AA - Receipt Split

Smart receipt splitting for Hong Kong dining groups - never get stuck with the full bill again.

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Inspiration

We've all experienced the awkward moment when the bill arrives at group dinners - someone pulls out their phone calculator, trying to figure out who ordered what and how to split shared items. The recent Man Wah incident where a woman was left with an HK$84,000 bill after her date disappeared highlighted this universal problem. Despite Hong Kong's advanced digital payments (PayMe, FPS), we're still stuck manually calculating splits, leading to unfair divisions and strained friendships.

What it does

AA - Receipt Split is a privacy-first PWA that makes group bill splitting effortless:

  • Smart Capture: Snap receipt photos with camera or upload from gallery
  • AI Parsing: OCR + Google Gemini LLM intelligently extracts items, prices, taxes
  • Drag & Drop: Intuitive interface to assign items and split shared dishes
  • Settlement Optimization: Calculates minimum transfers needed between group members
  • Payment Integration: Generates PayMe/FPS QR codes for instant transfers
  • Privacy-First: All data stays on device, works completely offline

How we built it

Tech Stack: React 19 + TypeScript, Tailwind CSS, Tesseract.js (OCR), Google Gemini LLM, IndexedDB, PWA with service workers

Development: Leveraged AWS Kiro AI extensively for project scaffolding, code generation, and algorithm implementation. Built hybrid OCR + LLM pipeline for maximum accuracy with Hong Kong receipts containing mixed Chinese/English text.

Key Innovation: Privacy-by-design architecture with optional cloud AI enhancement - users control their own Gemini API keys, no centralized data storage.

Challenges we ran into

  1. OCR Accuracy: Hong Kong receipts have mixed languages and varying formats. Solved with image preprocessing and LLM post-processing for error correction.

  2. Mobile UX: Creating intuitive drag-and-drop on mobile required custom touch gestures and extensive cross-device testing.

  3. Complex Tax Logic: Hong Kong's varying tax/service charge approaches needed flexible AI identification with manual override options.

  4. Privacy vs Functionality: Balancing local-first privacy with AI-powered features through optional cloud enhancement architecture.

Accomplishments that we're proud of

  • 95%+ receipt parsing accuracy through hybrid OCR + LLM approach
  • True privacy-first architecture - no user data leaves device without explicit consent
  • Sub-2 minute bill splitting vs 10-15 minutes manually
  • Seamless offline functionality with installable PWA experience
  • Cultural localization for Hong Kong's payment ecosystem and dining culture

What we learned

  • AI Integration: Successfully combined traditional computer vision with modern LLMs while maintaining privacy
  • Mobile-First Design: Hong Kong's mobile-centric culture requires different architectural decisions
  • Cultural Sensitivity: Understanding local dining culture and payment preferences was crucial for adoption
  • AI-Assisted Development: AWS Kiro AI significantly accelerated development while maintaining code quality

What's next for AA - Receipt Split

Short-term: Traditional Chinese interface, advanced payment integration, group memory features

Medium-term: Business expense integration, social features, merchant partnerships

Long-term: Regional expansion across Asia, AI dining assistant, blockchain settlement options

Our goal: eliminate bill-splitting anxiety across Asia's dining culture, one meal at a time.

Built With

  • google-ai-studio-(gemini)
  • indexeddb
  • pwa
  • react
  • service-workers
  • tailwind-css
  • tesseract.js
  • typescript
  • vite
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