Simulate real store with LLM-powered synthetic consumer
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!
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.
On the analytics side, Parastore competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Parastore performed against the three products that launched closest to it on the same day.
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.
For a complete overview of Parastore including community comment highlights and product details, visit the product overview.
KYEONGEOP LIM