“clinically proven to cure colds”
AI compliance checker
Paste your ad text and get two reads on it. Compliance shows what could get it pulled, with the exact rule that says so. The Virality Index scores whether anyone would pass it on.
Reviewing a whole creative? Open PolicyLens · Review queue · How it works
Scroll for what sits behind it ↓
“clinically proven to cure colds”
“biodegradable where facilities exist”
Not SOC 2. Not an EU AI Act inventory. Not cookie consent. Those stay your other stack. We sit on the creative.
Low-risk licensed copy clears. Absolute packs fail. Borderline goes to a person — with the graph attached.
The matcher does not change. Paste a creative, record a decision, or pipe the trafficking system — same packs, same graph, same verdict contract.
Copy or image in. Full path runs Gemini VLM into the lexical packs. Quick path is a single advisory pass for demos.
Queue of absolute fails and borderline assets. A recorded human decision is authoritative — and becomes labeled data for the next pack revision.
POST an asset, poll the verdict, pull the graph. Same deterministic audit fields whether the UI shows an LLM summary or not.
Other tools ask a model if an ad “feels” risky. We require a pattern to match, then a qualifier can clear it. Gemini may paint the display verdict. It does not decide the hit.
Second read · same paste
One paste, two independent reads. The compliance verdict stays deterministic and cited. The Virality Index is an advisory creative score on seven dimensions taken from the sharing research — scored separately, never able to move a compliance outcome.
After Berger & Milkman (2012) on arousal, and Berger’s STEPPS constructs.
Clarity is comprehension hygiene, not a sharing driver — so it’s reported as a diagnostic and flagged when it’s low enough to cap everything else.
Brand prominence measurably reduces sharing, so rewarding it would be backwards. It’s surfaced as a warning instead of being quietly averaged in.
All seven dimensions carry the same weight. With no outcome data of your own, invented
coefficients are false precision — and equal weights are hard to beat with guessed ones.
The payload says uniform_prior so you always know which it is.
Fit them on your own ad performance later, and the score becomes yours.
Advisory by design. It is not a share prediction, it never edits the compliance verdict, and when a virality tactic would cost you compliance it says so. Paste something and see both reads ↑
| LLM-only review | Under Intelligence | |
|---|---|---|
| Same ad, twice | May flip | Same text → same pack hits |
| Who decides the hit | The model, or an agent | A pack pattern. The model does not decide the hit |
| Why it failed | A paragraph of model prose | Clause + verbatim quote + trigger span |
| Lawful disclosure | Often still flagged | Qualifier gate licenses the hit |
| Images | One opaque vision call | VLM packet into the same matcher |
| Human in the loop | Chat thread | ReviewLens decision is the record |
No onboarding project. PolicyLens is the product — paste copy, get a cited hit. ReviewLens is where a person records the rest.