Detection and tracking
Build object detection, multi-object tracking, re-identification, event logic, and review workflows for real environments.
✓Computer vision development
Computer vision development for real-world operations.
The hard part is not recognizing an object in a sample image. It is making the system reliable across light, motion, camera position, hardware, connectivity, and the operating conditions where it will be used.
POCevidence-bounded validation
Edgelocal and embedded inference
Reviewableoperator-facing outputs
What we deliver
Built around the use case, not the buzzword.
Sketric builds computer vision for detection, OCR, tracking, augmented reality, video analytics, mobile devices, and low-latency edge deployment.
OCR and document vision
Recognize plates, documents, labels, and structured visual information with validation and downstream actions around the output.
✓Edge and mobile inference
Move inference closer to the camera when latency, privacy, bandwidth, resilience, or offline operation matters.
✓Evaluation and deployment
Test against representative footage, failure modes, lighting, motion, hardware limits, and operating thresholds before rollout.
✓Where this creates leverage
A strong fit when the difficult part is the whole system.
We scope delivery around the operating reality, the people responsible for it, and the evidence needed to make the next decision.
The camera is part of an operational workflow
Detections must trigger alerts, review, evidence capture, search, analytics, or another action—not simply produce a bounding box.
Cloud-only inference is too slow or fragile
Mobile, embedded, on-premise, or hybrid deployment can preserve responsiveness, privacy, and resilience.
Field conditions are more difficult than the benchmark
We evaluate against motion blur, glare, low light, occlusion, camera variation, and the false-positive cost in the real process.
Evidence in production
Proof from products and client systems.
Owned applications and selected client work show how AI decisions behave after launch.
Edge AI POC
PlateSniper
A tested license-plate detection, tracking, OCR, structured-event, and operator-review proof of concept.
↗ Assisted monitoringAquatic safety prototype
A self-hosted camera workflow with temporal tracking, evidence capture, dashboard alerts, Wear OS alerts, and human verification.
↗Questions teams ask
Useful answers before the first call.
Direct answers for teams deciding whether an AI system is worth building.
Can computer vision run without the cloud?+
Yes. Depending on the use case, inference can run on mobile devices, edge hardware, or on-premise systems with selective cloud coordination.
What data do we need to start?+
A representative sample of the visual conditions and target events is the best starting point. We can then define labeling, baseline, model, and evaluation requirements.
Do you build the application around the model?+
Yes. The camera workflow, alerts, review UI, data storage, search, evidence, and operational handoff are part of the product.
Can you optimize a vision model for mobile or edge hardware?+
Yes. The approach can include model selection, conversion, quantization, hardware acceleration, frame scheduling, and a hybrid architecture where appropriate.
How is computer vision accuracy measured?+
Model metrics are only a starting point. We define operational thresholds for representative conditions, false positives, missed events, latency, and the human review process.
Have a project in mind?
Bring us the workflow, constraint, or ambitious idea.
We will help identify the narrowest useful starting point and the evidence needed to move forward.
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