# MarketResearchAgents

- **Event:** [Google I/O Hackathon](https://cerebralvalley.ai/e/google-io-hackathon)
- **When:** Sat, May 23 at 9:00 AM – 10:00 PM (PDT)
- **Where:** Shack15, San Francisco, CA
- **Team:** [Reena Agrawal](https://cerebralvalley.ai/u/reenaagra), [Amol M](https://cerebralvalley.ai/u/akaal)
- **GitHub:** https://github.com/bluefalcon2357/demographic-reaction-engine
- **Demo video:** https://www.youtube.com/watch?v=Pfj-31A9pj0&feature=youtu.be
- **Gallery:** https://cerebralvalley.ai/e/google-io-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/google-io-hackathon/hackathon/gallery/80

Demographic Reaction Engine predicts how the US population would react to a pitch, ad, product page, or policy — before you ship it.

Paste copy or a URL. Google Gemini returns a structured analysis across five demographic dimensions (age, education, region, gender, marital status) with stance scores from -100 to +100, concerns, benefits, and verbatim quotes. Those segment reactions are mapped onto 13,354 simulated personas, rendered as a stance histogram, archetype heatmap, polarization index, and browsable persona directory.

Persona dataset. Built from NVIDIA Nemotron-Personas-USA (Census-grounded synthetic personas), filtered to working-age, and enriched with personal income via k-nearest-neighbor lookup against the 2023 ACS PUMS (~3.4M US Census records) on state × occupation × education × sex.

The bigger picture. Synthetic Census personas are the open-data proof of concept. The same engine plugs into first-party customer profile data at companies like Google or Meta — turning their audience graphs into a production market research product: run any new ad, feature, price, or policy past millions of agent-simulated real customers in minutes, before a single focus group.

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Markdown version of https://cerebralvalley.ai/e/google-io-hackathon/hackathon/gallery/80. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt