We simulate people
so you don't test on real ones.
Before a drug reaches patients, it's tested on a model. Before a car hits the road, it hits a dummy. Chatbots, ads, and AI products still go straight to real people. We build the dummies — and the crash-test rigs.
Personas you can query,
not paragraphs pasted into
a system prompt.
Every persona is a testable hypothesis — trait profiles validated through cross-model pipelines, not demographic cutouts. We maintain a library spanning cognitive styles, personality structures, cultural contexts, and vulnerability profiles.
Quality depends on signal-bearing features, not descriptor volume. Adding more persona details does not improve output coherence. We select the traits that matter and validate them through independent generate, simulate, and evaluate pipelines.
AI systems that do the
thinking, not the chatting.
Agents are complete analytical systems. They orchestrate entire research workflows end-to-end: assemble a persona panel, run structured interviews, collect responses, then hand the raw material to a separate expert agent that extracts patterns, flags contradictions, and delivers a structured insight report.
You can reach agents through a web interface or a dedicated email address. Send an email, get a report. Paste a draft, get a critique. No login, no API key, no onboarding deck.
We crash-test your AI
before your users do.
You've built a chatbot, a companion, a customer-facing AI system. How do you know it behaves correctly under pressure? Not from your QA team's polite test prompts — from sustained, psychologically realistic interactions with users who push, escalate, misunderstand, and need help.
We run two auditing systems: BSB, our proprietary behavioural stress-testing battery, and Reverse Audiences, where we send our personas to talk to your system and deliver annotated transcripts and risk reports.
Built on experimental psychology,
not prompt engineering.
We don't generate personas by writing longer system prompts. We separate creation, simulation, and evaluation across independent models — eliminating preference leakage. We validate against national survey data. We run hundreds of iterations per condition and analyze distributions, not cherry-picked outputs.
Our finding: newer, more capable models are often more sensitive to minor perturbations. A name change shifts scores. A formatting tweak alters reasoning. Single-shot evaluation hides this. We don't do single-shot evaluation.
Built for teams that need
more than a demo.
Tell us what you're building.
We'll tell you what breaks.
Raw personas via API, an async agent for a specific workflow, or a behavioural audit of your production AI — start here. No discovery decks. No six-week procurement waltz.
hello@impersonato.com ↗