Loading page
Object detection, image classification, OCR, video analysis and visual inspection — built where a camera already exists and a person is currently doing the looking.
Computer vision is worth the money when a human is looking at the same kind of image over and over and making the same small judgement. Reading a meter, checking a label is straight, counting stock on a shelf, matching a delivery photo to an order, pulling the numbers off a scanned invoice.
It is worth nothing when the images are inconsistent in ways nobody controls. Lighting, angle, resolution and background matter more than the model does, and a pilot exists mainly to find out whether yours are stable enough. We would rather run that pilot than sell a system that works in our office and not in your warehouse.
A small, separately quoted proof on your real images, with the failure cases shown before you commit.
Lighting, angle and resolution are reviewed up front — they decide the result more than the model does.
Extracted fields are posted to the ERP or accounting package, with the source image kept against the record.
Uncertain results go to a person rather than being silently accepted.
Where images cannot leave the premises, the model runs on hardware in the building.
Detection quality and review-queue volume are tracked, so degradation is visible.
Inference runs server-side or on-site depending on volume, latency and whether the images can leave the building. The result goes into the system that acts on it — the WMS, the POS, the ERP, the ticket.
Vision projects are quoted in two parts, and the first part is small on purpose. Almost every computer-vision failure we have seen described by a client was a capture problem discovered after the full system was paid for.
We do not supply, install or maintain cameras and we do not build facial recognition or biometric identification systems. We do not promise a detection rate before the pilot has measured one. And every vision system we deliver includes a human review queue for low-confidence cases, because a silently wrong count is worse than no count at all.
An operation where a person looks at the same kind of image again and again and makes the same small judgement: counting stock on a shelf or units on a line, checking that a label is straight, matching a delivery photo to an order, reading a meter or pulling the figures off a scanned invoice.
The requirement is stable capture. If lighting, angle and resolution vary in ways nobody controls, the pilot will say so before a production system is quoted.
Straight answers, including the ones that rule us out.
The pages people read next, and the products that connect to this one.
Image, video and text annotation — bounding boxes, polygons, segmentation, keypoints and classification — plus dataset cleaning, quality control and human-in-the-loop workflows.
Read moreCustom models for forecasting, classification, scoring, recommendation and anomaly detection — trained on your own history, evaluated against held-back data, and deployed where the prediction is acted on.
Read moreModel-serving APIs, AI backends, cloud deployment, monitoring, cost control and maintenance — for models we built and for models you already have that never made it out of a notebook.
Read moreEach of these has its own page on where the money actually leaks in that kind of business, and which systems cover which part of it.
Consignment booking, trip planning, fleet and driver records, fuel and maintenance costing, delivery proof and freight billing.
Read moreBill of materials, production orders, work in progress, wastage and job costing — so the cost per unit is a calculated number, not an estimate.
Read moreDescribe your operation and we will come back with a written scope, a fixed price and a delivery date.