Computer Vision
Vision models tuned to your line, your shelf, your footage.
Custom object detection, inspection, and monitoring models — trained on your own imagery, deployed on-site or in the cloud.
- Target production precision
- 95%+
- On-site, low-latency inference
- Edge-ready
- On your own imagery
- Custom-trained
The problem
Generic vision models miss what matters to you
Off-the-shelf vision APIs recognize cats and cars — not the specific defect on your production line or the exact planogram compliance issue on your shelf.
- Generic models aren't trained on your specific products, defects, or environment
- Public datasets don't reflect your lighting, angles, or edge cases
- No edge deployment strategy means latency makes real-time use impossible
- Models drift over time without a retraining and monitoring plan
The solution
Vision models trained on your reality
We build and train computer vision models on your own imagery and environment, then deploy them where they need to run — cloud or edge.
Trained on your data
Models learn your specific products, defects, and environment.
Deployed where it matters
Edge inference for real-time, on-site decision-making.
Monitored over time
Drift detection and retraining keep accuracy from degrading.
Capabilities
What we build
Vision systems for inspection, monitoring, and counting.
Object detection & classification
Models identifying and categorizing what the camera sees.
Real-time video pipelines
Live inference engineered for production and retail environments.
Custom model training
Trained on your own imagery, not generic public datasets.
System integration
Connected to cameras, PLCs, and existing plant or store systems.
Drift monitoring
Continuous accuracy tracking and retraining pipelines.
Edge deployment
On-premise inference for low-latency, offline-capable operation.
Technology
Technology we use
Production-grade vision infrastructure, not a research notebook.
Modeling
- PyTorch
- YOLO
- OpenCV
Edge hardware
- NVIDIA Jetson
- TensorRT
Deployment
- Edge AI runtimes
Architecture
How a vision pipeline is structured
From camera feed to actionable alert.
01
Capture layer
Cameras and video feeds integrated into the pipeline.
02
Inference layer
Trained models running on edge or cloud infrastructure.
03
Decision layer
Detections translated into alerts, counts, or system triggers.
04
Monitoring layer
Accuracy and drift tracked against ground truth over time.
Use cases
Where we've applied this
Manufacturing
Quality inspection
Automated defect detection on a production line.
Retail
Shelf monitoring
Planogram compliance and stock-level detection.
Industrial
Safety compliance
PPE and safety-zone monitoring on the floor.
Logistics
Automated counting
Visual inventory and foot-traffic counting.
Process
How we build vision systems
- 01
Collect & label
Gather and annotate imagery from your actual environment.
- 02
Train & validate
Build models and test against held-out real-world examples.
- 03
Deploy
Ship to edge or cloud infrastructure matched to latency needs.
- 04
Monitor & retrain
Track drift and retrain as conditions change.
Benefits
What custom-trained vision buys you
Higher accuracy
Models tuned to your specific defects and environment, not generic objects.
Real-time response
Edge deployment enables immediate action, not batch review.
Lower inspection cost
Automated monitoring reduces manual inspection labor.
Consistent quality
Vision doesn't get tired or distracted on shift four.
Keep exploring
Related services
Data Annotation
High-accuracy labeling for computer vision, NLP, and LLM training pipelines at scale.
ExploreCustom Robotics
Purpose-built robotics and automation hardware for manufacturing, logistics, and inspection.
ExploreAI Solutions
End-to-end applied AI strategy, model development, and deployment tailored to your data.
ExploreFAQ
Frequently asked questions
No — our Data Annotation team can build and label the training dataset from your own footage or images as part of the engagement.
Ready to give your cameras a reason to earn their keep?
Tell us what you need detected, counted, or inspected — we'll assess feasibility with your own footage.