voxpop · synthetic population simulation
voxpop simulates how population segments respond to a message, product, or idea. Every result carries a calibrated trust map: we tell you when the simulation can be trusted - and when it can't.
reality · spread · structure
Every voxpop result ships with one.
01 · The problem
Synthetic respondents are booming - and most vendors promise human-like answers with no way to verify them. We built the instruments and measured what actually happens.
In our measurements, price and brand do not predict whether a model can represent a population. Some premium models fail the basics on the raw signal, and some inexpensive ones do better than their price suggests.
Some flagship models compress the diversity of answers so far that different “respondents” sound like one person. It reads as confident - and it is wrong. Without a dedicated measurement layer, nobody notices.
A single “92% match” hides where a simulation fails. Trust has to be measured per segment and per question - and it has to be allowed to say “we don't know”.
02 · What we do
See how different population segments respond to a message, concept, or price - as distributions and differences between groups, not one inflated number.
Each output is labeled: here the result is reliable, here it isn't, and here we abstain. Honesty is a feature, not a disclaimer.
Already using synthetic respondents? Send us samples from your provider and get a report on where that model can be trusted - and where it can't.
Before you spend budget, a fast diagnostic tells you whether a given model is even worth calibrating for your population.
03 · How it works
We start from real, public survey data about the population you care about - or from your own research data.
We tune the synthetic population until its behavior matches reality where that can be verified - separately for each segment, never with one brush.
Before you see a single result, the simulation earns a certificate on independent axes: does it hit reality, does it match the real spread of opinion, does it preserve how attitudes hang together.
Numbers come from a calibrated population layer. Voice comes from a separately certified segment character - an illustrative composite of a segment, never a real person. Quantitative and qualitative each earn their own certificate.
Population layer
calibrated distributions · shares · differences between segments
Segment character
the “why” in the segment's own words · certified coherence
That's as deep as we go publicly - the method is our craft. The full validation methodology is being prepared for peer review.
04 · Use cases
Which variant lands with which segment - shares from the calibrated layer, objections and language from segment characters, and an explicit trust map over all of it.
Test an idea, ad, or price across segments before fieldwork. Learn where segmentation genuinely adds signal - and where a simple average is all you need, so you don't pay for more.
For research agencies and insight teams: independent validation of the synthetic respondents you buy or build. A report and a trust verdict - before your client asks.
An open validation method, pre-registered runs, and a public fidelity leaderboard in preparation. If you work on survey methodology or model evaluation - let's talk.
Live toy demo
An illustrative toy, not client data: distributions shift differently per segment - and the trust label never detaches from the result.
Illustrative animation. In a real engagement each strip is a calibrated, certified distribution.
05 · Why voxpop
Fidelity is judged on several independent axes, separately for every segment. No single vanity number.
The system distinguishes “no real effect” from “not enough evidence to call it”. Results that don't clear the bar are never dressed up as results.
A cheap pre-flight tells you whether a model is worth calibrating - before you pay for data, fieldwork, or compute.
Holding one character together and reproducing the diversity of a population are different skills. We measure both - separately.
Claims are frozen before the runs, and the methodology has been through adversarial review. We publish what fails, too.
In our tests, calibration maps held up across years of real-world drift - pointing to far less frequent re-tuning.
06 · Built in the open
The validation approach is developed open-source-first: pre-registered runs with claims frozen before data, honest publication of negative results, and a public fidelity leaderboard for models answering in Polish - in preparation alongside a peer-review preprint.
07 · Work with us
For research & insight teams
An independent audit of the synthetic respondents you build or buy - with a trust report you can show your clients.
Request an auditFor product & marketing teams
Test a message or concept on a calibrated population - with a trust map, not just numbers.
Request a pilotFor researchers
Open method, pre-registered runs, honest nulls. Help set the bar for synthetic audiences.
Get in touch08 · FAQ
No - by design. voxpop works only at the level of aggregates and segments. Predicting individuals is neither possible at this fidelity nor something we sell. Anyone promising it is overclaiming.
No. Segment characters are illustrative composites of a segment - an embodied distribution, never a simulated real person.
We are model-agnostic. We measure models before we use them, and the measurements decide. Expensive doesn't mean faithful - our diagnostics pick what actually works for your population, which is often not the most famous model.
Public reference survey data and, for commercial engagements, your own research data. Reference microdata never leaves the lab, and results are aggregates only.
Then we say exactly that. Abstention is a first-class output: you learn where the result is trustworthy, where it isn't, and what would be needed to close the gap.
A chatbot gives you one confident voice. voxpop gives you a calibrated population with measured diversity, per-segment verdicts, and a certificate that is earned, not asserted.