Projects · Case study
ValidateNine
Synthetic focus groups for pressure-testing ideas and messaging early.

The problem
Real focus groups are slow and expensive, so most small teams skip them and guess. I wanted to find out how much useful signal you could get from a simulated version - and where that signal stops being trustworthy.
What I built
You submit a concept or a piece of messaging. ValidateNine generates a panel of distinct personas with different backgrounds and priorities, has each one react, and consolidates the responses into a structured report: what landed, what didn't, and where the panel disagreed.
Decisions that mattered
- Keeping personas genuinely distinct, so the panel doesn't collapse into one voice with different names.
- Structuring the output as a report a decision-maker can scan, instead of a wall of generated text.
- Being explicit in the product about what a simulated panel is and isn't.
I designed and built all of it: the product, the React/Vite front end, the Supabase database, the prompt architecture, and the deployment.
My contribution
All of it. No team on this one.
What this doesn't prove
ValidateNine is a working, deployed application. It is not evidence that synthetic personas predict how real people respond, and it is not evidence of commercial traction. The more interesting question - which I'm still working through - is what evidence would justify using synthetic feedback for a particular decision.
Stack
- React
- Vite
- Supabase
- OpenAI API
- Vercel
- Cursor
Related writing
Synthetic focus groups: their potential, their limits, and what comes next
Can AI personas give every business the kind of market research only big budgets could buy?