We deploy computer vision on production lines for defect detection, sorting, and measurement, with edge inference tuned to your takt time.
Line vision systems must keep up with the belt, not a lab demo. Codoop designs camera placement, lighting, and models that classify defects and dimensional issues at production speed.
Inference runs on edge devices such as NVIDIA Jetson where latency and offline resilience matter. Results stream to plant dashboards and MES hooks so operators act immediately.
Retraining pipelines ingest newly labeled failures so the model adapts when products or materials change. Safety and PPE checks can share the same camera infrastructure where appropriate.
You own the models, inference services, and integration code. The line stays yours, vendors do not meter every inspected part forever.
Frequently Asked Questions
Can you work with our existing cameras?
Often yes. We assess resolution, shutter, and lighting first. Sometimes a lens or lighting change outperforms buying an entirely new camera set. Codoop delivers this as production software you own, in 14-day sprints, with staging demos and a full source handover.
What accuracy should we expect?
We establish baselines on your defect taxonomy and report precision/recall honestly. Targets are set with quality and production stakeholders before go-live. Codoop delivers this as production software you own, in 14-day sprints, with staging demos and a full source handover.
Does inference require cloud connectivity?
No. Edge inference keeps decisions on the floor. Cloud is optional for training, fleet management, and aggregated analytics. Codoop delivers this as production software you own, in 14-day sprints, with staging demos and a full source handover. Work starts from a written architecture note and finishes with repositories, runbooks, and full client ownership of the code.