AurvikAI

Computer Vision Development

Vision systems that see what your team can't — at the speed your line demands.

From quality inspection to medical imaging, we build the eyes your operations need.

Image recognition, object detection, video analysis, and quality inspection systems. Shipped across manufacturing, healthcare imaging, retail, and security — with real-time edge inference and human review for edge cases.

10K+units inspected per hour
99.2%classification accuracy
< 50msedge inference latency

Designed for your constraints

Edge, cloud, or hybrid — built for your latency and cost reality.

Real-time constraints are solved at the architecture stage, not after the model is trained. We design the inference pipeline around your latency requirements, camera infrastructure, and compute budget — whether that's sub-50ms edge inference on an NVIDIA Jetson or batch processing in the cloud.

A vision model that works in controlled conditions and fails in the field is not production-ready.

< 50ms

edge inference latency

3

deployment architectures supported

Edge computing device running computer vision inference on a manufacturing production line

Production-grade vision

Every system ships with human review for edge cases.

Edge cases are not an edge case in production — they are a constant. Every computer vision system we build includes a defined confidence threshold below which detections are routed to human review, and a feedback loop that improves the model from reviewer corrections.

99.2%

classification accuracy across production deployments

AurvikAI manufacturing client data

01

Confidence thresholds

Defined precision/recall targets agreed before training — with human review routing for low-confidence detections.

02

Synthetic augmentation

Data augmentation pipelines for rare defects and edge cases that don't appear frequently enough in real-world data.

03

Active learning

Human reviewer corrections automatically fed back into the training pipeline for continuous model improvement.

04

Model versioning

A/B testing of model versions in production with automated rollback on performance regression.

Computer vision applications we build

Different industries, different constraints, same engineering rigour.

Automated quality inspection at production line speed.

Defect detectionCore

Real-time identification of surface defects, dimensional errors, and assembly faults across high-speed production lines.

Multi-camera inspectionScale

Coordinated multi-angle vision systems capturing defects invisible from any single viewpoint.

Traceability integrationEnterprise

Linking defect detections to batch numbers, machine IDs, and operator data for root cause analysis.

Manual inspection vs. AurvikAI vision system

The difference between human fatigue and machine consistency is measured in defects caught and throughput maintained.

Manual quality inspection

  • Human inspectors miss 15–25% of defects due to fatigue
  • Inspection speed bottlenecks production throughput
  • Inconsistent standards across shifts and inspectors
  • No data trail for root cause analysis
  • Scaling requires hiring and training more inspectors

AurvikAI vision system

  • 99.2% detection accuracy that doesn't degrade with time
  • 10,000+ units per hour at full production speed
  • Consistent standards 24/7 across all production lines
  • Complete data trail linking defects to batches and machines
  • Scaling requires adding cameras, not headcount
Computer vision system detecting defects on a manufacturing production line with real-time bounding box overlays

AurvikAI vision system in production — real-time defect detection at full line speed.

Common questions about computer vision

From data requirements to deployment constraints.

Depends on the detection task. Simple binary classification (defect/no-defect) can work with 1,000–5,000 labelled images. Multi-class detection with rare categories typically needs 10,000–50,000+. We assess data sufficiency early and build synthetic augmentation for gaps.

Proof of concept

See results on your data in 4 weeks.

We run a focused proof of concept on your actual images, your actual defects, and your actual production constraints. If the model doesn't beat your current process, you'll know before committing to a full deployment.

Ready to build computer vision for your operations?

Let's start with your images, your constraints, and your detection requirements.