Computer Vision
Eyes on your operation. Wired to your systems.
Detection, counting, and inspection on real-world images and video — useful because the results land in the software that runs your operation, not in a dashboard nobody opens.
What we build
Detection & counting
People, products, vehicles, assets — from cameras you already have where feasible.
Visual quality control
Defect and anomaly detection integrated into the production or intake flow, with review queues for edge cases.
Document & image understanding
When the input is photos of the real world: receipts, meters, shelves, sites.
Edge or cloud
Deployment decided by your latency, bandwidth, and privacy constraints, not by fashion.
When teams come to us
- Manual counting or inspection that doesn't scale
- Incidents you only discover after the fact
- A camera investment that never became data
How it starts
The Discovery Sprint validates feasibility on your actual footage before anyone commits to a build.
How we build it
Feasibility on your footage
We test detection quality on your actual cameras and conditions before anyone commits. The Discovery Sprint can cover this step.
Metrics agreed first
Precision and recall targets defined with you, on your cases. "It works" gets a number before we build.
Pilot on one line or site
The smallest real deployment that proves value in production conditions.
Scale and operate
Rollout with monitoring, review queues for edge cases, and a retraining loop that keeps accuracy honest.
What breaks — so we plan for it
Placement
most accuracy problems blamed on the model are camera problems: angle, height, lighting. We look at the footage before we promise anything.
Occlusion
people behind people, products behind hands. Recall drops exactly where you most want it; the design assumes it instead of discovering it.
Drift
a season changes the light, a wall gets painted, a camera gets bumped. Accuracy is checked against reality on a calendar, not assumed from launch day.
False positives
the plant that counts as a person until the threshold moves. Thresholds are tuned per site, with the people who receive the alerts.
What never gets recorded
Counting people is not identifying them. The systems we build detect a person, not which person: no faces, no re-identification, no stored video unless you decide otherwise — counts, timestamps, and events, with retention and access decided before the first camera is connected. And when a camera watches a workplace, telling the crew what is detected, what isn't, and what is stored is part of the design, not a memo afterwards.
Works well with
FAQ
Do we need special cameras?
Usually not. We start from the cameras you already have — and tell you honestly if they're not enough before you spend anything.
What accuracy can we expect?
It depends on your footage, lighting, and cases — which is exactly why feasibility runs on your real data before any commitment, and targets are agreed as numbers, not adjectives.
Cloud or on-site processing?
Decided by your latency, bandwidth, and privacy constraints — both are on the table, and the answer is an engineering trade-off, not a default.