Custom computer vision that detects, tracks, and recognizes objects, people, faces, and license plates in live video — on the edge or in your cloud. Sub-200 ms inference, trained on your classes. First model in 5–7 weeks, from $8K. Our recognition reads 500,000+ vehicles a day in production.
If you have cameras or video streams and need software to understand what is in them — count it, track it, recognize it, flag it — we build the recognition models and the pipeline around them.
A cloud vision API (AWS Rekognition, Google Vision) is quick but generic, priced per image, and your footage leaves your network. An off-the-shelf VMS bundles fixed analytics. A custom model is trained on your classes, runs on the edge, and the cost per camera drops as you scale.
A cloud API is fine to validate an idea. When accuracy on your specific classes, on-device latency, or keeping footage in your network matters, the custom model wins at any number of cameras. Securing a site specifically? See computer vision for video surveillance.
Video recognition is a pipeline: see the frame, find the objects, follow them, recognize them, and decide what to do — fast enough to act on live video. Here is what we build.
RTSP, WebRTC, or file streams come in from your cameras, decoded and frame-sampled for the model.
YOLO26 or RT-DETR finds the objects in each frame — people, vehicles, products, PPE, whatever your classes are.
ByteTrack or DeepSORT keeps a stable ID on each object as it moves, so you can count, dwell-time, and follow it.
Face recognition (ArcFace/InsightFace), ANPR for plates, or a custom classifier identifies the specific entity, not just its category.
Zones, line-crossing, loitering, PPE-missing, count thresholds — the events that matter to your operation, defined by you.
Alerts, a live dashboard, or an API push to your systems, in real time, with the clip attached.
End to end, recognition lands in under 200 ms on an NVIDIA Jetson at the camera, or in your cloud — fast enough to flag a problem while it is still happening.
No single model is best at every task. We assemble the detector, tracker, and recognizer per job, train on your footage, and deploy where the latency has to be.
MindBox runs facial recognition at 99.5%+ with anti-spoofing, and ANPR reading 500,000+ vehicles a day across India at ~95%.
EyeBuild runs 4K recognition distinguishing humans from vehicles on solar-powered edge cameras, with automated alerts.
Live Eye processes 2,000+ interactions a day, cutting shrink up to 30% and drive-offs 40%, across 10,000+ locations.
V.A.L.T spans 2,500+ cameras for 770+ organizations and 50,000+ users — recording, observation, and analysis.
Detect missing hard hats, vests, or people in restricted zones, and alert before an incident.
Count footfall, measure dwell time, and map flow through a space, in real time.
A cloud vision API recognizes generic labels in someone else’s cloud. A custom model recognizes your classes, on your edge, with your footage staying put. Here is the split.
Not sure it will hit your accuracy bar? The free MVP planning below proves it on a sample of your footage first.
Data, training, detection, tracking, recognition, events, deploy. You get the model, the pipeline, and the code.
Drop detection and recognition into your existing video or camera product.
A model that misses, drifts, or runs too slow. We retrain, tune, and optimize for the edge.
Our computer-vision engineers join yours and build alongside you.
Fixed-scope starting points for a video recognition build. Each is a floor you build up from.
Edge hardware (Jetson) and any cloud GPU are billed at cost — no markup from us. We forecast them in the estimate.
Before the build, we will prove the accuracy on your footage and pick the right model and hardware.
Competitor analysis, core feature definition, monetization modeling, and a full launch blueprint — delivered within a week. Written by engineers who'll build what they plan.
An independent review of your system's technology choices, structural components, and workload fit — with a plain verdict on what's working, what's a liability, and exactly what to change to reach your goal. Delivered within a week.
A full audit of your code with every issue documented, evidenced, and located — exact file, exact line. Plus a system architecture review and a prioritized fix roadmap. Not a consultant's opinion. A case file. Delivered within a week.
A specialist review of your video or streaming product covering latency, media server architecture, WebRTC, playback reliability, real-time chat, and scalability. Every finding is specific, located, and fixable. Delivered within a week.
We have shipped computer vision that reads half a million plates a day, recognizes faces at 99.5%, and runs across thousands of cameras — in production, not a demo.
Two decades of real-time video and computer vision.
MindBox reads 500,000+ vehicles a day and recognizes faces at 99.5%+; Live Eye runs across 10,000+ locations; V.A.L.T spans 2,500+ cameras.
YOLO26, RT-DETR, ByteTrack, ArcFace, ANPR — chosen per task, trained on your footage, not a stock label set.
NVIDIA Jetson, Triton, TensorRT, OpenVINO — sub-200 ms inference at the camera, online or off.
Footage and recognition data on your edge or in your cloud, with privacy and consent patterns built in.
The model, the weights, the pipeline, the code. No black box, no per-image tax.
The questions teams ask before they build video recognition. The same answers power this page’s FAQ schema.
What is AI video recognition software?
What can it recognize?
Which models do you use?
How accurate is it?
Can it run on the edge / on-device?
How fast is it?
Does our video have to leave our network?
Can it detect custom events, not just objects?
Can you add recognition to our existing cameras or product?
What does it cost and how long does it take?
Computer vision for video surveillance
See the pageServiceComputer vision development
See the serviceAIAI software development
See the serviceTell us what you need to see — objects, faces, plates, events — and on how many cameras. We will pick the model, prove the accuracy on your footage, and give you a timeline and a number — in one call. Securing a site specifically? See computer vision for video surveillance.
Specialist software house for video, real-time and AI products. Founded 2005. 50 in-house engineers.
