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FinFit

FinFit

FINFIT πŸ’° β€” Smarter Finance, Better Living.

Created on 11th October 2025

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FinFit

FinFit

FINFIT πŸ’° β€” Smarter Finance, Better Living.

The problem FinFit solves

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Most people struggle to manage their finances because traditional expense trackers are manual, boring, and disconnected from real-life usage. Common pain points include:

  • Manual data entry fatigue β€” Nobody wants to type every expense from receipts.
  • UPI payments are instant, but tracking them isn’t β€” People spend digitally but record manually.
  • Travellers and freelancers deal with multiple currencies, and switching calculators breaks the flow.
  • Using the app on multiple devices means starting from zero β€” no native sync.
  • Language barriers stop non-English users from adopting finance tools.
  • Data lock-in β€” Most apps don’t allow easy export for backup or sharing.

FINFIT solves all of this with automation and intelligence:

  • βœ… OCR-based Receipt Scanning β€” Just click a photo and expenses auto-fill.
  • βœ… Deep Link UPI Recording β€” Pay via Google Pay / PhonePe / Paytm and log it instantly.
  • βœ… Multi-Currency Support with Live Exchange Rates β€” Seamless tracking across countries.
  • βœ… QR-Based Multi-Device Sync β€” Continue finances from any phone without cloud login complexities.
  • βœ… Multi-Language Interface β€” Breaking language barriers in financial literacy.
  • βœ… Export in PDF / CSV / JSON β€” Full control over your data.

FINFIT isn’t just an expense log β€” it’s your financial companion that adapts to how you actually spend.

Challenges we ran into

Building FINFIT came with several interesting hurdles:

  • State Management Across Multiple Features: With modules like analytics, chatbot, OCR, and budgeting running simultaneously, keeping the app’s global state
  • consistent without performance drops was challenging. We had to optimize our data flow and reduce unnecessary re-renders.
  • QR-Based Data Sync Implementation: Designing a reliable cross-device sync without a cloud backend initially caused version conflicts. We solved this by generating compact, checksum-validated JSON payloads to ensure data integrity.
  • OCR Accuracy & Formatting Issues: Raw OCR output was often inconsistent across fonts and receipt layouts, so we implemented regex-based post-processing to auto-correct misread values.
  • AI Chatbot Response Latency: The Google Gemini API occasionally caused noticeable delays. To enhance UX, we added loading placeholders and fallback canned responses.
  • Chart Rendering in React Native: Some chart libraries caused crashes on lower-end devices. We had to carefully benchmark options and optimize animations for smoother transitions.

Tracks Applied (1)

Ethereum Track

NA
ETHIndia

ETHIndia

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