About Us

What we do

Veenome identifies the “stuff” — objects, brands, even faces — inside the actual video and translates it into machine-readable data. We leverage existing computer vision libraries to process video and make intelligent statements about its content. Our core product is an API whose baseline output is a list of tags at the specific time in which they occur with no work required on your part. The API can be used for:

    • -Automated IAB categorization or re-categorization of videos based on the real visual data
    • -Automated moderation of videos from porn to copyright to “suggestive” spam
    • -Automated tags and SEO keywords based on the real visual data
    • -Automatic identification of shoppable products along with clickable code
Contextual information that can be delivered includes:
  •   -Duration of video
  •   -Position of the video on the page
  •   -Media URL
  •   -Video file type
  •   -Autoplay detection
  •   -Video canvas size
  •   and more…

On top of that, we can do many other kinds of analysis, such as indicating how much of any one thing appears throughout your video. And, through our client-facing dashboard, we allow you to custom-tailor API output to your specific needs for each video or group of videos.

What you can do

As a Publisher, Ad Network or Advertiser it is crucial to understand the data within your video. This means unlocking previously hidden value. Among the uses we frequently encounter:

  •   -Auditing large video catalogs for campaign analytics, discovery and SEO
  •   -Using the data to trigger more relevant & valuable pre-roll and display ads
  •   -Leveraging our video overlay to drive ecommerce through clickable objects
  •   -Using the raw data to support business intelligence & brand analytics
  •   -Private licensing of our API for use in legal discovery or secure environments


This all boils down to increased knowledge of what is in video content which leads to increased view counts and higher CPMs which results in increased revenue.

Copyright: Veenome 2012