Synthetic panels · Real statistics
ilaida runs focus groups with synthetic panelists — sampled from a statistical model of your audience by code, then brought to life by language models. Who they are is drawn; only how they talk is generated.
RELIGIOSITY — 2 observant · 4 secular-leaning · target 40/60 · quota
INCOME BAND — 2 under ₺40k · 3 ₺40–80k · 1 ₺80k+ · target 30/45/25
AGE BAND — 1× 18–24 · 3× 25–34 · 2× 35–44 · target 20/45/35
Counts allocated by largest remainder. Reproducible from seed.
The difference
The usual way
When a language model invents the panel itself, it samples from its imagination: each “religious” panelist becomes a caricature of the category, demographics drift run to run, and nobody can say what population the panel represents — or reproduce it tomorrow.
The ilaida way
A population model states your audience's attribute distributions and how they interact — metro residents skew less observant, younger panelists skew lower income. Code draws each panelist from that model with exact quotas and a stored seed. The AI receives the result as fixed fact and writes one specific person on top. Composition is arithmetic, not improvisation.
Method · Brief to report in minutes
Product, audience, the questions keeping you up. Plain language.
The system estimates your audience's attribute distributions and the dependencies between them — every number visible, every number editable.
Code samples panelists from the model with a seeded random draw and exact quotas. Same seed, same panel — forever reproducible.
A moderated focus group unfolds live: warmup, deep dive, concept test, wrap-up. Each panelist answers in character, shaped by what was sampled.
Findings with confidence scores and verbatim quotes, prioritized recommendations, the full transcript in the appendix.
Rule — Metro residents skew less observant.
if urbanicity = Metro → religiosity × {Observant 0.6, Secular-leaning 1.6}
Every number disclosed before sampling — estimates labeled, never laundered.
Same question to you, Emine — would the certification on the label change what you pick up?
For me it's not a label, it's the default. What I'd question is the price — devotion doesn't make me careless with money.
Honestly I'd walk past it. But if my mother visits and it's in the fridge, that's one argument avoided. That's worth something.
Sampled attributes reach the conversation — a devout panelist argues like one, not like a label.
Exhibit — What a finding looks like
Observant panelists treat certification as table stakes and evaluate on price; secular panelists are indifferent but value household harmony. Marketing it as premium misreads both.
“For me it's not a label, it's the default. What I'd question is the price.”
What you get
Every panel ships with its receipts: target shares vs. actual counts, the seed that drew it, and honest notes when an option can't be represented at panel size.
An AI moderator follows a discussion guide, probes contradictions, and pulls quiet panelists in — while insights surface in the margin as the session runs.
No vibes. Each finding carries a confidence score grounded in cross-panelist corroboration and the verbatim quotes behind it.
Nothing is a black box: the population model, its confidence labels, the seed, the quotas — all of it is visible and goes in your readout.
Regenerate a panelist's narrative without touching their sampled attributes. Composition holds; the writing changes.
No recruitment, no scheduling, no incentives, no no-shows. Write the brief in the morning, argue about the findings after lunch — then rerun with a new price point before dinner.
Run synthetic market research you can defend in the readout — sampled, seeded, and quoted.
Open the workbench