How it works
ilaida runs focus groups with synthetic panelists. The whole trick is in who gets to sit at the table — so let's follow one study from start to finish. Pick an industry; every example on this page will follow it.
Act 1 · The brief
No screener grids, no fieldwork briefs. What are you testing, who buys it, and what do you need to know. A paragraph is enough — everything downstream is built from it.
Testing
A premium pistachio–tahini snack bar
Audience
Shoppers in Turkey who buy packaged snacks every week
Key question
Would they pay ₺95 when the standard bar costs ₺60?
Act 2 · The population
Ask an AI for “six customers” and you get six clichés. So ilaida first builds a small table: which traits matter for this study, and how common each one is in your audience. It also notes how traits pull on each other — real populations aren't flat.
Every number is visible before anything else happens. Each one is labeled as an estimate — or as verified when it comes from real statistics you've entered. And you can simply type over any of them.
Interaction — Metro shoppers are more likely to be ingredient-readers — so the city panelists lean label-conscious, the town panelists don't.
Act 3 · The draw
A random draw — plain arithmetic, not AI — assigns each of the six seats its traits from the table. Marked attributes get a guaranteed mix: a 40/60 split over six panelists is always 2 and 4, never “roughly”. The draw has a ticket number, so the exact same panel can be reproduced later, for anyone who asks.
Only then does the AI write each person: a name, a job, a life that fits the drawn facts. The facts are locked; the storytelling is layered on top.
Elif, 34
Pharmacist, Istanbul
“I flip the package before I look at the price. If the list is short, we can talk.”
Hasan, 51
Shop owner, Denizli
“₺95 for a snack? It had better feed me, not just impress me.”
Act 4 · The room
An AI moderator runs the room like a researcher would: easy questions first, follow-ups on what people actually said, quiet panelists pulled in by name. Each panelist answers in their own voice, shaped by their drawn facts — the label-reader reads labels, the burned investor asks about the exit.
Watch it live, or close the laptop — the session runs on our side and is waiting, finished, when you come back.
The bar costs ₺95 — about 60% more than the one you usually buy. Honest first reactions?
Depends entirely on what's in it. If it's pistachio, tahini and nothing I can't pronounce, I'd buy it for the office drawer. But I'd check the price per gram first — premium bars cheat on size.
No. My customers wouldn't either. We know what a snack should cost.
Hasan, is there anything that would change your mind at that price?
If it replaced a meal, maybe. As an indulgence, never. That's what lokum is for, and lokum is cheaper.
Act 5 · The report
The session is distilled into a handful of findings. Each carries a confidence score — based on how many panelists independently corroborate it, not on enthusiasm — and the verbatim quotes behind it. Then recommendations: actions, not themes.
The whole study files itself in your archive and exports as a branded PDF — population table, panel profiles, findings, full transcript. Everything a skeptical stakeholder would ask for is already in there.
Label-conscious city shoppers will pay ₺95 — but they justify it with the ingredient list, not the word “premium”. Taste-first shoppers reject the price outright regardless of framing.
“I flip the package before I look at the price.”
Recommendation — Lead shelf materials with the short ingredient list, not “premium”. Skip rural and town distribution at launch — the price is a non-starter there.
Where this is going
Admins can already enter real statistics — census splits, survey results — as the fixed base for a market. Those attributes show a “verified” badge with the source, and the AI may only express audience skew on top, never rewrite the numbers.
Population bases imported directly from public statistical offices and large recurring surveys, kept current — so a study in a supported market starts from cited, dated numbers by default instead of estimates.
Upload survey results or crosstabs as a file and have them become distributions and interaction rules automatically — your tracker data becomes the ground your panels stand on.
The same sampling engine, but hundreds of respondents answering structured questions — quantitative reads with the same auditable composition, next to the focus-group depth.
Side-by-side runs against real fieldwork on the same brief, published as calibration reports — so you know where synthetic panels track reality closely and where to stay careful.
Your first study takes about as long as reading this page did.
Open the workbench