AI and platforms
Models trained on your data, systems designed for the field.
The difference between an AI demonstration and a system you can operate is measured in the field. A plate recognition model trained on generic data collapses when it meets Tunisian plates, a badly angled camera, rain, or low sun behind the vehicle.
Our approach is empirical. We start from your actual video streams, build an annotated dataset from your operating conditions, and train a dedicated model.
Example: Bizerte Economic Activity Park
On the Bizerte economic activity park project, this took recognition accuracy from roughly 50–65% with a generic model to approximately 95% with a model trained specifically on Tunisian plates, from 3,200 images.
Operations engineering
Automatic number plate recognition (ANPR / LPR)
Model training on annotated local data
Object detection and tracking (YOLO)
OCR and document data extraction
Real-time processing of multiple RTSP streams
Sizing and installation of dedicated GPU workstations
Integration of models into an existing product
Projects delivered in this field.
Economic activity park
PAEB — Parc d'Activités Économiques de Bizerte
A plate recognition system driving three barriers from six video streams, with a model trained specifically on Tunisian plates.
Logistics
It depends on how variable the conditions are. For the Tunisian plate model on the Bizerte project, 3,200 annotated images were enough to reach approximately 95% accuracy. A site with more camera angles or harder lighting will need more.
For real-time video, almost always on-premise. Pushing several RTSP streams to the cloud is expensive in bandwidth and adds latency that is incompatible with opening a barrier. We size a dedicated GPU workstation on site and send only the results upstream.
Describe your need: an engineer replies within 48 business hours.
