No Niner is a growing set of 60+ no-code data tools that run in the cloud. Scrape Amazon reviews, get YouTube transcripts, find and verify work emails, enrich companies, track Nordic public tenders and more. No setup, no subscription, you pay per run. And one thing nobody else lets you screenshot: do ChatGPT, Perplexity and Gemini actually recommend your brand? No Niner measures your AI-search visibility by category, so you see where you get named and where you don't.
Ask any question to a representative synthetic audience built from real public census data. Get a simulated response distribution in minutes — with segment breakdowns and an honest calibration score that back-tests the prediction against real data. A fast first read before you pay for a real study.
We built a tool that polls a census-grounded synthetic audience in minutes - and then shows you how accurate the result actually is (we back-test it against real polls). Launching on PH tomorrow.
Genuinely curious what this community thinks: for which questions or categories would you NOT trust synthetic respondents? That's exactly the limit we're trying to measure honestly.
Hey everyone, I'm Fredrik. I'm launching No Niner tomorrow and wanted to share the thing behind it before launch day.
Everyone optimizes for Google. But more and more buyers now ask ChatGPT, Perplexity and Gemini "what's the best X for Y" and get a direct answer with a handful of brands named. Almost nobody can tell whether they're one of those brands, or invisible.
Hey Product Hunt!
Every founder and marketer hits the same wall: you want to know what a market thinks, but real surveys are slow, expensive, and you need a panel.
So we built Synthetic Audience. You ask a question; we assemble a representative audience of digital respondents - each one drawn from real public census data (age, sex, education, income, by country) - and simulate how they'd answer. Minutes, not weeks.
But here's the part I actually care about, because it's where every other "AI persona" tool quietly looks away: synthetic respondents are a model, not a mirror. They can be confidently wrong.
So we made the accuracy a feature. Calibration mode takes a real poll you already have, re-simulates every real respondent, and tells you exactly how close the prediction landed - mean error, per segment. You launch a study knowing whether it's a confident read or just a hypothesis.
You can use it right now: start free in the web app (Pro from $49/mo when you need more), or run it as a pay-per-use engine on Apify - links above.
I'd love your hardest question: where should synthetic respondents NOT be trusted? That's exactly what we're trying to measure honestly. 🙏
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