Moonlit lets founders verify what customers already buy before building

Moonlit, from Vulture Labs, is pitching a paid research service that uses participant-approved answers from personal AI assistants to help founders check existing purchases and subscriptions before making product or pricing decisions. The company says the system can speed studies, reduce panel setup work and return aggregated results with source visibility.

Why it matters: - Founders often rely on stated preferences and miss what people already spend money on. - Moonlit is designed to ground product and pricing research in purchase records and subscription history. - The service aims to give teams a more concrete starting point before they build, price or distribute a product. - People who contribute accepted answers get paid, turning research into a task market for personal AI assistants.

What happened: - Moonlit, a paid research service from Vulture Labs, connects a company's question with available participants and their AI assistants. - The assistant looks for permitted information, prepares an answer and asks the participant to approve it. - Accepted answers earn the participant a reward. - Founders can check whether a study is feasible at more information.

The details: - The service can help teams ask what related services people already pay for, how often they pay and which subscriptions they have stopped. - Moonlit distinguishes between records and a person's explanation, and keeps that distinction visible. - The company says receipts can support spending facts, but they do not prove a person will buy a future product. - Moonlit checks audience availability before a study is funded. - The company says using people already connected can avoid the setup required to recruit a new panel. - Moonlit targets a one-day research cycle for suitable studies. - Timing depends on audience availability, approvals and verification. - Buyers receive aggregated results. - Groups smaller than five are withheld. - Buyers can see whether answers came from records, estimates or questions that could not be answered.

Between the lines: - The model shifts some of the research burden from panel recruitment to people already in a relevant audience. - The structure also creates a clearer audit trail for how an answer was produced. - That could make research more useful for founders making fast go or no-go decisions. - The setup still leaves the hardest tests untouched: founders must validate their own offer, price and distribution.

What's next: - Moonlit expects founders to define the decision, audience and purchase question before requesting a study. - Companies can request a study through the company's announcement. - People who already use an AI assistant can connect it through the setup guide. - New users can use the same guide to choose a supported assistant and set it up for paid surveys. - Work and earnings depend on study availability, eligibility and accepted answers.

The bottom line: - Moonlit is trying to make customer research more grounded by asking what people have already bought, not just what they say they want.