Most teams test their agent against a handful of personas they wrote themselves. Synthetic Signals gives it a whole city: thousands of distinct people, synthesized from real US Census data, each living a realistic day on real San Francisco streets — a research panel and a test bed in one.
Every citizen is generated from public, aggregate US Census statistics — never from any real person's record. Age, sex, income, household, education and occupation are matched to the city's real demographics, so the population looks like your actual users, not a designer's hunch about who they are.
Citizens move along actual San Francisco streets, routed over OpenStreetMap, and follow a realistic daily rhythm — home, commute, work, errands, social. Your agent meets them in context, at the right moment in their day, not as a frozen profile.
Demographics come from the Census. Personality and behavior are modeled on top: every citizen carries Big Five (OCEAN) traits that shape how they act in a conversation — the skeptic, the rambler, the anxious caller, the one who won't read the instructions. These are the edge cases you'd never think to script by hand.
Generate a fresh population in minutes and point your agent at all of it at once — not twenty hand-picked cases. Runs are deterministic and seeded, so the same city comes back every time — replay the exact setup that produced a failure.
The Census base is a starting point, not a ceiling. Extend the existing citizens with the attributes your use case needs — a chronic condition, an insurance plan, a digital-literacy level — and behaviors like distrusting AI or skipping follow-ups. You layer onto the population you already have; you don't rebuild it.
+ chronic condition+ insurance plan+ digital literacydistrusts AIcode-switchesskips follow-upsyour region · ACS+ your aggregate statsEvery population is synthesized from public, aggregate data — generated, never collected.
The city's real age, income and household shape, from the ACS.
Anonymized household microdata — realistic combinations of traits.
Synthesize a population that matches the marginals, person by person.
Assign homes, jobs and daily schedules on real OSM streets.
Grounded in US Census data (ACS) & OpenStreetMap. Synthetic throughout — no real individuals, no personal data.
No. Every citizen 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.
We fit a Census microdata seed to the city's ACS marginals using iterative proportional fitting, so the synthetic distribution matches the real one — typically within about 2.7% — across age, sex, income, household size and more.
Demographics are Census-grounded. Daily schedules are modeled from typical time-use patterns, and each citizen carries Big Five personality traits that shape how they behave. Personality and behavior are modeled on top of the demographics — they are not drawn from the Census.
San Francisco and Oakland are ready today. Any US city with open Census and OpenStreetMap data can be added.
The default is 1,000 people per city, up to 5,000 today. You can generate a fresh city on demand whenever you need one.
Generate a Census-grounded population in minutes and find the people your agent lets down.