// ENGINEERING STACK
Built for the frame. Tuned for the millisecond.
YOLO-family detection, person & vehicle re-identification and vision-language models — optimized through ONNX Runtime + TensorRT on GPU-accelerated PyTorch, deployable to cloud, on-premise or NVIDIA Jetson-class edge devices.
THE PERCEPTION PIPELINE
Frame in.
Action out.
Five connected stages turn video into something your operation can use.
Frames ready for inference
Connect RTSP and IP camera streams to a consistent video pipeline.
// MODEL STACK
The stack.
Detection
- ▸YOLO-family object detection
- ▸Configurable zones & lanes
- ▸Multi-class — vehicle / motorcycle / person
Re-Identification
- ▸Person re-ID
- ▸Vehicle re-ID across cameras
- ▸Persistent track IDs — one log per person, not per frame
Language & Vision
- ▸Vision-language models (FastVLM)
- ▸Natural-language querying of live scenes
- ▸Ships in SeeAnything Pro
Serving
- ▸ONNX Runtime
- ▸TensorRT-optimized inference
- ▸GPU-accelerated PyTorch inference
- ▸NVIDIA Jetson-class edge deployment
- ▸Multi-camera geometry & stitching — AVM 4× fisheye
// PROOF IN PRODUCT
Real footage. Real inference.
Multi-class tracking — line-crossing counters
Vehicle / motorcycle / person tracks with live crossing tallies. Hover to play.
Night footage restoration
Drag the divider — SOURCE vs ENHANCED, restored at the edge in real time.
// DEPLOYMENT MATRIX
Your infrastructure. Your rules.
// ETHICAL AI
Privacy-first. Responsible by design.
Face data is handled with enrolled-gallery discipline — matched only against galleries you approve. Every sighting is logged with confidence scores and full audit trails. Unknowns are flagged, never silently ignored. And every deployment follows local regulation.