Mimese stages simulated humans against your model before real ones arrive: adversaries hunting for jailbreaks, everyday users exposing persuasion and trust failures, edge cases surfacing who your model fails. Every checkpoint gets a ranked behavioral readout — what emerged, how often, how severe. We’re onboarding design partners to harden the evals.
Static benchmarks don’t tell you what happens when real humans show up. Mimese rehearses the human side of deployment — continuously, with every release.
Synthetic adversaries probe your model for jailbreaks, prompt injection, and social-engineering paths — before deployment, and again at every checkpoint.
Multi-turn interactions with diverse simulated users surface persuasion, sycophancy, deception, and over-trust — measured, not vibes.
A ranked readout of emergent behaviors per release — evidence you can put in front of a deployment decision instead of a post-launch incident report.
One pipeline turns raw human-model interactions into decision-ready signal.
Adversary profiles, user populations, deployment contexts. Bring your threat model — or your target user base.
Your model faces simulated humans in multi-turn scenarios, via API or checkpoint. A working prototype, being hardened with design partners.
Failure modes and behaviors extracted and ranked by severity × prevalence — a behavioral readout for every release.
“We don’t generate more answers.
We measure how much each answer can be trusted.”
Training or deploying a frontier model and want behavioral evidence before it ships? Let’s talk.