Parastore is an open-source (MIT) retail simulation where LLM-powered synthetic consumers walk through a 3D virtual store, browse shelves, and make purchase decisions. Each consumer follows one of 12 behavioral patterns with grammar-constrained actions, randomized context (mood, budget, company), and impulse-buy logic triggered by what they see along their route. Validated against real POS data with 0.955 Spearman correlation. Python/FastAPI + React/Three.js. Any LLM backend.
Hey Product Hunt! 👋
I'm Kay from Intellicia. We've been building AI synthetic consumer technology for the past year — helping brands like CJ, Pulmuone, and Fursys test products and messaging with AI personas instead of traditional surveys.
Parastore is a different beast. Instead of answering surveys, our synthetic consumers now physically walk through stores. They browse, pick up items, impulse-buy snacks near the checkout, and generate revenue data — all in a 3D simulation.
We're open-sourcing the entire simulation framework (MIT license) because we believe agent-based behavioral simulation is a space that deserves more builders and researchers.
A few honest notes:
The validation numbers come from our proprietary persona engine. The OSS version is a simpler pipeline — still useful for layout testing and agent behavior research, but don't expect the same accuracy out of the box.
Each sim run calls the LLM hundreds of times, so it's not free to run.
This is a simulation tool, not a crystal ball. It shows plausible outcomes, not predictions.
Would love your feedback — especially from anyone working on LLM agent simulation, retail optimization, or behavioral AI.
⭐ Star us on GitHub if this looks interesting!
How much freedom do you give them, like can they compare prices and brands or is it mostly choose and buy?
@kaylim022 this is so cool! Out of curiosity - did you observe “unconventional” agent behavior that was far off from what humans would do?
This is honestly one of the more interesting AI simulation projects I’ve seen lately. Synthetic shoppers with mood, budget, impulse buying, and route-based decisions feels weirdly realistic. Congrats on the launch!
About Parastore on Product Hunt
“Simulate real store with LLM-powered synthetic consumer”
Parastore launched on Product Hunt on May 28th, 2026 and earned 83 upvotes and 7 comments, placing #21 on the daily leaderboard. Parastore is an open-source (MIT) retail simulation where LLM-powered synthetic consumers walk through a 3D virtual store, browse shelves, and make purchase decisions. Each consumer follows one of 12 behavioral patterns with grammar-constrained actions, randomized context (mood, budget, company), and impulse-buy logic triggered by what they see along their route. Validated against real POS data with 0.955 Spearman correlation. Python/FastAPI + React/Three.js. Any LLM backend.
Parastore was featured in Open Source (68.5k followers), Developer Tools (514k followers), Artificial Intelligence (471k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 207.8k products, making this a competitive space to launch in.
Who hunted Parastore?
Parastore was hunted by KYEONGEOP LIM. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Parastore stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
KYEONGEOP LIM