We are hiring across engineering, design and delivery.
See open rolesComputer vision has moved from research to routine for a specific class of problem: consistent lighting, a defined field of view, and a decision a person currently makes by looking.
It remains unreliable outside those conditions, and we would rather say so in the first meeting than in the third month. Where it fits, the return is unusually clear — inspection that never gets tired, counting that never loses its place.
On your images, in your conditions. Sample data from a supplier proves nothing.
Building the training set, including the defects you rarely see and most need caught.
Detection, classification or segmentation, evaluated on held-out examples.
On-device where latency or connectivity demands it, cloud where it does not.
Uncertain cases to a person, with the image and the reason for doubt.
Sampled against human judgement, so drift is caught before it costs anything.
If one of these sounds like your situation, it is worth a conversation.
People inspecting items on a line, where consistency drops across a shift.
Stock, footfall or dimensions estimated manually and recorded approximately.
Forms and IDs arriving as phone pictures that somebody types up.
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 computer vision is not the answer, we will say so.
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