The manual entry problem across document types
- Every document type requires operators who know which AS/400 (IBM i) screens to navigate and which fields to populate
- Document layouts vary by vendor, customer, and region - traditional OCR needs a new template for each variation
- Cross-training operators on multiple document types takes months and increases error rates during the learning period
- Scan-to-data workflows stall when document quality is poor - faded faxes, angled photos, handwritten notes
Supported document types
Invoices
Vendor name, invoice number, line items, amounts, tax, PO reference, payment terms
Purchase Orders
PO number, vendor, item descriptions, quantities, unit prices, delivery dates, ship-to address
Bills of Lading
BOL number, carrier name, weight, piece count, origin/destination, SKU, freight class
Work Orders
WO number, equipment ID, repair codes, technician name, service dates, parts used, labor hours
Insurance Claims
Claim number, policy number, date of loss, loss description, claim amount, claimant name, adjuster
Patient & Medical Forms
Patient ID, diagnosis codes (ICD-10), procedure codes (CPT), provider NPI, dates of service, billing amounts
No templates required
Unlike traditional OCR systems that need a pre-configured template for every document layout, legacy bridge uses AI vision to understand documents semantically. It reads invoices from any vendor, purchase orders in any format, and claims in any layout - without setup per document type. When a new vendor sends invoices in a different format, legacy bridge handles it automatically.
Why it works across document types
Any format
PDF, scanned image, photo, fax - legacy bridge processes documents however they arrive. No format conversion or pre-processing needed.
Any layout
Structured tables, unstructured paragraphs, handwritten notes, mixed formats. AI vision reads the document as a human would, not as a template matcher.
Field-level confidence
Every extracted value includes a confidence score. Low-confidence fields are flagged for human review before entry, preventing uncertain data from reaching your AS/400 (IBM i).
Continuous learning
Extraction accuracy improves over time as the system processes more documents from your specific vendors and document sources. Corrections feed back into future processing.
