A synthetic assembly of minds
Rehearse
reality.
GroupThinQ summons living populations of AI minds — each with a demography, a psychology, and an honest opinion — so you can hear how the world will answer before you ask it.
click anywhere — watch an opinion ripple through the society
Standing on published science
The problem
Every launch is a wager
placed in the dark.
You can build the product, write the copy, set the price. But the only way to learn what an audience actually thinks has been to wait, pay, and hope — so most teams simply don’t ask.
Six weeks of silence
A traditional concept test takes four to eight weeks to field, code, and report. Your market does not pause while you wait for permission to act.
A toll only giants pay
At $30,000–$80,000 per study, research is rationed. The decisions that most needed testing are the ones that never got it — instinct fills the gap.
The room lies
Focus groups answer to the loudest voice in them. People perform agreement in public and change their minds in private. You measured the theater, not the preference.
0% of new products fail. Most of those verdicts were available in advance — nobody could afford to hear them.
The simulation — live on this page
A society in miniature,
summoned on demand.
Pick a stimulus. The same three minds — drawn from real census and psychometric distributions — will read it and answer honestly. They are free to be bored. They are free to say no.
“Saffron Press — a cold-brew chai subscription. ₹299 a month, delivered every Sunday morning.”
Target audience · Urban India · 22–60 · mixed income
Meera Krishnan
31 · UX designer · Bengaluru
“The Sunday-ritual angle is genuinely lovely. But ₹299 is two café visits I already enjoy. I’d trial a month — keeping me past the novelty is the hard part.”
Rajan Iyer
58 · Retired banker · Coimbatore
“I have made my own filter coffee every morning for thirty years. Why would I pay a stranger to post me chai? A solution looking for a problem I don’t have.”
Ananya Bose
24 · Content strategist · Mumbai
“Okay — the packaging alone would end up on my stories. If the first box tastes as good as it sounds, I’m subscribed, and three of my friends are too.”
Panel verdict · 300 minds
38%
Top-2-box intent
Strong with metro 22–34. Collapses past 45. Price is the pivot.
Tier B · rank-order reliableIllustrative output — in the demo, every panel is generated fresh against your demographic
The method
From census to verdict
in four movements.
Summon the population
Census-grounded · psychometrically calibratedDescribe an audience — "urban Indian women, 25–40, mid-income" — and GroupThinQ assembles hundreds of statistically faithful minds. Demographics are drawn from census microdata; personalities from published psychometric norms built on 600,000+ real people. Nothing is invented that data could supply.
Stage the stimulus
Text · image · price · A/B/nA product concept, an ad, a price point, a tweet, an image — the panel sees exactly what your audience would see. No more, no less. Test one idea, or pit five against each other.
Let every mind speak
Independent · uncoached · honestEach agent reacts privately, in its own voice — no herding, no moderator, no pressure to agree. Free-spoken reactions are then scored by a distribution-preserving method from the recent research literature, validated against thousands of human survey responses.
Read the verdict
Distributions · segments · rationales · tiersPreference distributions, A/B winners, segment fault lines, and quotable rationales — each stamped with a confidence tier that tells you exactly how much weight it can bear.
The evidence
Accuracy is the product.
Synthetic research lives or dies on one question: does the simulation predict reality? We hold ourselves to the published record — and report every number against the ceiling of human self-agreement.
The headline result
“Synthetic consumers reproduced human purchase intent at 90.2% of human test-retest reliability — across 57 real consumer surveys.”
Published validation · PyMC Labs × Colgate-Palmolive · 9,300 human responses · 2025
Why most AI panels fail
Ask an AI to “rate this 1–5” and opinion collapses to the middle — everyone mildly likes everything, and the spread that real markets are made of disappears.
GroupThinQ’s engine is built to preserve disagreement: its response distributions match human ones at 0 fidelity, where naive prompting manages 0.26–0.45. The spread is the signal.
Every verdict is auditable — ask any mind why it voted the way it did.
Marble portrait of Gaius · The Met · public domain
Every number ships with its confidence tier
Calibrated
Category validated against paid human anchor studies. Calibrated estimates with empirical error bands.
Rank-order
Adjacent validated category. Winners and rankings are reliable; absolute levels carry wider bands.
Screening
Unvalidated territory. Clearly labeled a directional signal — never dressed up as a measurement.
Use cases
If it will ever meet an audience,
rehearse it here.
Concept testing
Rank five product directions before a rupee of engineering is spent. Find the winner — and the segment that crowns it.
“Which of these three flavors wins with health-conscious millennials in Tier-1 cities?”
Copy & creative
A/B/n test headlines, taglines, and campaign concepts against the exact demographic the media buy will reach.
“Test five headlines for the relaunch against urban professionals, 25–34.”
Price sensitivity
Watch intent bend as the price moves. Find the point where enthusiasm breaks — segment by segment.
“Where does top-2-box intent fall below 30% for SMB buyers?”
Posts before posting
Hear how a tweet, a reel, or an announcement lands across audiences — including the ones it will annoy — before it ships.
“How will founder-stage CTOs receive this pricing-change post?”
Survey rehearsal
Pilot your questionnaire on synthetic respondents first. Catch confusing wording and broken scales before the field bill arrives.
“Run the CSAT redesign past 200 respondents matching our base.”
Segment cartography
Map where your audience fractures — which psychological and demographic fault lines split the verdict on the same idea.
“Show me who loves and who hates the rebrand, and why.”
Our covenant
We would rather be trusted
than impressive.
Synthetic research earns its seat by knowing its own limits. The industry’s overclaims are our moat: we publish what works, disclose what doesn’t, and stamp every number with the confidence it deserves.
What we stand behind
- Rank-ordering of ideas within validated categories — the decision that matters most, made reliable
- Directional effects and segment differences, anchored to human data wherever it exists
- A rehearsal layer that makes your human research cheaper, sharper, and better targeted
- Every output traceable: ask any agent to explain its verdict
What we refuse to claim
- We never report fake margins of error — synthetic panels are not probability samples
- We don’t predict virality; no method on earth reliably can, and we won’t pretend otherwise
- We don’t sell uncalibrated point estimates for narrow subgroups as facts
- We are not a replacement for human research on high-stakes, irreversible decisions
Methodology designed for AAPOR-era disclosure standards · radical transparency is the credibility wedge
The assembly
is waiting.
Summon a population. Stage your idea.
Hear three hundred minds answer.
Runs locally · no account · full demo mode without API keys