How I Built a Real-Time Grocery Price Comparator with React Native & Groq AI

Zaid Bhimala — Sat, 14 Nov 2026 12:20:25 +0000

Indian quick-commerce platforms deliver groceries in 10 minutes, but catalog fragmentation and dynamic pricing mean that identical grocery baskets often differ in price by 20% or more.

To solve this, I built BasketBuddy—a React Native app that compares grocery baskets across stores in real time, auto-matches catalog entities, and bridges carts directly to store checkouts.

Here is an architectural breakdown of how it works and the engineering hurdles faced.

1. The Core Engineering Challenge: Catalog Entity Resolution

Store catalogs rarely share standardized SKUs or naming conventions:

Store A: "Amul Taaza Homogenised Toned Milk 1L"
Store B: "Amul Toned Milk 1000ml pouch"
Store C: "Amul Taaza Fresh Milk - 1 Litre"

Standard Levenshtein distance fails because volume formatting (1L vs 1000ml) and promotional noise (Super Saver, Combo) distort similarity scores.

Solution: Hybrid Deterministic + LLM Matching

Deterministic Normalization:
- Standardizes unit tokens (ml -> l, gm -> kg).
- Strips common promotional stop-words.
- Extracts brand tokens and item weight/quantity.
Sub-second LLM Fallback (via Groq):
- When deterministic confidence falls below threshold, an ultra-fast LLM call through @ai-sdk/groq evaluates the ambiguous title candidates in ~200–300ms to verify item equivalence.

2. Natural Language AI Mode (Recipe to Cart)

Instead of searching item-by-item, users can type natural meal prompts:

"Ingredients for Butter Chicken and Jeera Rice for 4 people"

Using structured outputs with Groq:

The prompt translates into a list of normalized grocery ingredients with realistic quantities.
The auto-matcher searches available inventory across stores and maps them directly to active stock.

3. Architecture & Tech Stack

Framework: React Native with Expo SDK 58 & Expo Router (typed routing)
Language: TypeScript
AI Integration: Vercel AI SDK (@ai-sdk/groq)
Animation: React Native Reanimated (fluid price delta transitions)
Store Bridges: In-app WebViews to preserve session context and export finalized baskets
Testing: 560+ deterministic unit tests using jest-expo (mocked network & store layers)

4. Key Takeaways

Avoid purely algorithmic matching for messy real-world retail data: Combining lightweight token extraction with an ultra-fast LLM fallback yields over 90%+ match accuracy without breaking latency budgets.
Mocking third-party integrations early: Quick-commerce web structures change rapidly. Keeping store adapters strictly decoupled and mocked in unit tests saved countless hours of debugging.

Links & Resources

Google Play Store: Download BasketBuddy
Video Walkthrough: Watch on YouTube

Have you tackled entity matching across messy e-commerce catalogs? What heuristics or embedding approaches worked best for you? Let's discuss in the comments!