A document is not a record until the information is understood.
Documents arrive in countless formats. The challenge is not merely extracting words. It is deciding which information is reliable enough to enter a business system, preserving the evidence and knowing who should review uncertainty.
Which values are reliable enough to become a business record?
Preserve field-level evidence, apply deterministic checks and route uncertainty to review before any consequential downstream action.
Extract the value. Preserve the reason to believe it.
Synthetic quotation · sample A
Item: service component
Quantity: 12
Unit price: 40.00
Line total: 520.00
Currency and tax treatment: not stated
Synthetic document specimen
The proposed record
Quantity
12Quotation · item line
Candidate value with source retained
Line total
520.00Quotation · total line
Conflict: 12 × 40.00 = 480.00 before unspecified adjustments
Currency
Not establishedNo explicit source value
Missing required context; do not infer
Before a value becomes a record
- Keep original and document version
- Link every proposed field to its evidence
- Hold arithmetic conflicts for review
- Write only after required validation and approval
- Reconcile the destination and preserve corrections
Standard forms may already have a standard solution.
Managed document products or ERP modules may be sufficient. Custom engineering earns its place through distinctive document families, business rules, review experience or downstream contracts.
Test the documents that create manual work.
- Poor scans, unusual tables, duplicates and missing pages
- Field-level quality and review workload
- A corrected field traced through approval and reconciliation
The complete document intelligence argument
For CTO, CIO, CPO, COO
The situation behind the request
A business receives supplier quotations, customer orders and contract changes through different channels. Staff copy fields into an ERP and check exceptions manually. A compelling extraction demo may process the easiest PDFs but fail precisely where the costly work begins.
Where the obvious solution falls short
OCR mistakes, ambiguous tables, contradictory line items and missing pages can turn an automated write-back into a data integrity problem. Documents also contain sensitive information and adversarial text. Human review needs to be designed as a first-class state, not an afterthought.
How we would approach the work
We map document families, expected fields, confidence criteria, validation sources, review paths and downstream actions. Use deterministic validation where possible. Store provenance for every extracted value so operators can inspect its source.
What the engagement may include
- Intake channels, file handling controls, document versioning and classification.
- Extraction with visible evidence links and data-field validation.
- Exception queue, review decisions, corrections and approval audit.
- Integration to records system with reconciliation and reprocessing.
The choices worth making explicitly
Managed document AI products and ERP modules may already solve standard documents. Custom engineering is worthwhile when layouts, business rules, approval UX or cross-system data contracts are distinctive.
What we would test before calling it ready
Evaluate on a representative document set containing poor scans, missing items, duplicates, unusual tables and contradictory figures. Measure field-level accuracy and manual-review load, not only whether a summary reads well.
A sensible first step
Select one document class and one downstream action, establish a baseline and test end-to-end correctness. Scale formats only after the exception workload is understood.