Industrial AI Vision, Reimagined.
Plug in your models. Connect your cameras. Deploy analytics pipelines - from edge to cloud - all in one unified platform.
Turn Visual Data into Production Intelligence
Factories, plants, and inspection lines produce vast amounts of visual data - most of which goes unused. TheYantr Vision converts that stream into actionable intelligence. From detecting micro-defects to monitoring quality trends, it lets engineers design, deploy, and observe AI pipelines without writing code.
An End-to-End Platform
From initial setup to real-time analysis, our platform provides all the tools you need.
Build Once. Deploy Anywhere.
Every component of TheYantr Vision is modular — connect what you have, scale when you need. Bring your own models, define your input streams, and choose your preferred compute.
- Unified Model Adapter
Supports TensorFlow, PyTorch, ONNX and more.
- Flexible Camera Inputs
Connect RTSP, file streams, or industrial cameras.
- GPU / CPU / Hybrid Compute
Deploy on edge GPU, CPU clusters, or the cloud accelerator.
- Zero-Code Configuration
Design and manage pipelines without writing code.
Monitoring & Analytics
Once deployed, every pipeline can be observed in real time. TheYantr Vision surfaces everything — inference latency, throughput, accuracy metrics, and alerting.
- Live Dashboards
Visualize pipeline health with latency and FPS data.
- Event Logs for Trends
Track anomalies and performance over time.
- Seamless Integrations
Connect directly to Grafana, InfluxDB, and more.
AI Vision That Adapts to Any Industry
Automotive
Weld & paint inspection
Pharma
Capsule counting
Textile
Fabric anomaly detection
Food Processing
Contamination detection
Energy
Equipment monitoring
Fits Right Into Your Ecosystem
TheYantr Vision connects seamlessly with your existing analytics and control stack.
Purpose-Built for Industrial Intelligence
Modular by Design
Built for Reliability
Insight-Ready
See. Think. Act.
Experience the future of industrial AI vision—modular, scalable, and built for real-time performance.