A model that hits 94% accuracy in the lab and 61% on the actual production line hasn't failed at vision — it failed at deployment conditions. We build and validate against the environment the model will actually run in, not a clean benchmark set.
Serving UK and EU clients: GDPR, EU AI Act, data residency, and VPC / on-premise deployment options are covered on our Trust & Safety page.
Computer vision and edge AI deployment means training a model on your specific domain — your products, your defects, your camera angles — and running it where it needs to run: on edge hardware, embedded systems, or cloud inference, without the latency of a round-trip to a server for every frame.
The hard part isn't training a model that performs well on a validation set. It's keeping that performance once the model meets real lighting, real vibration, and real edge cases the training data never saw.
Every vision system we ship is built and checked against the conditions it will actually run in — not just a clean test set.
Trained on images that look like your deployment environment — your lighting, your angles, your defect types — not a generic public dataset.
Deployed to the hardware the use case actually needs — edge devices, embedded systems, or cloud — so inference happens at the speed the production line runs, not the speed of a network round-trip.
Tested against real field conditions before go-live, so the accuracy number you see in testing is the accuracy number you get in production. See our Trust & Safety framework.
The questions we get asked most about computer vision and edge deployment — answered straight, no sales pitch.
Lab accuracy is usually measured on clean, well-lit, curated images. The production line has glare, vibration, dust, inconsistent lighting, and edge cases the training set never saw. The fix is training on data that actually looks like the deployment environment and validating against it before rollout — not chasing a higher benchmark number on a clean dataset.
It depends on how much labeled domain-specific data exists already, the target hardware, and the accuracy bar the use case demands. We price by milestone, not by the hour, so the number is fixed before work starts. Book a call and we'll scope it in detail.
Yes, when the use case calls for it — we deploy to edge hardware and embedded systems that run inference locally, with no dependency on a cloud round-trip or network connection. That's standard for production-line and latency-sensitive deployments.
A model with usable labeled data and a defined target device can typically go from training to field validation in a matter of weeks. Timelines extend when data collection or hardware procurement is the bottleneck, which we flag during scoping rather than discovering mid-project.
Book a 30-minute call — we'll tell you honestly whether computer vision is the right fit for your use case, and what it would take to make it hold up in the field.