Finance and procurement teams spend time reading purchase orders and invoices, keying fields into ERP systems, and resolving mismatches. Layout changes, complex line items, and handwritten notes drive manual checks that increase cost and cycle time while adding risk of overbilling and compliance issues.
AI extracts and structures PO data, validates invoices against PO terms, and routes only true exceptions for review. Teams gain faster throughput, higher accuracy against SLA targets, and clean audit trails without changing vendor document formats.
This total cost of ownership calculator helps you evaluate the true ROI of the category using modern AI powered alternatives over traditional OCR based solutions.
Key Use Cases:
PO Based Data Validation
Matches invoice details against purchase orders and verifies items, quantities, and pricing to prevent overbilling.
Automated PO Data Extraction
Captures PO number, buyer and supplier details, line items, quantities, pricing, terms, and delivery dates from documents.
Line Item Parsing and Structuring
Extracts product or service descriptions, SKUs, quantities, unit prices, discounts, taxes, and delivery schedules into a structured format for ERP systems.
Handwritten and Low Quality Document Recognition
Uses handwriting recognition and image enhancement to extract data from handwritten notes, annotations, or low resolution scans.
Start by selecting a typical scenario or adjust the baseline details to reflect your exact needs. The calculator will update automatically.
If your organization relies on legacy OCR for PO data capture, accuracy typically caps at about 80%. OCR struggles with handwritten invoices, poor scans, and complex tables, forcing analysts to manually recheck and re-enter data. This results in higher costs and slower processing cycles.
Modern AI that combines OCR with natural language processing and vision models lifts accuracy to approximately 92% accuracy ±2%, generalizes better across layouts. However generic AI cannot pinpoint which fields are wrong with certainty, so teams still verify low confidence outputs and critical fields to protect financial controls.
Your AI investment isn’t delivering expected savings. This may indicate inefficiencies or incorrect assumptions in your current workflow.
Contact us to identify more optimization opportunities.
Contact Us