Real-time video AI
Real-time video AI pipelines that hold up in production
A model that runs on a laptop is a demo. A model that decodes dozens of camera streams, infers in milliseconds, and keeps doing it for months is a system. We design and build the second kind — end-to-end video AI pipelines that ingest live cameras, run GPU inference at scale, and stay observable and reliable long after launch.
- multi-camera ingestion
- RTSP processing
- NVIDIA Triton
- DeepStream
- TensorRT / CUDA
- GPU inference
- object tracking
- edge & cloud
- Dockerized deploy
- drift monitoring
Multi-camera ingestion and RTSP stream processing
The pipeline starts at the source: multi-camera ingestion from RTSP, ONVIF and IP feeds, with hardware-accelerated decode so the GPU spends its cycles on inference, not on unpacking H.264. We handle the unglamorous realities that break naive pipelines — reconnects on dropped streams, clock skew across cameras, variable frame rates, and backpressure when a downstream stage falls behind — so the feed stays clean before a single frame reaches a model.
GPU-optimized inference: Triton, DeepStream, TensorRT, CUDA
Throughput lives and dies in the inference stage. We serve models on NVIDIA Triton and build streaming graphs on DeepStream, with models compiled to TensorRT and custom CUDA where a kernel is the bottleneck. Batching across cameras, mixed precision, and pinned-memory transfers let us push real-time object detection and tracking across many concurrent camera streams on a single GPU — instead of dedicating one card per feed.
Scaling, latency and edge-vs-cloud inference
Every deployment has a latency budget, and it decides the architecture. We size the pipeline to it: edge inference on-site when the round trip to the cloud is too slow or the network too fragile, cloud or on-prem servers when models are heavy and cameras are dense. We profile the full path — decode, preprocess, infer, track, post-process — and cut the tail latency that turns a real-time system into a laggy one, so it scales with cameras added rather than falling over.
Dockerized deployment, on-prem or cloud
The pipeline ships as versioned, Dockerized services with GPU runtime configured, so what we validated is exactly what runs — no bespoke server that only one person understands. We deploy on-prem behind your firewall when data cannot leave the site, or into your cloud when it can, and wire in the dashboards operators actually watch: live detections, per-camera health, and alerting.
MLOps, monitoring and model drift
Video pipelines degrade quietly. Lighting shifts with the seasons, cameras are moved, a new object class appears, and accuracy erodes without a single error in the logs. We build MLOps for computer vision around that reality: monitoring on inference health and prediction quality, drift detection that flags when the world has moved away from the training data, and a retraining path that closes the loop. This is work we have built and deployed at an industrial AI lab — client anonymized.