Synthetic Individuals
Synthetic Individuals
AI people, calibrated to real-world data, that think, react, and answer like the real humans they represent. Each one holds a stable point of view and a memory of its own, so you can interview it as a user or group thousands into an audience and test any idea in hours.
What a synthetic individual is
A synthetic individual is an AI-generated person, built from real-world data and calibrated against real human distributions, that thinks and responds the way a specific real person would.
It is not a chatbot with a name tag. It is a persistent, model-backed representation of a specific kind of person: their demographics, context, values, and the way they actually make decisions. Ask it a question and it answers the way that person would, not the way a generic assistant would.
This matters because most AI personas are shallow. They are a paragraph of prompt text, so they drift, agree with whatever you say, and collapse toward a bland average. A synthetic individual is built to hold a stable point of view and to reflect the real spread of opinion in a population, which is what makes its answers usable for a real decision.
Grounded in real data, checked against reality
Two things separate a synthetic individual from a prompt. First, anchoring: each one is generated from real demographic and sociological data, so it starts from a real point in the population rather than a stereotype. Second, calibration: iMario corrects its answers toward the distributions real people actually produce.
This is measurable, not a slogan. On the public iMario accuracy benchmark, synthetic audiences match real survey answer distributions to about 89% across 11 populations and more than 900 questions drawn from Pew Global Attitudes, ANES, CGSS, and the Stack Overflow Developer Survey. For reference, rerunning a real survey on a fresh sample of people only agrees with itself about 93% of the time.
- Anchored to real demographic and sociological data
- About 89% match to real survey distributions, verified on named public data
- The human re-survey ceiling is about 93%, so the gap to real fieldwork is small
- Every persona, answer, and line of scoring code is published and reproducible
Different shapes of a synthetic individual
Synthetic audience
Group thousands into a market panel and test concepts, messaging, pricing, and creative across segments in hours.
Learn moreSynthetic users
Stand in for the people who use your product, so you can run deep interviews and validate flows before you build.
Learn moreSynthetic customer
Role-play the buyer you sell to, so your team can rehearse the pitch and its hardest objections before the real call.
Learn moreSynthetic employee
Put an individual to work as synthetic labor, or drop it in as the human layer inside an AI agent.
Learn moreDigital twin
Model one specific real person, and keep a standing stand-in of a stakeholder or key account you can ask anything.
Learn moreSynthetic group
Convene a small room of individuals and watch them react to an idea and build on each other, without the recruiting.
Learn moreHow iMario builds a synthetic individual
Persona Engine
You describe the audience in plain language. A multi-phase pipeline parses it into diversity axes and a sampling matrix, then generates deep nine-chapter profiles so each individual has a coherent identity rather than a one-line prompt.
Real-data anchoring
Each individual is tied to real demographic and sociological data, so the population you generate matches the real composition of the market you are modeling, not a convenient stereotype.
Calibration layer
Raw model answers are biased. The calibration layer corrects responses toward the distributions real people produce on similar questions, which is what moves accuracy from raw-model levels into the range of real research.
Persistent memory
Each individual keeps a memory, so it stays consistent within an interview and across sessions instead of contradicting itself from turn to turn.
Reusable and orchestrable
Once built, the same individuals can be organized into audiences, interviewed one on one, or orchestrated together, so you are not regenerating people for every study.
Synthetic individuals vs the alternatives
| Synthetic individuals (iMario) | Generic AI personas | Human panel | |
|---|---|---|---|
| Data anchor | Real data, calibrated | Prompt-only guess | Real, but slow |
| Speed | Minutes | Minutes | Weeks |
| Cost per study | Low, repeatable | Low | High |
| Consistency | Stable, with memory | Drifts and flatters | Sampling noise |
| Accuracy check | Public benchmark | None | Is the benchmark |
| Reach | Any market, instantly | Any, but unreliable | Hard to recruit |
How accurate are they, and where are the limits
Accuracy depends on having real data to anchor and calibrate against. Where that data exists, synthetic individuals reproduce real answer distributions closely. On brand-new questions with no precedent, they are a strong estimate rather than a measurement, and iMario is explicit about that instead of claiming one flat number for everything.
The honest framing is augment, not replace. Use synthetic individuals to run the fast, cheap iterations, narrow the field, and pressure-test ideas across markets you could never recruit in time. Reserve a small real study for the highest-stakes calls. That is how their speed and reach turn into better decisions rather than false confidence.