What this makes possible
We build modular vision pipelines that decouple video ingestion (RTSP/WebRTC), accelerated frame decoding, neural inference (YOLOv10, RT-DETR, custom transformers), spatial tracking (ByteTrack, BoT-SORT), and downstream analytics into high-performance C++ and Python microservices.
Where we focus
- Real-time object detection & classification
- Multi-object tracking (MOT) & trajectory modeling
- Dense video analytics & event recognition
- Action classification & temporal sequence modeling
- Human pose estimation & biomechanical tracking
- Multi-camera calibration & 3D homography
- Automated visual inspection & defect localization
- Edge vision pipelines (NVIDIA Jetson / TensorRT)
How we build it
Multi-Camera Fusion
Synchronize multiple camera angles with automatic homography calibration to generate unified 3D spatial coordinate feeds.
Edge-Optimized Inference
Quantize models (INT8/FP16) for real-time execution on NVIDIA Jetson, Intel OpenVINO, or industrial IPCs without cloud round-trips.
Event & Action Detection
Temporal deep learning models that recognize domain-specific events, safety violations, athletic maneuvers, and workflow anomalies.
Synthetic Data & Active Learning
Systematic data engines that identify edge cases in production, queue them for labeling, and trigger automated re-training cycles.