Request an AI use-case review to see how AI Integration Services Group delivers managed extraction and abstraction for enterprise operations.
Transform contracts, claims, invoices, files, PDFs, emails, and legacy records into structured, governed, business-ready data.
Unlock the data trapped in your documents. Our managed AI delivery extracts and structures information from any format, making it immediately actionable across your enterprise.
Enterprises are drowning in documents—contracts, claims, invoices, applications, and legacy records that contain critical data but remain inaccessible in their original format. Manual extraction is slow, error-prone, and doesn't scale.
Teams spend thousands of hours typing data from PDFs into systems—work that machines could do in seconds with higher accuracy.
Manual keying introduces 2–4% error rates. At scale, even small error percentages create large downstream problems—incorrect payments, compliance violations, customer disputes.
Claims, contracts, and loan files sit in queues for days while staff manually review documents before processing can begin.
AI-powered document intelligence transforms unstructured files into structured, actionable data—ready for downstream workflows.
Extract parties, dates, values, termination clauses, SLAs, and renewal terms—structured and ready for contract management systems.
Pull vendor, amounts, PO numbers, GL codes, and approval statuses from AP invoices—feeding automated payment workflows.
Extract incident details, coverage elements, claimant information, and loss documentation from FNOL forms and supporting documents.
Capture applicant information, income documentation, employment history, and supporting evidence from loan or benefit applications.
Parse email threads, attachments, and client communications to build structured case histories and compliance records.
Convert decades of scanned documents, paper files, and archived records into structured digital data ready for modern systems.
A governed extraction pipeline that transforms document chaos into structured, actionable data.
Ingest documents via email, file upload, API, or system integration. Handle PDFs, images, scanned documents, emails, and legacy formats.
Auto-classify document type, determine applicable extraction schema, and route to the correct processing pipeline.
Pull structured fields with confidence scoring. Flag low-confidence extractions for human review. Cross-validate against known data.
Write structured data to downstream systems, trigger workflows, and log audit trails. Retain original documents with extraction metadata.
Document extraction at enterprise scale requires controls that ensure accuracy, compliance, and traceability—without creating new bottlenecks.
Extractions below confidence thresholds automatically flag for human review. Thresholds are configurable by document type and risk level—higher risk documents get tighter controls.
Every extraction logs the source document, timestamp, AI model version, confidence score, and reviewer actions. Retain for compliance and dispute resolution.
Documents that can't be automatically processed route to exception queues with full context. Reviewers can correct, override, or escalate—decisions are logged.
A regional insurer deploys document extraction for their FNOL (first notice of loss) process. Incoming claim documents—police reports, repair estimates, medical records—are automatically extracted and structured before adjuster review. Extraction accuracy reaches 97%, reducing adjuster prep time by 70% and cutting average claim intake from 3 days to 4 hours.
Track percentage of extractions that pass validation without human correction. Target: 95%+ for structured documents, 90%+ for complex legacy files.
Measure time from document receipt to structured data availability. Target: 60–80% reduction in manual processing time per document type.
Calculate fully-loaded cost of manual extraction vs. AI-assisted extraction—including staff time, error correction, and rework.
Track percentage of documents that auto-process without routing to human review. Higher rates indicate better model performance and fewer bottlenecks.
FNOL intake, supporting document extraction, coverage validation, and claims prep for adjusters
Invoice extraction, PO matching, GL coding, and three-way match validation for AP teams
Application package extraction, income verification, title abstraction, and closing document preparation
Lease terms, vendor commitments, SLA clauses, and renewal dates extracted for contract management systems
Audit logs, policy acknowledgments, and regulatory submissions extracted and structured for compliance reporting
Decades of archived records, paper files, and scanned documents converted to structured digital format