Simulate retail spaces with AI-driven synthetic consumers to optimize layouts and product placement.
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Tracked since2026
0 reviews trackedThe Bottom Line
Entry price
Free plan available, paid tiers above
Biggest pro
Enables risk-free experimentation with retail strategies
Biggest con
Currently a prototype, not a production forecasting tool
TL;DR - Parastore
- Simulates retail environments with AI-driven synthetic shoppers.
- Tests store layouts and product placements in a risk-free 3D sandbox.
- Uses LLMs to generate realistic consumer personas and behaviors.
Pricing: Free plan available
Best for: Growing teams
What is Parastore?
Parastore is an experimental 3D sandbox that allows users to build, simulate, and optimize retail spaces using LLM-generated synthetic consumers. It extends the concept of synthetic consumer surveys to physical retail environments, enabling the simulation of shopper behavior, including how they navigate a store, interact with products, and make purchases. This tool is designed for experimenting with retail strategies in a risk-free virtual setting.
While not a production forecasting tool, Parastore serves as a prototype for understanding the potential of LLM-driven agent simulations in retail. It allows businesses and researchers to test different store layouts, product placements, and customer pathways to optimize traffic flow, conversion rates, and evaluate potential remodels before committing physical resources. The project is open-sourced to encourage further experimentation and development in this domain.
Pros & Cons
Pros
- Enables risk-free experimentation with retail strategies
- Provides insights into shopper behavior in virtual environments
- Open-sourced for community contributions and further development
- Offers a novel approach to retail optimization using AI agents
- Uses modern tech stack for both backend and frontend
Cons
- Currently a prototype, not a production forecasting tool
- Lacks advanced features like POS history, real foot-traffic data, and detailed SKU taxonomies
- Simulation accuracy is dependent on the quality of synthetic consumer generation
Key Features
LLM-driven synthetic consumer generationIsometric 3D sandbox for retail space designSimulation of shopper movement and purchasing behaviorA/B testing for store layout and circulationProduct placement and conversion testingEvaluation of store layouts for acquisitions and remodels
Pricing Plans
Free TrialFree
$0 USD per month
- Unlimited public/private repositories
- Dependabot security and version updates
- 2,000 CI/CD minutes/month (Free for public repositories)
- 500MB of Packages storage (Free for public repositories)
- Issues & Projects
- Community support
Team
$4 USD per user/month
- Everything included in Free
- Access to GitHub Codespaces
- Repository rules
- Multiple reviewers in pull requests
- Draft pull requests
- Code owners
- Required reviewers
- Pages and Wikis
- Environment deployment branches and secrets
- 3,000 CI/CD minutes/month (Free for public repositories)
- 2GB of Packages storage (Free for public repositories)
- Web-based support
Enterprise
Starting at $21 USD per user/month
- Everything included in Team
- Data residency
- Enterprise Managed Users
- User provisioning through SCIM
- Enterprise Account to centrally manage multiple organizations
- Environment protection rules
- Repository rules
- Audit Log API
- SOC1, SOC2, type 2 reports annually
- FedRAMP Tailored Authority to Operate (ATO)
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Parastore FAQ
How does Parastore generate its synthetic consumer personas?
Parastore utilizes Large Language Models (LLMs) to analyze a real-world store address and a one-line customer-profile description. Based on this, it builds a daily traffic profile per weekday and generates individual personas for each hour of operation, simulating their behavior within the store.
What kind of retail strategies can be tested using Parastore?
Parastore allows for testing various retail strategies, including A/B testing different aisle structures, entry points, and customer pathways to optimize traffic flow. It also enables simulating the impact of moving high-margin items or changing rack categories on customer engagement and purchase conversion rates.
What is the technical stack used to build Parastore?
The backend of Parastore is built with Python 3.13, FastAPI, Pydantic, LiteLLM, Instructor, pathfinding, pandas, and openpyxl. The frontend uses React 19, Vite, TypeScript, React Three Fiber (Three.js), TanStack Router/Query, Zustand, Tailwind v4, shadcn/ui, Recharts, and ExcelJS.
How accurate are Parastore's simulations compared to real-world sales data?
According to the provided data, Parastore's synthetic consumer simulations show a Spearman Correlation of 0.955 by category and a JS-Similarity of 0.802 across all 109 products when compared to actual sales history from a physical convenience store. It's important to note these results were generated via Intellicia's own synthetic consumers, not the synthesis method published in the repo.
Can I use my own LLM provider API key with Parastore?
Yes, Parastore is designed to be flexible with LLM providers. The default model is gemini/gemini-3.1-pro-preview via LiteLLM, requiring a GEMINI_API_KEY. To switch providers, you can edit the backend/src/store_emulator/application/config.py file and supply the matching API key for your chosen provider.
Source: github.com