Vision systems that ship to the line, the floor, or the cloud.
Detection, segmentation, OCR, and tracking trained on your imagery, then deployed to the hardware that fits.
- Pilot to production
- 0-10wk
- Detection precision
- >0%
- Edge inference
- <0ms
- Less labeled data with active learning
- 0x
Vision tuned to the conditions you actually operate in.
Off-the-shelf vision works on the wrong distribution. We collect, label, and fine-tune on your imagery: factories, retail shelves, drones, medical scans, vehicles. Then we deploy where it runs fastest: edge, cloud, or both.
- Detection, segmentation, OCR, pose estimation, and event detection
- Active-learning loops to cut labeling cost
- Deployed to Jetson, Coral, mobile NPUs, or your cloud
From discovery to production.
- 01
Discover
Walk the workflow, audit the imagery, define accuracy and latency targets, and pick the deployment topology.
- 02
Prototype with evals
Build a labeled holdout. Models pass when they meet precision and recall on your task, not COCO.
- 03
Deploy
To edge devices, your cloud, or both. We handle hardware integration, OTA updates, and monitoring.
- 04
Operate
Active learning on uncertain frames, retraining triggers, and drift detection on production imagery.
OpenCV pipeline hit a precision wall?
Book a 30-min consultWhat you get.
Real environments, real failure modes, real evals.
We test against your actual conditions: low light, motion blur, occlusion, weather. Models go through structured failure-mode analysis before shipping. Drift on production frames triggers retraining automatically.
- Failure-mode-aware test suites, not just held-out IoU
- Quantisation and distillation for cost-effective edge inference
- Drift detection on production frames, automatic retraining triggers
Common questions.
Move vision off the prototype shelf.
Free 30-minute consultation. Bring a use case; we'll size it.
Schedule consultation