BuyerIQ is an AI market research product that simulates purchase intent across more than 100 demographic segments. A founder can paste a website or describe a product and receive an analysis of who may buy and why in under 30 seconds.
The first version went from an idea on Friday to seven paying customers by Sunday.
The starting point
Gary Tan posted about research showing that large language models can reproduce human purchase intent. I saw a practical product inside the idea: let a founder test positioning against many synthetic buyer personas before spending weeks on conventional research.
I started building that night.
Turning research into a product
The underlying methodology comes from the paper LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation. The research reports 90% accuracy against 9,300 real shopping decisions.
BuyerIQ turns that method into a workflow. It accepts a website or product description, runs simulations across more than 100 demographic segments, and summarizes likely buyers, objections, and purchase reasons.
Validating with payment
I did not want to treat signups or compliments as the main signal. I added payment early and reached seven paying customers by the end of the first weekend.
BuyerIQ later helped several hundred founders with early-stage market validation and generated several thousand dollars in revenue. The important lesson was not simply that a product could be built quickly. It was that a research insight could be packaged into a result people understood and paid for immediately.
What I carried forward
BuyerIQ reinforced a pattern I now use across products:
- Start with a specific technical capability or behavioral insight.
- Wrap it in a workflow that produces a clear decision for the user.
- Ask for payment early enough that demand cannot hide behind enthusiasm.
- Keep the first product narrow enough to ship while the underlying idea is still timely.
See the BuyerIQ project page for the product details, methodology link, technology stack, and results.
