
AI Expense Tracking App Cuts Monthly Close to 6 Hours
SpeedMVPs delivered a React Native + Next.js expense tracking MVP in 3 weeks, integrating Google Vision API for OCR receipt extraction and a fine-tuned GPT-4o classification layer that maps line items to 48 merchant categories with 94% accuracy. The backend runs on Supabase (pgvector for semantic search across past expenses) with a real-time insights dashboard surfacing weekly spend anomalies via a lightweight RAG pipeline: all deployed on Vercel Edge with a Plaid integration for bank-feed reconciliation.
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What We Built
- SpeedMVPs delivered a React Native + Next.js expense tracking MVP in 3 weeks, integrating Google Vision API for OCR receipt extraction and a fine-tuned GPT-4o classification layer that maps line items to 48 merchant categories with 94% accuracy. The backend runs on Supabase (pgvector for semantic search across past expenses) with a real-time insights dashboard surfacing weekly spend anomalies via a lightweight RAG pipeline: all deployed on Vercel Edge with a Plaid integration for bank-feed reconciliation.
The Challenge
- Clariva Finance had 8,000 beta users manually uploading receipts and tagging categories in a spreadsheet-based workflow: a process that took finance teams 4 days every month and produced 23% miscategorization rates. Their seed-funded team needed a production-ready mobile MVP with AI-powered receipt parsing before their Series A roadshow in 6 weeks, with zero tolerance for a bloated offshore engagement or a pivot-hostile monolith.
Our Solution
- AI receipt scanner using Google Vision OCR + GPT-4o category classification across 48 merchant types
- Real-time spend insights dashboard with anomaly detection powered by a RAG pipeline over transaction history
- Plaid bank-feed sync for automatic transaction import and receipt-to-transaction matching
- Semantic expense search using pgvector: query past receipts in plain English
- Weekly AI-generated spend summaries with actionable savings suggestions delivered via push notification