Services
Computer vision for real cameras and real constraints.
Compact ONNX models, scene-specific evaluation, and deployment paths for ops, safety, and traffic environments.
Evidence note: Vision proof is first-party: public HF Spaces and Work items labeled Internal Product. Metrics are internally measured — see linked model notes.
Problems we address
Cloud-only inference
Latency, bandwidth, or privacy rules block streaming every frame to the cloud.
Generic models in specific scenes
Off-the-shelf detectors fail on dense crowds, highways, or outdoor fire/smoke.
No regression discipline
Accuracy claims without held-out scene tests.
Capabilities
Edge detection systems
Person, vehicle, and fire/smoke detection patterns based on our public Sentinel, Vantage, and Ember lines.
Dataset & eval design
Scene splits, labeling guidance, and acceptance thresholds.
Deployment packaging
ONNX export, browser demos, and integration into existing camera workflows.
How engagements typically run
Scene & constraint review
Cameras, lighting, latency, and false-positive cost.
Baseline & data
Measure current performance; define holdout scenes.
Model adaptation
Tune, distill, or train against your conditions.
Integrate & monitor
Ship with a review path for misses and drift.
Related proof & next steps
Related services
Questions
No. Published metrics are from Innomium evaluation protocols on defined splits. Your deployment needs its own acceptance tests.
Talk with us about computer vision.
Share constraints and goals — we will respond with a technical discovery path.