The difficult invoice is not the clean example in a demo. It is the scan with a faint tax amount, the supplier’s new layout, the duplicate attachment, or the line item that does not match the purchase order. A document system has to recognize those situations before they become business records.
We separate recognition, interpretation, validation, and approval. This makes it possible to improve one component without hiding its uncertainty inside the next, and gives reviewers a concrete reason why a document needs attention.
Selected for the workload—not prescribed as a single mandatory stack. Explore the technology ecosystem ↗
The invoice with a plausible but wrong total
Suppose OCR reads a subtotal correctly but misses a negative adjustment. The model produces a well-formed record with a total that looks reasonable. Schema validation passes because the field is a number; the business record is still wrong.
A validation layer reconciles line items, tax, discounts, currency, and the document total. It also checks supplier identity and duplicate fingerprints, and compares the record with purchase-order data where available. A mismatch sends the invoice to a review queue rather than into a payment workflow.
A reviewer sees the proposed values beside source evidence and can correct the adjustment. The correction becomes a labeled evaluation example; it is not automatically fed into training without checking quality and data permissions.
Document + target schema + business reference data
Validated draft record or an evidence-linked exception
Design the exception queue before the happy path
A useful review queue groups exceptions by business consequence: missing identity, inconsistent amounts, unreadable fields, or an unmatched purchase order. Reviewers need enough context to resolve the issue without opening several unrelated applications.
Keep the extracted record, source locations, validation results, and approval history separate. A reprocessing run can then be compared with the prior extraction without overwriting the record that a person already approved. Downstream writes should carry an idempotency key so retries do not create duplicate entries.
Understand the document mix
We separate native text, scanned pages, tables, and images before selecting an extraction approach. Layout, language, handwriting, and source quality affect what should be automated and what needs a review queue.
Define a business schema
The output must fit your workflow: dates, amounts, identifiers, line items, and relationships. We design structured fields, required values, and normalization rules, then retain references to the source so reviewers can verify important information.
Combine AI with deterministic checks
Models can propose an extraction; code should check totals, formats, duplicates, and record matches. Low-confidence fields and conflicting evidence are routed to a person rather than silently written into an operational system.
Connect the next step
Approved data can feed ERP, CRM, case management, or a specialist application. Idempotent imports, audit records, retry handling, and clear ownership keep a document pipeline from becoming a new source of duplicate or incomplete work.
record_status: draft
checks:
supplier_match: required
totals_reconcile: required
duplicate_check: required
on_exception: human_review
on_approval: idempotent_importChoose the approach for the constraint
| When this matters | An approach to consider | What not to assume |
|---|---|---|
| Documents contain native text | Preserve text and layout before using OCR | Do not discard structure by treating every page as a photograph. |
| Critical fields are uncertain | Field-level review with source evidence | A document-wide confidence score hides individual errors. |
| Imports can be retried | Idempotent writes and reconciliation | Extraction success is not proof that the import completed. |
The boundary we keep explicit
An extracted contract clause is not legal advice. An extracted financial value is not an approved transaction. Domain review and authorization remain explicit workflow steps.
What a useful evaluation should reveal
Evaluate this workload against representative examples and agreed consequences—not just a convincing response. The review should make these dimensions visible:
- Field-level accuracy by document type
- Exception and review rate
- Duplicate prevention
- End-to-end processing time
Where this approach fits
- Invoice intake and purchase-order matching
- Contract metadata and clause review assistance
- Form processing and operational report extraction
A considered first step
Use a permissioned, representative sample of documents—including difficult examples—and agree on the output schema and validation rules. Test one intake-to-review workflow before connecting production writes.
Serving enterprise teams in California, Atlanta, Georgia, and across the United States.
Discuss your requirementsQuestions worth resolving
Will one model handle every document?
Not always. A mixed pipeline may use OCR, layout-aware extraction, language models, and rules. We choose the components based on the documents and required accuracy.
Can users check the extracted values?
Yes. A review interface can place source pages beside proposed fields, highlight exceptions, and capture corrections for evaluation and future improvement.