Parastore

Product Introduction

1. Definition: Parastore is an open-source retail simulation platform and agent-based modeling tool. It is a technical prototype that combines Large Language Model (LLM)-powered synthetic consumer agents with an isometric 3D virtual environment to simulate in-store shopping behavior, foot traffic, and purchase decisions.
2. Core Value Proposition: It exists to enable retail professionals, data scientists, and developers to build, simulate, and optimize physical retail spaces in a risk-free, virtual sandbox using synthetic data, bypassing the high cost and logistical complexity of real-world A/B testing.

Main Features

1. LLM-Powered Synthetic Consumer Agents: The core engine uses LLMs (like Google's Gemini) to generate and drive virtual shoppers. Each agent follows one of 12 predefined behavioral patterns (e.g., "mission shopper," "browser"). Their actions—such as navigating, browsing shelves, and deciding to purchase—are constrained by a custom grammar and influenced by randomized context (mood, budget, companions) and impulse-buy logic triggered by products they see.
2. Isometric 3D Store Editor & Visualization: Built with React Three Fiber (Three.js), this feature provides an interactive grid-based editor for designing store layouts. Users can place assets like shelves, fridges, counters, and walls, assign product categories to fixtures, and then watch a real-time animated simulation of agent paths and interactions within the constructed 3D environment.
3. Data-Driven Simulation & Analytics Dashboard: The simulation outputs quantifiable metrics validated against real Point-of-Sale (POS) data. The frontend dashboard, powered by Recharts, displays live and post-simulation analytics including aggregate visitor count, conversion rate, average dwell time, and per-rack engagement heatmaps, enabling performance comparison between different layout variants.

Problems Solved

1. Pain Point: The exorbitant cost and irreversibility of physical retail experiments. Changing a store's layout, product placement, or circulation pathways in the real world is expensive, disruptive, and difficult to measure accurately.
2. Target Audience: Retail Operations Managers, Store Layout Planners, CPG (Consumer Packaged Goods) Analysts, Commercial Real Estate developers evaluating tenant layouts, and AI/ML Engineers or Researchers interested in multi-agent simulation and synthetic data generation.
3. Use Cases: Essential for pre-construction layout validation, A/B testing product placement for maximum conversion, optimizing aisle flow to eliminate dead zones, and rapidly prototyping concepts for store acquisitions or remodels to estimate potential sales impact before capital expenditure.

Unique Advantages

1. Differentiation: Unlike traditional retail analytics (heat maps from camera footage) or simple 2D planning software, Parastore introduces behavioral intent via LLMs. It simulates why a customer might move somewhere, not just that they do. Compared to other agent-based models, its tight integration with LLMs for persona generation and decision-making is a novel approach in the open-source retail simulation space.
2. Key Innovation: The integration of grammar-constrained LLM agents within a spatially-aware 3D pathfinding simulation. The "impulse-buy logic triggered by line-of-sight" is a specific technical approach that bridges the AI's narrative decision-making with the physics of the virtual environment, creating more plausible emergent shopping behavior.

Frequently Asked Questions (FAQ)

1. Can Parastore accurately predict real store sales? No, Parastore is a prototype designed for relative comparison (e.g., Layout A vs. Layout B), not absolute sales forecasting. Its outputs are LLM-grounded plausibility simulations, though it achieved a 0.955 Spearman correlation in validation against a real convenience store's POS data.
2. What are the costs associated with running a Parastore simulation? The primary cost is LLM API calls (e.g., to Google Gemini). Generating a week of synthetic consumer personas for a modest store can require hundreds of calls. Users must manage their own provider budget and API keys, with a default daily persona cap of 100 to manage costs.
3. What technical skills are needed to deploy and use Parastore? Deployment requires intermediate DevOps skills to set up a Python (FastAPI) backend and a React (Vite) frontend. Users need Python 3.13+, Node.js, and an LLM API key. Using the pre-built interface for layout design and simulation requires no coding.
4. How does Parastore handle product data and inventory? It does not use a real SKU master or inventory model. Product categories, names, and prices are LLM-derived based on the store's described trade area and customer profile, making it suitable for category-level analysis rather than SKU-level forecasting.
5. Is Parastore suitable for simulating large crowds or congestion? No, its current pathfinding is per-customer and deterministic. It does not model crowd dynamics, queueing, congestion, or social interactions between agents, focusing instead on individual agent behavior and aggregate metrics.