person.
Drift detection and periodic re-evaluation, with alerts when quality moves.
If one of these sounds like your situation, it is worth a conversation.
Made hundreds of times a day against fairly consistent criteria.
It demoed well and never reached production. Usually an integration problem, not a model one.
We will help you find the case that pays, or tell you there is not one yet.
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 development is not the answer, we will say so.
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