Real-time vision and audio intelligence over live streams, at fleet scale. A zero-copy GPU pipeline decodes hundreds of feeds straight into device memory, runs a catalog of detectors on every frame, and turns raw video into a verified, queryable signal — with sub-millisecond validation and an agent layer that reasons and acts on top. Proven on broadcast-grade media: 1,000+ concurrent 4K streams across racks of edge GPUs.
Logos, freezes, macro-blocking, blank and splash screens, lip-sync and on-screen errors are scored per frame — video and audio anomalies caught the instant they appear.
OCR and vision-language models extract guide data, clocks, version strings and error dialogs; object detectors track focus, icons and UI state.
An agent layer plans and acts — driving devices through an IR / Bluetooth control plane and verifying every step against the live stream.
Feeds decode into GPU memory.
A model graph scores every frame.
Validate sub-ms; reason in minutes.
Drive devices, publish, alert.
Telecom and satellite feeds land at the edge, get scored frame by frame, and serve verified media — at fleet scale.
Freezes (consecutive pixel-difference), macro-blocking and pixelation (block-variance + Sobel edge density), tearing and stutter — flagged inside the stream buffer.
An RF-DETR detector with a CLIP refiner confirms logos, app tiles and widgets with bounding-box precision.
GPU OCR (docTR) and vision-language models read guide grids, clocks, version strings and error dialogs — signal-loss, auth and tune failures included.
Audio-presence and lip-sync checks run beside the video probe, so silent feeds and A/V drift are caught too.
GStreamer + DeepStream pull RTSP / H.265 into the GPU via NVDEC; composite grids map to regions once, then a swappable model graph scores each region every frame.
Detectors run as auto-scaling GPU functions (Nuclio) drawn from a continuously trained catalog — new models deploy without touching the pipeline.
Detections stream over NATS JetStream into ClickHouse for sub-second OLAP, with a knowledge graph, vectors and a fine-tuned vision-action model driving next-best-action.
Misses become flagged frames become new annotation tasks — captured, versioned in COCO and retrained, then promoted through a registry.
Turnkey Edge-AI — fixed time, fixed cost, full responsibility.
