Video compliance review powered by rules you control
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.
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!
About Compliance by TwelveLabs on Product Hunt
“Video compliance review powered by rules you control”
Compliance by TwelveLabs launched on Product Hunt on September 4th, 2026 and earned 252 upvotes and 18 comments, earning #3 Product of the Day. 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.
On the analytics side, Compliance by TwelveLabs competes within SaaS, Artificial Intelligence and Video — topics that collectively have 523.8k followers on Product Hunt. The dashboard above tracks how Compliance by TwelveLabs performed against the three products that launched closest to it on the same day.
Who hunted Compliance by TwelveLabs?
Compliance by TwelveLabs was hunted by fmerian. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Reviews
Compliance by TwelveLabs has received 1 review on Product Hunt with an average rating of 5.00/5. Read all reviews on Product Hunt.
For a complete overview of Compliance by TwelveLabs including community comment highlights and product details, visit the product overview.
fmerian
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!