FinFit
FINFIT π° β Smarter Finance, Better Living.
Created on 11th October 2025
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FinFit
FINFIT π° β Smarter Finance, Better Living.
The problem FinFit solves

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 isnβt 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 donβt allow easy export for backup or sharing.
FINFIT solves all of this with automation and intelligence:
- β OCR-based Receipt Scanning β Just click a photo and expenses auto-fill.
- β Deep Link UPI Recording β Pay via Google Pay / PhonePe / Paytm and log it instantly.
- β Multi-Currency Support with Live Exchange Rates β Seamless tracking across countries.
- β QR-Based Multi-Device Sync β Continue finances from any phone without cloud login complexities.
- β Multi-Language Interface β Breaking language barriers in financial literacy.
- β Export in PDF / CSV / JSON β Full control over your data.
FINFIT isnβt just an expense log β itβs 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 appβs 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
ETHIndia
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