Moonlit aims to deliver consumer research in one day without building a new panel

Vulture Labs’ Moonlit is pitching a faster way to run consumer research by matching companies with already available participants and using their AI assistants to gather evidence from real purchases. The service is designed for studies that can be completed in about a day, with results aggregated and small groups withheld.

Why it matters: - Moonlit is designed to shorten consumer research timelines by skipping the work of building and recruiting a new panel. - The service targets studies that can produce usable answers in one day, which could help teams move faster on pricing and product decisions. - Buyers get a clearer distinction between verified answers, estimates and unanswered questions.

What happened: - Vulture Labs launched Moonlit as a paid consumer research service. - The service checks for available participants before a company funds a study. - Moonlit’s first use case focuses on purchase research. - Companies can ask what people bought, how much they paid or which subscriptions they cancelled. - Participants’ AI assistants search for relevant records, prepare answers and ask the person to approve them. - Accepted answers earn the participant a reward.

The details: - Moonlit’s one-day turnaround is a target, not a guarantee. - Audience size, eligibility, question design, participant approval and verification all affect delivery time. - The service compares itself with recruiting a new panel, not with every established research panel. - Results are aggregated, and groups smaller than five are withheld. - A receipt can support a purchase fact. - Reasons, preferences and future intentions still need to be treated as what the person says, not what a receipt proves. - Companies can use the research request page to check whether Moonlit can support a study.

Between the lines: - Moonlit is trying to combine two trends: companies want research answers faster, and people want personal AI assistants that can create earnings. - The product depends on matching the right audience to the right paid question before any money is spent. - The emphasis on receipts and answer quality suggests Moonlit is positioning itself as a research utility, not just a faster survey tool.

What's next: - Companies can submit a study request at the research request page. - People who already use an AI assistant can connect it at the setup guide. - People new to AI can use the same guide to choose a supported assistant and set it up for paid surveys. - Work and earnings will depend on study availability, eligibility and accepted answers.

The bottom line: - Moonlit is betting that companies will pay for faster research if AI assistants can verify answers from real-world records and still keep responses aggregated and private.