StockSpot – Smart Local Inventory & Discovery Platform

AI-powered platform for local product discovery and merchant inventory management with smart search, semantic AI, and low-stock notifications.

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StockSpot – Smart Local Inventory & Discovery Platform

Inspiration

Shopping locally is often inconvenient because customers don’t know which stores have what they need.
At the same time, merchants struggle with managing stock and keeping customers informed.

We wanted to solve this two-sided problem by using:

  • AI-powered semantic search
  • Distance and city-based queries
  • Real-time inventory data
  • AI-enhanced notifications for merchants

This way, customers can easily discover nearby products, while merchants get smart tools to manage and grow their business.

What it does

  • For Customers:

    • Search for products in nearby stores using exact, partial, or natural language queries
    • Use distance-based search (e.g., within 5km of current location)
    • Use city-based search (e.g., “Chennai”)
    • Get related product suggestions powered by vector search in TiDB
    • See shop locations on Google Maps and get directions

  • For Merchants:

    • Register shops with GPS, shop details, and owner information
    • Add products with name, price, and quantity
    • Enable AI-assisted product categorization and description generation
    • Update or delete products easily
    • Configure low-stock and critical-stock thresholds
    • Receive AI-enhanced notifications with actionable restock suggestions

How we built it

  • Frontend: React.js (responsive and mobile-first design)
  • Backend: Node.js + Express.js (REST APIs for products, merchants, and users)
  • Database: TiDB Cloud (MySQL-compatible with vector functions)
  • AI Layer: Kimi AI (query refinement, product enhancement, notification text)
  • Maps Integration: Leaflet, Google Maps API for shop locations and navigation
  • Deployment: Vercel (frontend) + Render (backend)

Challenges we ran into

  • Designing a multi-layer search system (exact → partial → semantic search)
  • Handling vector similarity queries at scale
  • Managing real-time inventory data with location filters
  • Preventing notification overload for merchants
  • Ensuring privacy and security with customer location data

Accomplishments that we’re proud of

  • Built an end-to-end pipeline: merchant registration → product upload → AI enhancement → searchable inventory → AI-powered notifications
  • Successfully implemented vector search in TiDB for semantic matching
  • Enabled distance-based and city-based product discovery
  • Created AI-driven tools that benefit both customers and merchants

What we learned

  • How to combine AI + Databases + Maps to solve real-world problems
  • Working with TiDB vector search for semantic recommendations
  • Designing a privacy-focused geospatial search system
  • Improving user experience when blending multiple AI-driven workflows

What’s next for StockSpot

  • Launch a mobile app with push notifications
  • Add bulk product uploads (CSV, Excel) for merchants
  • Enhance AI recommendations with dynamic pricing and supplier suggestions
  • Partner with local delivery services for end-to-end shopping
  • Integrate with billing and POS software for seamless merchant operations

License

This project is licensed under the MIT License – free to use, modify, and distribute.

Built With

  • description-enhancement
  • express.js
  • express.js-database:-tidb-cloud-(mysql-compatible-+-vector-search)-ai:-moonshot-ai-(query-refinement
  • javascript
  • leaflet.js
  • moonshotai
  • node.js
  • notifications)-apis:-google-maps-api-platforms:-render-(backend-hosting)
  • react
  • render
  • sql
  • sql-frontend:-react.js-backend:-node.js
  • tidb(mysql+vectorsearch)
  • vercel
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