Document intelligence for document-heavy operations
Systems that read the documents your operation already depends on: invoices, statements, contracts, intake forms, and correspondence. Extraction is only useful when the exceptions reach a person who can fix them.
- Documents
- Classify
- Extract
- Validate
- Human review
- System of record
The problem
Document work scales with headcount. People open attachments, retype fields, chase missing information, and route exceptions from memory. Volume grows, accuracy depends on attention, and the audit trail lives in an inbox.
What we build
- Ingestion from email, scanners, and shared drives
- OCR and multimodal extraction for structured and semi-structured documents
- Classification and routing by document type
- Confidence thresholds and review queues for uncertain fields
- Validation against accounting or operational systems
- Source-linked evidence for every extracted value
Systems involved
- Email and document stores
- Accounting systems and ERPs
- Databases and workflow tools
- Storage and content platforms
- Model providers behind an adapter layer
Controls and validation
Extraction is evaluated on samples of your documents before it is trusted. Confidence thresholds route uncertain documents to review, and every field keeps a link to its source page or region so a person can verify it in seconds rather than hunting through a folder.
Implementation examples
- Invoice intake that extracts header and line data, validates totals, and queues mismatches
- Client onboarding that classifies submitted documents and routes missing items to a checklist
- Statement processing that converts PDFs into structured transactions with review of low-confidence rows
- A correspondence workflow that classifies incoming mail and records the routing decision
What this does not promise: No extraction is presented as perfect. Exceptions, low-confidence fields, and unusual formats are handled by people by design, and the review queue is part of the system.
Questions buyers ask
Which document types can this handle?
Invoices, statements, contracts, intake forms, correspondence, and other recurring business documents. The first step is a sample of your actual files, including the messy ones, because document variability is where these systems succeed or fail.
What accuracy can we expect?
No accuracy number is promised before evaluation. Extraction is measured against your documents with a labeled sample, and the review rate is set from that evidence. Where the format is genuinely ambiguous, the system routes to a person rather than guessing.
What happens to uncertain fields?
They go to a review queue with the source visible beside the extracted value. The reviewer corrects or confirms, and the decision is recorded. Over time, corrections become training and evaluation material for the next iteration.
Can this run without sending documents to an external model?
Where the data classification or policy requires it, extraction can run against models in your own cloud environment or a local runtime. That is a scoping decision made with your constraints in view, not an afterthought.