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04 · Computer Vision

Eyes that do not blink, on cameras you already own.

Inspection, counting, OCR, and tracking — deployed to the edge of the production line or the fleet, with the labeling loops that keep them accurate as the world changes.

What it is

Production vision, not a Kaggle notebook.

The model is the easy part. The hard part is the lens that fogs over in summer, the new SKU that nobody told the data team about, and the lighting that drifts six lumens by month three. Computer vision in industry is a labeling and feedback problem first, and a deep-learning problem second.

We deliver the full loop: camera selection, capture pipeline, labeling tooling, model training, edge or cloud deployment, and the retraining workflow that lets your team add a new defect class without phoning us. The model is a deliverable. The loop is the asset.

"It catches the failure mode we used to ship to customers. Quarterly recalls went away. That was the line item that mattered."
Capabilities

The vision problems we ship into production.

Each one delivered with a labeling pipeline, an eval set, and a runbook for the day the camera moves.

Quality control & defect detection

Surface scratches, missing components, mis-prints, color drift. Per-station accuracy reports your QC team can actually act on.

Object counting & tracking

Pieces on a conveyor, vehicles through a gate, customers through a doorway. Persistent IDs across frames, even when things overlap.

OCR & document understanding

Printed labels, handwriting, tables, and stamps — extracted into structured fields with the confidence score for downstream routing.

Aerial & satellite imagery

Drone surveys, satellite passes — change detection, asset counts, vegetation encroachment. Stitched, georeferenced, and queryable.

Edge deployment

NVIDIA Jetson, Coral, or industrial PCs on the floor. Inference at the camera, no round-trip to the cloud, telemetry sent up.

Retraining pipelines

Mis-predictions captured, queued, labeled, and folded back into the next model version — automatically, on a schedule.

How we deliver

Collect, label, train, deploy.

It is a loop, not a launch. The model on day one is rarely the model on day ninety.

Phase 1

Collect

Camera placement, lighting, lens, frame-rate. We capture across shifts and seasons so the data covers the cases the model will actually see.

Phase 2

Label

Annotation tooling shaped to your taxonomy, with reviewer workflows and inter-annotator agreement scoring. Labels are the asset.

Phase 3

Train

Architectures matched to the constraint — accuracy budget, latency budget, edge memory. Tracked runs, reproducible, all in code.

Phase 4

Deploy

Hardened image, OTA updates, drift monitoring, and a clear path for your team to retrain on new defects without our help.

Where it earns its keep

Environments we have shipped into.

Manufacturing Lines Warehouse & Logistics Retail Footfall & Loss Prevention Agriculture & Precision Farming Construction Site Safety Utility Inspection Medical Imaging Triage Document & Forms Capture
Outcomes our clients report

Numbers from real production systems.

94%Defect-class recall at the production threshold our QC team agreed to
30 msAverage end-to-end inference latency on the edge devices we deploy
2 wksFrom a new defect appearing to a retrained model running on the line

Got footage and a problem the eye can solve?

Send a few hours of representative video. A free read-out will tell you whether vision is a fit — or whether a cheaper sensor wins.

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