Every citizen is a persona you can talk to — grounded in real Census data, with a personality and memory. Tune every trait, assemble an audience for the exact job you're testing, and report on the outcomes that matter to you. Not the 20 personas you'd write by hand — thousands, and you choose who.
A persona here isn't a paragraph you wrote. It's a synthetic person with Census-grounded demographics, Big Five (OCEAN) personality, a daily life and memory across sessions. Talk to one, or run your agent against thousands at once — the skeptic, the rambler, the anxious caller, all in character.
An audience here isn't just a demographic skew — it's built for a job-to-be-done. Loan applicants, parking-permit renewals, first-time buyers: describe the scenario and the audience assembles from citizens whose lives actually fit it. Need a market shape instead? Tilt age, income or language and the population re-weights while staying Census-honest.
Add attributes and behaviors we don't ship out of the box — a chronic condition, low digital literacy, a distrust of AI, a tendency to skip follow-ups. Build the difficult, high-stakes, easy-to-miss people your agent has to get right, layered on top of the population you already have.
+ chronic condition+ insurance plan+ digital literacydistrusts AIcode-switchesskips follow-upsyour region · ACS+ your aggregate statsShare the shape of your audience — segment sizes and key distributions, aggregate stats, never individual records — and we synthesize a population that mirrors it. No PII ever changes hands, and the result stays fully synthetic, just like everything else.
The triad finishes with reporting: your rubric, your cohorts, your dashboards. Score with the built-in model-graded rubric or bring your own evals, break results down by the segments you care about, and export everything — transcripts over MCP or REST, runs as OpenTelemetry traces — into the stack your team already works in.
Start from a Census-grounded city and shape it down to the people who matter for your agent.
Pick a city and population size — a Census-grounded starting point.
Describe the job-to-be-done, or skew age, income and language to match your market.
Layer on the attributes and behaviors your use case needs.
Set your agent loose and read results by persona and cohort.
Grounded in US Census data (ACS) & OpenStreetMap. Synthetic throughout — no real individuals, no personal data.
No. Every persona is fully synthetic, generated from public, aggregate Census data. There are no real individuals, no personal data, and nothing that maps back to a real person.
A persona is one synthetic person — demographics, personality and memory. An audience is a segment of many personas, assembled for a purpose: a job-to-be-done like applying for a loan, or a market shape like a senior-heavy city. You can talk to a single persona or run your agent against a whole audience at once.
An audience assembled around a scenario rather than a demographic slider: describe the job-to-be-done — disputing a fee, renewing a permit, comparing plans — and the audience draws citizens whose circumstances actually fit that job, while staying demographically honest.
Hand-written personas only cover the handful of people you already thought of. These are thousands, demographically grounded, and shaped to your market — so you also find the cohorts you'd never have scripted.
Yes. Add custom attributes and behaviors on top of the Census base yourself. To match your real users, share aggregate stats — segment sizes and key distributions — and we synthesize a population that mirrors them. We work from aggregates, never individual records, so no PII changes hands and the output stays fully synthetic.
Yes. The population and setup are seed-deterministic, so the same audience comes back every run. See Reproducibility for what is and isn't deterministic.
Shape your audience, add the personas you need, and set your agent loose.