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Solution · 04

Document Processing Automation

Process invoices, orders, contracts and forms with less typing.

Invoices and contractsfields readSupplierAmountDue dateTermsStructured recordPaperwork turned into records your systems can use

The problem

Why this exists

Staff retype the same fields from documents into systems, and mistakes are only found later.

  • Staff retype data from documents into systems.
  • Approvals wait in inboxes.
  • Errors are found after payment or shipment.
  • Exceptions are handled differently by each person.

What it does

  • Document sorting and field capture
  • Business rules, routing and approvals
  • Exception queue for uncertain cases
  • System updates with a full audit log

How it works

The steps we run

  1. 01

    Choose one document type and owner

  2. 02

    Agree fields, rules and approval points

  3. 03

    Build capture, routing and system updates

  4. 04

    Test with real documents

  5. 05

    Launch, monitor and improve

Process flow

From received document to traceable update

The supported workflow receives an approved document, classifies it, extracts agreed fields, validates those fields, sends exceptions for review, updates the approved destination and keeps an audit record of the result.

  • Document received
  • Classification
  • Data extraction
  • Validation
  • Exception review
  • Approved system update
  • Audit trail

Human review

People remain responsible for uncertain cases

Human review is useful when a field is missing, evidence conflicts, a document cannot be read reliably or a business rule identifies an exception. The aim is not to claim complete automation. It is to let routine records move consistently while giving reviewers the source document, extracted values and reason for the exception.

Methodology

How the work is structured

  1. 01

    Choose one document flow

    Agree document type, owner, fields, source and approved destination.

  2. 02

    Define rules and exceptions

    Document validation rules, required evidence and review triggers.

  3. 03

    Extract and validate

    Capture agreed fields and check them against format and business rules.

  4. 04

    Review uncertainty

    Send unreadable, missing or conflicting cases to the responsible person.

  5. 05

    Update and record

    Apply approved updates and retain the relevant audit trail.

How we measure success

Agree the measures before delivery starts

These are measurement categories, not performance claims. The relevant definitions and acceptance criteria are agreed for each engagement.

Completeness
Required fields captured or explicitly flagged as missing.
Exception rate
Documents routed to review under the agreed rules.
Accepted and rejected records
Clear disposition after automated checks and human review.
Output consistency
Use of the agreed structure and business rules across documents.

Measures of value

How we measure success

Cycle timeManual touchesError rateException rateCost per document

See what document processing automation would look like for you

Tell us the problem, the systems and the target. We come back within one business day with scope, first result and timing.

FAQ

Document Processing Automation questions, answered

How this solution scopes, integrates, and proves value.

It is a service that reads invoices, orders, contracts or forms, extracts the fields you need, applies your business rules, and updates your systems without staff retyping data. We start with one document type, agree the fields and approval points, build the capture and routing, then test it against real documents before launch. Every record is logged so you can audit what happened. Most clients see a working first version within four to eight weeks depending on document complexity.

Common starting points are invoices, purchase orders, contracts, claims and shipping documents, chosen because they are high volume and repetitive. We agree the fields for each document type and set a confidence threshold, so the system handles routine cases and only sends unclear ones to a person. Starting with one document type keeps the project focused and lets you measure cycle time and error rate before adding more types.

The document goes to an exception queue along with the original file and the reason it was flagged, so a person can review and correct it in minutes. This keeps errors from reaching your systems while still processing the majority of documents automatically. Over time we track the exception rate and adjust the rules or capture settings to reduce how often documents need manual review.

Most clients see a working pilot on one document type within four to eight weeks, once we have sample documents, the target system access and an approval owner. We need at least a few hundred example documents to test accuracy, plus a clear description of the business rules for approval and exceptions. The pilot is measured on cycle time, manual touches and error rate before we expand to more document types.

A generic OCR tool extracts text but leaves you to build rules, routing, exception handling and system updates yourself. Document processing automation includes all of that as one service: field capture, business rules, an exception queue for unclear cases, and audited updates to your systems. This means your team gets a finished workflow rather than raw extracted text to process further. Cost per document and cycle time are tracked so you can see the ongoing return.

Next step

Discuss your use case

Bring one pain point, a data source, a workflow, a margin question. We'll come back with a focused assessment and a clear ROI hypothesis.

Get a focused reply within one business day

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Book a meeting

Talk to a data and AI lead, not a sales rep

Pick a 30-minute slot. Bring one problem. You leave with a scoped approach and a rough ROI range.

  • 30 minutes
  • Video call
  • Reply within 1 business day