Contact sales VERIXA · IDP
Entering data from different documents often means retyping fields and checking every line. Verixa Multimodal IDP extracts fields and tables and displays the results beside the original. Staff can correct discrepancies and export the reviewed data for the next task.
Multimodal IDP Match business records
When the same part appears in several documents, compare the source, approved revision and extracted data.FOXIT IDP · PRODUCT INTRODUCTION
See how Foxit IDP extracts document fields and line items, using a purchase order as an example, so teams can review the results against the original.
Orders, engineering changes and quality records all depend on accurate source data. IDP retains the original alongside extracted fields. Comparing documents before and after an engineering change helps staff identify differences that need review.
An engineering change notice often needs to be checked against several specifications and bills of materials. IDP extracts part numbers, revisions and specification fields, with source references for engineering and purchasing to verify what changed.
Extract inspection items, measurements and results from supplier reports and incoming inspection forms. Check exceptions against the original record before consolidating results.
Extract parties, line items, amounts and key terms from varied business-document layouts. Reduce rekeying and focus people on discrepancies, exceptions and higher-value judgment.
Display an uploaded order, specification or inspection report alongside its extracted fields and tables. Staff can correct discrepancies and assess part numbers, revisions and business rules before passing data to ERP or MES.
The purchase order says 120. IDP read 12. Compare the two columns and correct the quantity before exporting.
Interactive example · sample data
Source document
Extracted data
Ingest order documents from designated sources for extraction and review.
PO-2026-1048.pdf
Extract order identifiers, items and quantities. The sample source quantity is 120, while extraction returns 12 and requires human review.
Reviewers verify evidence, correct exceptions and approve output.
Quantity discrepancy: source value 120; extracted value 12. Human review is required.
Reviewed data is available for export or project-scoped integration.
{
"order_id": "PO-2026-1048",
"quantity": 12,
"amount": 3000,
"review_status": "pending"
}Quantity discrepancy: source value 120; extracted value 12. Human review is required.
Display source documents alongside extracted fields for discrepancy review. The fixed example demonstrates correction of quantity 12 to the source value 120 and subsequent reviewed output.
Export reviewed data or integrate it with ERP, MES or PLM through project-scoped RESTful APIs. Field mappings, validation rules, CSV/Excel conversion and error handling are designed and tested against target interfaces.
Capture documents → Match business records → Review and use
Check the fields and source, then match the company part number, revision and applicable rules so the owner can decide what to do next.
Extract fields and line items from specifications, tables, business documents and inspection reports. Keep the source for review.
Match extracted data to part numbers, approved revisions and business rules in ERP, MES or PLM.
Engineering and purchasing can use approved part numbers and revisions to find related documents and assess a change. The responsible people decide when to update a rule or approve a revision.
Operational feedback → Case-based validation → Approved knowledge and rule updates
Bring a few typical files and a real question. Review extracted values, revision differences and exceptions with an engineer.
A correct part number is only the beginning. Compare the names, revisions and rules used in specifications, bills of materials and ERP to identify what changed and which records need attention.
VERIXA / DOCUMENT AUTOMATION
Verixa desktop AI workstation
Expand applications while retaining control of enterprise data. Assess existing servers, GB10, GPU servers or dedicated compute, with processing locations, external connections and access controls aligned to security requirements.
Provide representative documents and exceptions; define target fields, data quality and acceptance measures.
Test extraction and analyze field accuracy, human corrections and processing time.
Assess existing equipment, GB10 or GPU servers; define security requirements, integration scope and delivery schedule.
Assess existing infrastructure against model size, workload and compatibility, with GB10, GPU servers or dedicated compute as alternatives.
Test field extraction, exception rates and correction effort using representative samples. Unreadable information requires a usable source rather than model inference.
Define processing and storage locations, external APIs, remote support and access permissions. Private deployment does not imply that every component operates offline by default.
Validate extraction and review quality using actual single-language or multilingual samples. Website languages do not constitute document-processing language support.
We test representative samples, then agree on hardware timing, security requirements and integration scope with you before scheduling delivery and acceptance.