Cross-industry · Document workflow
Intelligent Document Extraction & Validation Agent
- Who this is for
- Operations, back-office, and workflow-owner teams whose next step depends on structured evidence from mixed business documents.
- Outcome
- Turn configured document families into source-linked structured data, validated against related evidence, with exceptions routed for human review.
- Boundary
- The agent extracts, validates, and routes. Reviewers accept, correct, or reject each field. Downstream systems act on the approved record.
Documents are the last unstructured layer inside every enterprise workflow.
Mixed packs — PDFs, scans, forms, medical reports, invoices, contracts, KYC packets — are the input to nearly every business process. Stable, well-formed documents extract cleanly with deterministic rules; the interesting work is everything else.
The interesting work is choosing the right processor for each document, following references between pages, resolving conflicts between sources, chasing missing evidence, and giving a reviewer a legible summary of what was extracted and what was not.
End-to-end extraction flow
Seven coordinated steps. Deterministic extraction runs where the schema is stable; agents handle mixed packs, evidence retrieval, conflict resolution, and follow-up.
- Ingest from configured sources (mailbox, portal, shared drive, S3 bucket, API).
- Record source, received-at, and handler assignment against each pack.
- Split a mixed pack into individual documents.
- Classify each document into a configured document family; low-confidence pieces route to a reviewer.
- Use deterministic extractors where the form is stable.
- Use agentic extraction where layout varies, evidence spans pages, or references cross documents.
Example reviewer states on this step
Invoice totalVerified
- Every extracted field carries a link to the exact page, region, or table cell used.
- Fields that cannot be located in a source page are marked missing rather than infilled.
- Apply the configured cross-field, cross-document, and cross-system rules.
- Flag conflicts and evidence gaps with the shared reviewer copy.
Example reviewer states on this step
Vendor nameConflicting Bank accountMissing
- A reviewer sees the flagged fields, the source page, and the reason.
- Corrections are recorded and become training signal for the next iteration.
- The approved record is emitted to the configured downstream system (claims, KYC, contract, invoice, or custom workflow).
- The complete evidence trail travels with the record.
Which decisions belong to which actor
Deterministic where the schema is stable, agentic where evidence must be located and reconciled, human where a judgement is required.
Stable schemas, format checks, and configured cross-field rules.
- Format and presence checks on extracted fields.
- Configured cross-field and cross-document consistency rules.
- Ingestion, deduplication, and downstream handoff.
Suggestions a human confirms.
- Document family classification.
- Layout hints for extractor selection.
Multi-document coordination.
- Selecting the right processor for each document in a mixed pack.
- Retrieving related evidence to validate a field.
- Preparing conflict summaries for the reviewer.
Anything that requires judgement or accepts liability.
- Accepting, correcting, or rejecting flagged fields.
- Choosing which source to trust when they disagree.
- Deciding when a pack is complete enough to hand off.
Human boundaries
The reviewer decides what is trustworthy. The agent prepares — it does not accept liability.
Operations reviewer
- Accept, correct, or reject each flagged field.
- Decide when a pack is ready for downstream handoff.
Workflow owner
- Approve the document families and schemas that are in scope.
- Approve the configured cross-validation rules.
Reviewer states you'll see on a record
The same four-state vocabulary as the rest of the platform. Every field carries one state, a source link, and a short reason.
Invoice totalVerified Handwritten referenceNeeds review Bank accountMissing Vendor nameConflicting
What we measure
The measures we agree with the customer during Discover and Bound. Public numbers are attested per customer before they appear.
Field-level acceptance rate against a customer-reviewed ground truth
Baseline captured in Discover; target agreed in Bound; observed value tracked from Prove onwards.
Reviewer touch rate per pack
How often a pack needs a person, and on which fields.
Time from ingest to reviewer-ready record
End-to-end latency including retries and follow-up.
Correction feedback loop
Reviewer corrections feed into the next iteration; we track whether corrections reduce future touches.
How we prove it before scaling
The first commercial unit is a Workflow Proof: one configured document family, one user group, one measurable outcome, and one downstream handoff. Only after the agreed thresholds are met do we extend to more document families, regions, or systems.
Frequently asked
- Does the agent handle every document type?
- No. The agent extracts from configured document families with an agreed schema. Formats, languages, layouts, and evidence quality vary and are attested per customer during Discover and Bound.
- How does this differ from a generic OCR pipeline?
- OCR reads pixels into text. This workflow selects the right processor per document, links every extracted field to a source, cross-validates against related evidence, and routes exceptions to a reviewer with the reason.
- Can we plug this into an existing workflow?
- Downstream integrations are configured per customer to the systems already in place. The approved record and evidence trail are emitted in an agreed schema.
- What is the human's role?
- The reviewer accepts, corrects, or rejects flagged fields; decides which source to trust when they disagree; and decides when a pack is complete enough for downstream handoff.
Ready to review a document workflow with us?
Pick one document family, one user group, and one outcome. We start there.