Computer vision systems for object detection, image classification, OCR, quality inspection, and video analysis — built with the right balance of accuracy, latency, and deployment footprint for your use case.
Computer vision delivered on its decades of promise around 2018, and the toolkit has only matured since. We build vision systems that work in production — handling real lighting conditions, real camera variation, and real edge cases that demos politely skip.
Discuss your project ↗Every engagement gets shaped to fit, but these are the building blocks we rely on.
Real-time detection for retail, manufacturing, and logistics — counting, tracking, and locating objects in images and video streams.
Custom classifiers trained on your domain — defect detection, content categorization, document type identification — with calibrated confidence scores.
Text extraction from scanned documents, invoices, IDs, and forms. Multilingual OCR with structured output suitable for downstream automation.
Industrial vision for assembly line inspection — surface defects, missing components, dimensional checks. Faster and more consistent than manual QC.
People counting, dwell time analysis, queue monitoring, and behavioral patterns from CCTV or IP camera feeds. Privacy-respecting by design.
On-device vision for low-latency, privacy-sensitive applications. ONNX, TensorRT, and Core ML deployments tuned for the target hardware.
Two decades of engineering practice, sharpened by the realities of production AI.
We tell you what's achievable on your data before training starts. No promised 99% on a problem that's structurally 87%.
Latency, privacy, and cost trade-offs decided up front. We deploy where the problem actually lives.
We help with annotation tooling, quality review, and class balancing. Models are only as good as the data behind them.
Drift detection, accuracy tracking, and re-training triggers. Vision models degrade quietly without proper observability.
Let's discuss how this fits your business. We reply within one working day.
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