Human-interaction evals for frontier AI

Know what humans will do
to your model — before they do.

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.

Behavioral evidence, per checkpoint.

Static benchmarks don’t tell you what happens when real humans show up. Mimese rehearses the human side of deployment — continuously, with every release.

Red-teaming

Adversarial red-teaming at scale

Synthetic adversaries probe your model for jailbreaks, prompt injection, and social-engineering paths — before deployment, and again at every checkpoint.

Behavioral evals

Human-AI interaction evals

Multi-turn interactions with diverse simulated users surface persuasion, sycophancy, deception, and over-trust — measured, not vibes.

Deployment readiness

Pre-deployment safety evidence

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.

Verified against humans — not just plausible.

Everyone else sells you half the problem. Mimese closes the loop with real human ground truth.

The status quo

Generic synthetic users

Plausible dialogue with no error bars. Built for market research — never validated against how real humans actually behave with your model.

The status quo

Task benchmarks

Independent evals now ship inside frontier model cards — proof labs pay for third-party measurement. But they test professional tasks, not human behavior.

The status quo

Crowd labor by the hour

Expert red-teamers rented by the hour. Powerful, but it’s staffing — not a repeatable, versioned eval product.

Mimese is the missing piece: consented human panels, a published fidelity methodology, and private, continuously-refreshed test sets — packaged as red-team-as-a-service, per model, per release.

From open interactions to ranked evidence

One pipeline turns raw human-model interactions into decision-ready signal.

01

Define the humans

Adversary profiles, user populations, deployment contexts. Bring your threat model — or your target user base.

02

We stage the interactions

Your model faces simulated humans in multi-turn scenarios, via API or checkpoint. A working prototype, being hardened with design partners.

03

You get ranked evidence

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.”

Our goal: every simulated behavior pattern validated against real human-model transcripts — prediction vs. reality, tracked over time. We’re building that evidence base with our design partners now.

Onboarding design partners — frontier labs first.

Training or deploying a frontier model and want behavioral evidence before it ships? Let’s talk.