Where every claim comes from
Before we ask you to trust synthetic, here is the full trail: one real study end to end, human vs synthetic theme by theme, and how we audit it.
We pre-registered a prediction in our own favor. The data said no. We published it anyway.
A pre-registered study of 2,600 synthetic respondents across eight markets, sealed by hash before we saw a single data point. It set out to test whether the psychometric layer our engine adds is what makes markets differ. It is not — and we publish that, alongside the correction we made to our own first reading when the data would not support it.
The pre-registered successor to our earlier exploratory study (n=5 per market) — built to re-test our own prior claim, with controls. Read the v1 on SSRN
Profile divergence within a market
Fix a persona's value profile and its behavior changes — in the predicted direction, across all eight markets. This is what a segment is, and it is our strongest result. It is what QualiSynth is built to do.
Differences between markets
These are real, but they do not come from the psychometric layer — a control cell without it shows just as much structure. They are rendered by the base model conditioned on country and language, so we present them as hypotheses to validate against humans, not as national truths.
What we found, and what we do not claim
- Sealed by SHA-256 hash before any data. Nothing was added, removed, or reformulated after.
- The central result runs against our own product — and we published it in full.
- Our first reading suggested a stronger claim; about 79% of it was measurement noise, so we withdrew it.
- No human judges yet: interim reliability is an independent LLM coder. Human validation is Phase 2.
- Data and code with fixed seeds are published — a skeptic can re-run the entire analysis.
From brief, to synthetic conversation, to a traceable theme.
Before we ask you to trust synthetic research, we show you where the output comes from. This is one real study, end to end.
"Why do customers stay with their insurer even when they are unhappy?"
Internal hypothesis: Price is the main barrier.
Audience: Spanish insurance customers, mixed age and income segments.
Guide: Switching triggers, trust, coverage understanding, reactions to renewal messaging.
What stops you from switching insurer, even when you are not happy?
"Price is the brake. Complexity is the fog. The fog is worse — it stops me from even seeing the road."
Complexity blocks switching more than price.
Mapped across 217 synthetic interviews — strongest where coverage feels hardest to compare.
The brief shifted before fieldwork.
From "test price messaging" to "probe clarity, trust, and rejection of scare tactics".
What 217 Spanish insurance consumers actually said
Insurance Coverage Choice — Spain. N=217 synthetic consumers. SHQI 0.989 (internal quality score).
We needed to understand why Spanish consumers stay with their insurer even when unhappy — and what triggers switching. The internal hypothesis: price is the main barrier. We ran 217 synthetic consumer interviews in under 30 minutes.
- Fear-based upsell was the #1 rejected pattern — 125 mentions, 0 acceptances across all 217 respondents
- The market leader dominated spontaneous recall with 530 mentions — 2.2× more than its nearest competitor
- "Complexity is the fog" — consumers could not evaluate coverage options, so they stayed put by default
The real blocker was complexity and distrust of scare tactics. The brief shifted from price messaging to transparency and simplicity — before a single real interview started.
Hypothesis: price is the main barrier.
Real blocker: complexity and distrust of scare tactics.
The fieldwork guide was rewritten before a single real interview — before any of the budget was spent on the wrong questions.
You don't have to "believe" in synthetic.
See where it aligns with humans. And where it doesn't.
Mirror View runs the same interview guide and analysis pipeline on humans and synthetics, theme by theme — mention rates, themes, sentiment. Today the human grounding is our back-test against real survey data (World Values Survey); live per-study human comparison is rolling out. You audit convergence and divergence — not AI claims.
Illustrative — how human and synthetic responses line up per theme in a hybrid study.
- Same interview guide and analysis pipeline for humans and synthetics — comparable outputs.
- Quotes and evidence trace back to conversation transcripts, not free-floating AI summaries.
- Use synthetic for coverage and speed; use humans when live validation is required.
Built to be audited, not believed.
Every claim on this page maps to something you can inspect — not an AI wrapper that asks for faith.
- SHQI — 12 deterministic quality metrics scored on every interview
- Mirror View — human-vs-synthetic mode on the same guide and pipeline
- Every theme linked back to the conversation that produced it
- Full transcript export — read the raw evidence yourself
- Population back-tested against real-world survey data (World Values Survey)
- Honest about limits — we show you where synthetic diverges from human
We pressure-tested this very page with QualiSynth.
Before publishing, we ran synthetic interviews with agency researchers in the US, UK and Spain — the exact buyers this page is for.
One finding: buyers thought QualiSynth analysed their own transcripts, instead of generating respondents.
That's why this page now leads with the mechanism. Directional, fast, and honest about its limits — exactly how we'd want you to use it.