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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Draw a store, generate LLM personas, and watch them shop — an isometric 3D sandbox for synthetic-consumer experiments.
| Date | Stars |
|---|---|
| 2026-07-24 | 555 |
| 2026-07-25 | 554 |
| 2026-07-28 | 554 |
| 2026-07-30 | 554 |
| 2026-07-31 | 526 |
| 2026-08-06 | 526 |
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<div align="center">  <h1>Parastore</h1> Build, simulate, and optimize retail spaces with synthetic consumers. <br>  </div> ## Overview [intellicia](https://intellicia.co) builds tooling around _synthetic consumers_ — virtual personas generated by LLMs that we run surveys against in place of (or alongside) real respondents. Parastore is an experiment in extending that idea from surveys to **physical retail**: if we can simulate how a synthetic shopper answers a survey question, can we also simulate how they walk through a store, what they pick up, and what they end up paying for? This repository is a **prototype**, not a production forecasting tool. A rigorous offline-sales model would need POS history, real foot-traffic data, SKU-level taxonomies, weather/seasonality signals, queueing dynamics, and a lot more than what is wired up here. Parastore is closer to a sketch — a "what if we tried it this way?" — than a finished product. We are open sourcing it because the sketch itself might be useful: as a starting point for your own experiments, as a reference for how to wire up an LLM-driven agent simulation end-to-end, or simply as something fun to play with. ## Screenshots <div align="center"> <table> <tr> <td width="50%"><img src="./.github/assets/screenshot_1.png"></td> <td width="50%"><img src="./.github/assets/screenshot_2.png"></td> </tr> <tr> <td width="50%"><img src="./.github/assets/screenshot_3.png"></td> <td width="50%"><img src="./.github/assets/screenshot_4.png"></td> </tr> </table> </div> ## Use Cases Parastore goes beyond simple 3D visualization. By combining LLM-driven synthetic consumers with physical space simulation, it allows you to test retail strategies in a risk-free environment. - **Store Layout & Circulation A/B Testing:** Experiment with different aisle structures, entry points, and customer pathways to optimize traffic flow and eliminate dead zones. - **Product Placement & Conversion Testing:** Simulate how moving high-margin items or changing rack categories impacts customer engagement and overall purchase conversion rates within the store environment. - **Acquisition & Renewal Valuation:** Rapidly prototype and evaluate potential store layouts for new acquisitions or upcoming remodels before committing physical resources. ## Simulation Accuracy <div align="center"> <img src="./.github/assets/predicted_vs_actual_sales.png" width="80%"> </div> To evaluate performance, this chart compares **actual sales history from a physical convenience store** with **synthetic consumer simulations driven by Parastore**. The analysis covers 500 real customers and 109 different products. | Metric | Value | Scope | | :--- | :--- | :--- | | **Spearman Correlation** | `0.955` | By category | | **JS-Similarity** | `0.802` | Across all 109 products | | **NDCG@all** | `0.868` | Across all 109 products | >_Note: These results were generated via Intellicia's own synthetic consumers, not the synthesis method published in this repo._ ## Demo video <div align="center"> **▶️ Click to Watch the Full Demo Video:** <a href="https://youtu.be/q_96OIBJTBQ?si=0gMDBfdbokAwhXXo"> <img src="./.github/assets/video_thumbnail.png" width="80%"> </a> </div> ## Tech stack - **Backend:** Python 3.13, FastAPI, Pydantic, LiteLLM, Instructor, pathfinding, pandas, openpyxl. - **Frontend:** React 19, Vite, TypeScript, React Three Fiber (Three.js), TanStack Router/Query, Zustand, Tailwind v4, shadcn/ui, Recharts, ExcelJS. ## Requirements - Python **3.13+** with [uv](https://github.com/astral-sh/uv) - Node **20+** with **pnpm 10.33.0** (pinned in `frontend/package.json`) - An LLM provider API key. The default model is `gemini/gemini-3.1-pro-preview` via LiteLLM, so a `GEMINI_API_KEY` works out of the box. To switch providers, edit `backend/src/store_emulator/application/config.py` and supply the matching `*_API_KEY`. ## Qu
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:da2bf10cefe518ad, topic:synthetic-data
matched fp:da2bf10cefe518ad, topic:llm