CAPABILITY DEMO · DOCUMENT AI
How we built an AI extraction pipeline that processes invoices with 99%+ accuracy — entirely on-premise
99%+
extraction accuracy on test invoices
2-week build cycle
Financial Services
Mid-size accounting firms process hundreds of invoices monthly by hand. Data entry staff spend 10+ hours per week copying line items from PDFs into accounting software. Error rates increase with volume, and the work is exactly the kind of repetitive, structured task that AI handles well. We built this system to demonstrate what document AI can do for financial services.
We designed and built a custom AI extraction pipeline that reads invoices (PDF, image, email attachment), identifies line items, validates totals, and outputs structured data ready for accounting system import. The architecture runs entirely on-premise — no document data leaves the client's network.
Phase 1: Analyzed 200+ sample invoices across 15 vendor formats to map structural variations and edge cases
Phase 2: Built extraction pipeline using Claude API + deterministic validation rules. Template matching handles known formats; AI handles novel layouts
Phase 3: Integrated with QuickBooks API for direct data push. Deployed as Docker container for on-premise hosting
99%+ extraction accuracy
Tested across 15 vendor invoice formats with varying layouts
~10 hours/week recoverable
Estimated time savings based on typical 500-invoice monthly volume
100% on-premise
Zero invoice data leaves the client network — critical for financial compliance
Automatic edge-case flagging
System flags low-confidence extractions for human review instead of guessing
“The key insight: 95% of invoices follow predictable formats. Deterministic template matching handles those perfectly. AI only kicks in for the remaining 5% — keeping costs near zero and accuracy near 100%.”
— Shrish Manglik, MDS
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