Traditional accounts-payable systems are built for recording transactions, not for resolving anomalies. Every month, finance teams must manually match supplier invoices with purchase orders, goods receipts, and internal payment ledgers. Any mismatch—whether caused by late submissions, incorrect identifiers, duplicate documents, or rounded totals—delays close and locks up working capital.
At ShipLabs, we designed a deterministic multi-agent pipeline to solve this. Instead of simple keyword matching or rigid spreadsheet formulas, the system utilizes a combination of advanced optical character recognition (OCR) and an agentic decision tree to perform deep semantic reconciliation.
The ingestion layer handles diverse, noisy data sources: scanned thermal paper receipts, mobile camera captures from transport partners, and standard PDF invoices. It extracts tabular data with 99.8% precision, converting unstructured layouts into standardized transaction schemas.
Once normalized, the matching engine runs an agentic reconciliation loop. It handles edge cases like split payments, misspelled supplier names, duplicate invoice numbers, and posting-date shifts across accounting periods. If the system detects a mismatch, it doesn't just raise a flag—it initiates resolution. The system drafts a context-aware email to the supplier detailing the exact mismatch, transaction date, and corrective action required.
By translating probabilistic language-model understanding into highly disciplined, deterministic business workflows, our clients are reclaiming locked capital without human intervention.