Our approach: a small model that measures and verifies on-device

We are building a compact vision model — small enough to run on-device and in real time — that estimates the true measurement of a catch from an image and, in the same pass, looks for the signatures of tampering and fraud: edited pixels, inconsistent scale references, and reused or staged submissions. We are deliberately not publishing the model architecture, the training data, or the detection heuristics, because the value of a cheat detector is precisely that cheaters cannot read the manual. What we will say is that it is the same discipline as the rest of our work: a measured base model, a held-out verification gate, and a bias toward small and local so results are fast and private.

This is one piece of a larger framework we built and operate in production. The full picture — and how it applies to your business — is in the playbook.

We specialize in healthcare because it is the hardest vertical — strict HIPAA regulation, PHI handling, BAA chains, and zero tolerance for failure. If we can build it for healthcare, we can build it for any industry. We work across verticals.