How I Built DockParser: AI Invoice Parsing with Gemini
A technical deep-dive into building a production document parser. Why I chose Gemini over GPT-4, server-side architecture, and cost optimization.
The Problem
Manual invoice processing is slow, expensive, and error-prone. Operations teams spend hours on data entry instead of strategic work. I built DockParser to solve this — an AI-powered document parser that extracts structured data from invoices and contracts with confidence scoring.
Why Gemini Over GPT-4
This was one of the first major architectural decisions. Here's my reasoning:
- Cost: Gemini is approximately 3x cheaper for vision tasks
- Cold starts: Faster response times for document processing
- Accuracy: Comparable performance on structured data extraction
- API design: Cleaner multimodal interface
I tested both on 100 sample invoices. Gemini hit 94% accuracy on field extraction vs GPT-4's 96% — a 2% difference that didn't justify the 3x cost increase for my use case.
Server-Side Only Architecture
Every AI call happens on the server. Zero client-side API calls. This was non-negotiable for two reasons:
- Security: API keys never touch the browser. No key exposure, no abuse.
- Control: Rate limiting, usage tracking, and cost controls all happen server-side.
The Stack
- Frontend: Next.js 14 with App Router
- Backend: Vercel Edge Functions + Supabase Edge Functions
- Database: Supabase (PostgreSQL)
- AI: Google Gemini 1.5 Pro (vision + text)
- Payments: Stripe with webhooks
- Storage: Supabase Storage (documents never leave Supabase)
Key Tradeoffs
Supabase RLS vs Custom RBAC
I used Supabase Row Level Security instead of building a custom authorization layer. Faster to ship, battle-tested security, but less flexibility for complex permission hierarchies. For a document parser, this was the right call.
Rate-Limited Demo Mode
The public demo is limited to 5 documents per day per IP. This prevents abuse and keeps my Gemini bill under control while still letting users try the product.
Results
- ~90% reduction in manual data entry time in test scenarios
- Handles 50+ document formats (invoices, contracts, receipts)
- Production-ready with Stripe billing integration
What I'd Do Differently
If I rebuilt this today, I'd add a confidence threshold that routes low-confidence extractions to human review. The current system flags them, but doesn't have a review queue. That's the next feature.