Turning video streams into actionable statistics, in real time

Context

Our client develops online software (SaaS). Its ambition: let users obtain useful statistics about what their cameras capture, with no human intervention.

For confidentiality reasons, this client remains anonymous.

The challenge

Video is rich but raw data: dozens of frames per second, with no structure. Turning it into numbers means automatically spotting what matters, frame after frame, then converting those observations into data the software can display.

And it has to happen in real time: a statistic that arrives too late is worthless to the user.

Our approach

We treated this as a data flow problem before treating it as an AI problem. The best detection model is useless if information does not travel reliably between the camera, the analysis and the product.

What we built

The data foundation

We built the video data processing pipeline: it receives raw streams, prepares them and turns them into structured information the application can use.

The main challenge was robustness: a processing chain that moves data reliably between video analysis and the different parts of the product.

The AI layer

We designed the computer vision layer that analyses the streams and identifies the elements needed to compute statistics. It relies on YOLOv11, an object detection model known for its speed.

Everything runs on AWS cloud infrastructure, which supports processing and integration with the software.

Results

The platform automatically turns raw video into actionable statistics, in real time.

For users, the complexity disappears: they read numbers, without having to watch or analyse footage themselves.