A correction layer for AI emotional reasoning
Models talk to people in pain every day. This dataset teaches them when they're getting it wrong.
We tested frontier models across dozens of emotionally complex cases. They identified the right mechanism every time. Then delivered the intervention in a way that would cause harm — every time.
Every AI company trains for engagement. We train for the moment engagement becomes harmful — and teach your model what to do instead.
The difference between a response that sounds good and one that actually helps is where user-loyalty is earned. The Correction Layer bridges that gap by optimizing exclusively for the user’s benefit.
Each row encodes the psychological mechanism a model would miss, the intervention it should select instead, and how to deliver that intervention so the person remains open to receiving it.
1500+ row structured dataset, 16 failure modes where models cause harm, immediate impact. Plug it into any fine-tuning pipeline to instantly improve your model.
When a user in pain gets met with the wrong response, they’re gone forever. The only way to help is to connect, and that means getting it right the first moment it matters.
Sourced from authentic interactions in a mental health education community. Validated by a licensed therapist with 20+ years of experience. The only dataset that encodes clinical reasoning at the delivery level.
No scraped data, no crowd-sourced labels, highest ethical consideration.
Current training data teaches models what pain sounds like. Ours teaches them what it needs.