Video analytics
for business:
catching what people miss

Usually not. Most existing systems are good enough, and we tell you after looking at the feed. Sometimes an angle has to change or lighting has to be added for a specific scenario, and that is cheaper than replacing the hardware.

It depends on the scenario and the quality of the feed, so we quote accuracy after training on your own footage rather than promising a number upfront. Where a miss is expensive we tune the thresholds towards catching more and let a person filter the false positives.

It does not have to. Counting and compliance scenarios work on anonymous detections, without identifying anyone, which keeps them clear of most privacy constraints. If your scenario genuinely needs identification, that is a separate design decision with its own legal review.

On your servers, at the edge next to the cameras, or in the cloud. Video is heavy, so for large installations processing close to the source is usually both cheaper and faster than shipping every frame elsewhere.

Yes, though it usually costs more: a self-hosted model needs a GPU server, rented or bought. So while the task involves no sensitive data, most teams start on cloud LLMs. When the data is sensitive, we deploy an open-source model inside your perimeter: the model, the knowledge base and the logs never leave the company, which is what GDPR and internal security reviews ask for. We do it end to end, including the servers, the deployment and the access rules.

Business processes change and the solution grows with them: we refresh the knowledge base as your policies and products change, extend the scenarios to neighbouring tasks, and move to stronger models as they ship. You can run this yourself or hand it to us as support.