Solution · 04
Document Processing Automation
Process invoices, orders, contracts and forms with less typing.
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
- 01
Choose one document type and owner
- 02
Agree fields, rules and approval points
- 03
Build capture, routing and system updates
- 04
Test with real documents
- 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
- 01
Choose one document flow
Agree document type, owner, fields, source and approved destination.
- 02
Define rules and exceptions
Document validation rules, required evidence and review triggers.
- 03
Extract and validate
Capture agreed fields and check them against format and business rules.
- 04
Review uncertainty
Send unreadable, missing or conflicting cases to the responsible person.
- 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.
Services combined
Built from our core capabilities
Measures of value
How we measure success
Lead industries
Where it fits best
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.
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03
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05
Company Knowledge Assistant
Help employees find reliable answers from company documents.
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.
Proof
Where this work paid off
Manufacturing
Cut purchasing cost surprises and document work across plants
This manufacturer had spend and supplier data trapped in plant-level systems, spreadsheets and supplier portals. Purchase teams spent days reconciling costs, tariffs changed without warning, and quality documents were processed by hand. DataplexLabs connected the sources, built a unified purchasing view and put document extraction in place with a human review queue.
Read the case study →Import & Export
See landed cost before buying and cut import cost surprises
Landed cost was calculated after goods arrived, so buyers made decisions with the wrong number. Duties, freight and routing changes created unexpected invoices. DataplexLabs modelled total import cost into the buying workflow and added alerts on variance.
Read the case study →Logistics & Freight
Reduce document work and handle shipping exceptions faster
Shipping and customs documents were typed in by hand, exceptions were found late, and customers asked for updates the operations team could not answer quickly. DataplexLabs automated document capture, matched shipments to documents and added status alerts.
Read the case study →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.