Test the moments where pressure, coercion, and disclosure are easy to miss. Get one repeatable score grounded in real situations, then use it to improve every release.
Built for the people who carry this risk: trust and safety, compliance and legal, policy and regulatory affairs, responsible AI and model evaluation, and the product leaders who have to sign off on a release.
The Consent Ambiguity Score is the diagnostic, not the destination. It shows you the gap in a number your leadership can act on — the eval suite, the response API, and the improvement loop are how you close it.
Run your product through clinically reviewed scenarios rooted in what young people actually disclose.
Measure recognition, clarity, response safety, and escalation — not whether the answer merely sounds polished.
Use one shared score to catch regressions and decide whether a model or product change is ready to ship.
Benchmark your product across 130+ clinically reviewed scenarios, calibrated on thousands of real disclosures — not synthetic prompts. Get a Consent Ambiguity Score that shows whether your AI recognizes pressure, responds clearly, and avoids compounding harm.
When a user describes coercion, abuse, or a confusing sexual situation, your product calls Override and returns a response built on our full safety stack. The benchmark shows where you fail; this is how you improve.
Every model update re-runs the suite automatically. The loop catches regressions, proposes fixes, and verifies them — so the score becomes a release standard, not a one-time report.
When a user discloses something serious, Override gets it to the right person at your company, with a record. The serious stuff never disappears into a chat log again.
Open: eval scenarios and the public scorecard
Paid: the detector, response API, and the improvement loop
Raw partner conversations and word-for-word user submissions never enter our corpus. With a partner’s explicit agreement, patterns from that traffic can be de-identified and generalized into new test scenarios.
Content sent through the detection and response API is not added to our corpus or used to train the engine.
Runtime traffic is processed to return a response and retained only as long as needed to operate and debug the service, then deleted.
If agreed in advance, we can turn de-identified patterns into generalized, clinically reviewed test prompts — never copied conversations.
The same engine, deployed three different ways depending on how much a partner wants to own.
Our own apps answer the question in the moment. The same engine is white-labelled for nonprofits, schools, and youth-serving organizations that want it under their own name.
Youth-serving nonprofits, schools, prevention coalitions
When a companion or social product detects a signal of coercion or sexual harm, it calls Override and gets back a safe response in its own voice.
AI companion and character platforms, chat and dating products
Large platforms keep their own response engine and use our evals, scoring, and continuous submissions to make sure it holds up.
Major social, gaming, and dating platforms with in-house trust and safety teams
A general-purpose model sees this topic occasionally. We see it continuously — every session in our free apps adds to a corpus no proprietary engine can assemble on the side.
Every Override product runs on these same six layers.
The self-improving loop isn’t a diagram. Here is one full turn of it, from real sessions.
Thousands of real sessions, plus user studies across hundreds of boys and men.
42% changed how they read the situation — but 11% moved in the wrong direction.
The failures clustered where the engine hedged instead of naming what had happened.
It now checks for crisis first, calls a crime a crime, and refuses to stay neutral about coercion.
That correction is the product a company is buying. Most safety systems never find out which of their responses made things worse.
Override Labs is an Illinois 501(c)(3) nonprofit.