AI that truly understands video. Uses multimodal models (Marengo/Pegasus) to search, analyze & generate text from video content at scale.
This is the 6th launch from TwelveLabs. View more
Compliance by TwelveLabs
Launched this week
Compliance by TwelveLabs is a SaaS application that reviews your video library against rules your team writes, not ours. It ingests footage, applies your own compliance rule packs, and returns reviewer-ready findings with context, not just a timestamp and a label. Powered by TwelveLabs' Pegasus model, it explains why a moment may violate a rule so reviewers can accept, reject, or annotate in one queue.
Launch Team
Dial
targeting a 15% reviewer-rejection rate makes sense as a false-positive metric, but for a compliance tool I'd actually worry more about the other direction - the violation it never flags at all, so nobody ever puts eyes on that clip. false positives get caught by a human in the queue eventually. a false negative just quietly never shows up. is there a way to sample the "cleared" footage to catch what Pegasus missed, or does the whole system rely on the rule pack being complete upfront
@galdayan Good catch, and I agree. That’s probably the harder failure mode for a compliance tool.
Today, the coverage is really based on how complete the rule pack is. Pegasus will apply the rules you give it, but if something is missing from the rule pack, it can obviously miss it.
One thing we’re looking at is spot checking some of the content that was cleared, as another QA layer, to help identify gaps or drift in the rule pack itself.
Would be interested to hear what you would want to see from something like that.
Dial
@lior_berezinski_tl random sampling of cleared clips would catch some of it, but I'd weight the sample toward the clips where Pegasus's own confidence was closest to the decision boundary rather than sampling uniformly. a clip it cleared with high confidence is probably fine; a clip that just barely cleared is exactly where a gap in the rule pack would hide. the other thing I'd want is for whatever the spot check finds to feed back into the rule pack automatically as a suggested addition, not just a flagged miss someone has to manually translate into a new rule - otherwise the QA layer catches the same category of gap over and over instead of closing it.
This could be interesting for teams where compliance checks are still mostly manual. I’d love to see how accurate the explanations are on real world footage.
@edward_paul3 Yeah, that’s probably the part we’re most focused on getting right.
Happy to share some real examples. Is there a specific type of content or violation you’d be interested in seeing?
I appreciate the focus on reviewer-ready results rather than just dumping AI detections on the user. The human review part feels well condsidered.