Compliance by TwelveLabs

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

Interesting use of AI and this could save reviewers a ton of time. Congrats team!

How easy is it to adapt compliance rules for different countries when every region has its own standards?

Congrats @simon_lecointe & team!

The different market angle is pretty interesting. I can see that being useful for larger content teams.

The reviewer queue makes a lot sense. Nobody wants to rewatch 20 mins just to verify one flag.

How does it handle rules that depend more on context than what's actually visible?

humbled by this new collaboration with @c3lim and the @TwelveLabs team.Fun fact: this is their 6th launch on @Product Hunt, their 5th in the last 10 months. Behind the scenes, there's a formula we started shaping when we launched Marengo 3.0 and Pegasus 1.5, and it's all about consistency.Learn more in this thread in /p/twelvelabs.Launch, and keep launching!

I appreciate the focus on reviewer-ready results rather than just dumping AI detections on the user. The human review part feels well condsidered.

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.

I’m Simon, Head of Field Engineering at TwelveLabs, part of the team behind Compliance by TwelveLabs.The problem we kept coming back to: if reviewers have to rewatch the entire video to validate AI flags, what have we actually saved them?Compliance review is still largely manual, and every market brings different rules. Detecting “violence” isn’t enough. Reviewers need to understand what happened, in what context, and why it matters under the policy they’re applying.That’s what we built this around:Context reviewers can act on. Pegasus explains findings instead of just labeling them.Rules compliance teams own. Adapt regional packs, edit rules, tune thresholds, and publish versions without waiting on us.Synthetic media detection built in. NVIDIA’s Synthetic Video Detector adds frame-level scoring alongside our contextual analysis.Findings ready for review, with signed reports and API access.We’re targeting a reviewer-rejection rate of 15% or less. The goal is straightforward: less time chasing false positives, more time on decisions that need human judgment.What’s an edge case you’d want to put this through? A scene that’s acceptable in one market but restricted in another? Something AI consistently flags incorrectly? I’d love to hear it and learn where we still have work to do!