Artificial vision Enhanced intelligence

Video Recognition software

Recogneyes is an innovative Video Recognition software able to detect scenes in a digital video, extract information such as objects, people, people emotions, places, environments etc…
Thanks to artificial intelligence the algorithm can be trained to find what you need. It allows to easily retrieve all the extrapolated information through familiar natural language searches.

Recogneyes works upon media AI technologies to make it easier to extract insights from videos. Power new forms of content discovery such as searching for objects, faces, characters, and emotions.

Computer vision is transforming businesses every day, it has been in broad use for decades, however, when it comes to their application to a real business and production cases, a distinctive approach is needed. Depending on your business needs and niche, it can bring you the following advantages: enhance the consumer experience, reduce costs, time-saving, better quality control, big data analytics and easy reporting.


Insights extracted from the video can be used to enhance the search experience across a video library. For example, indexing faces can enable the search experience of finding moments in a video where two people were seen together. Search based on such insights from videos is applicable to news agencies, educational institutes, broadcasters, entertainment content owners and in general to any industry that has a video library that users need to search against.


Industries that rely on ad revenue (for example, news media, social media, etc.), can deliver more relevant ads by using the extracted insights as additional signals to the ad server (presenting a sports shoe ad is more relevant in the middle of a football match vs. a swimming competition).

Content creation

Insights extracted from videos and help effectively create content such as trailers, social media content, news clips etc. from existing content in the organization archive.

User engagement

Improves user engagement by positioning the relevant video moments to users. As an example, consider an educational video that explains spheres for the first 30 minutes and pyramids in the next 30 minutes. A student reading about pyramids would benefit more if the video is positioned starting from the 30-minute marker.