Seenit - Using machine learning for fine-tuned video search in the cloud

Built from the ground up with Couchbase Server and Mobile, Seenit gives enterprises a highly innovative and collaborative video platform that’s easy to use. After crowdsourcing video content from employees, fans, and customers, companies can quickly sort through thousands of videos to find the perfect clips. The powerful combination of Couchbase’s N1QL and Full-Text Search on top of machine learning in the cloud allows users to filter by visual objects in the video, words or phrases in the audio, sentiment, and other key attributes.

Couchbase

About Seenit

Challenges

    • From vast catalogs of videos, find short clips that meet specific requirements
    • Evaluate, store, and search complex properties of video content
    • Scale to accommodate a quickly growing business, including new features, large files, and massive amounts of data

Outcomes

    • Full-Text Search allows sophisticated search on any combination of visual objects, words, and sentiments
    • Video tags stored as JSON objects are searchable using wildcarding, fuzzy search, and Boolean search
    • Couchbase scaled effortlessly, and a new search feature implementation was shortened from 12 weeks to 1 week
We already extensively use N1QL for our structured querying, but now with full-text search integrated into Couchbase, we can seamlessly search all this data and derive relevance-based intelligence using a single data platform.

Dave Starling CTO, Seenit

Maccabai logo Use case

  • Recommendation engine
  • User profile
  • Media catalog
Start building exceptional customer experience today.