An AI-powered accounts payable automation system that replaces manual data entry and legacy OCR in enterprise finance teams. It ingests invoices in any format, extracts all structured fields using LLM-powered document intelligence, performs automated 3-way matching against purchase orders, and routes exceptions for human review.
Finance teams at a large enterprise were processing 8,000+ invoices per month with manual data entry and legacy OCR tools achieving only 70% extraction accuracy. Errors caused payment delays, supplier disputes, duplicate payments and audit findings. A 14-day average invoice cycle was creating real supply chain strain.
A focused, deliberate process — from discovery to deployment.
Fine-tuned a multimodal LLM on 50,000 annotated invoice samples across 40+ document layouts — scanned PDFs, email attachments, EDI and structured XML. Achieved 98.5% field extraction accuracy across all major invoice types.
Built a reconciliation engine that automatically matches extracted invoice data against the client's ERP purchase orders and goods receipt records — flagging discrepancies in quantity, price or vendor for exception handling.
Designed an ML classifier that categorises exceptions by root cause — price variance, PO mismatch, duplicate, vendor error — and routes each to the correct team via a smart inbox with recommended resolution actions.
Built native connectors for SAP S/4HANA and Oracle Fusion, with full audit logging of every AI decision, extraction confidence score and human override — meeting SOX and GAAP compliance requirements.
Real impact, measured and validated.
Every tool chosen deliberately for the problem at hand.
We processed 3,000 invoices in a single day during month-end close. That would have taken a team of eight people an entire week — and the accuracy is genuinely better than what we achieved manually.