TensorLab Applied Computer Vision

Computer Vision built for production.
Not demos.

TensorLab designs, deploys, and operates production-ready computer vision systems — detection, recognition, and end-to-end ML pipelines built for real-world workloads.

Focus Computer Vision at Scale
Outcomes Reliability • Performance • Cost
Typical work Pipelines • Deployment • Monitoring

Services

Practical, production-focused computer vision work — from feasibility to deployment to long-term reliability.

01 • Validate

Feasibility & Prototyping

Structured validation of your use case: data assessment, baselines, risks, and success metrics.

  • Data + labeling plan
  • Baseline model & metrics
  • Go / no-go recommendation
02 • Build

Production CV Systems

End-to-end pipeline design and implementation: training, inference, deployment, and performance optimization.

  • Training + inference workflows
  • Deployment (GPU/CPU) & optimization
  • Integration with your stack
03 • Operate

Monitoring & Support

Keep systems reliable after launch: monitoring, drift checks, retraining, and incident response.

  • Monitoring + alerting
  • Data drift & model quality checks
  • Planned iterations & fixes
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TensorLab Example engagement

Scaling face detection & recognition to production

Refactored an existing CV system to reduce manual human input and rebuilt it to be GPU-accelerated and reliable under production-scale data volumes.

01 Bulk pipeline + refactor for scale
02 GPU utilization fix (OpenCV → PyTorch)
03 ~2-3× faster analysis in production