Typical inputs
- PDFs and scans
- Office documents
- Email attachments
- Reference data
- Validation and retention rules
Workflow automation / Documents
Move document data into useful work without retyping it.
Document processing automation receives business documents, identifies the document type, extracts only the agreed information, validates it against rules or source systems and sends uncertain cases to a person. The aim is a traceable operational record, not an unreviewed AI summary.
Who it is for
A practical fit when the process already matters.
This service suits finance, operations, procurement, recruitment and professional-services teams handling recurring PDFs, scans, spreadsheets or forms. The strongest cases have a stable destination, repeatable fields and enough volume or error cost to justify integration and monitoring.
Current-state signals
- Staff repeatedly open documents and type the same fields into another system.
- Files arrive through several channels and are named or stored inconsistently.
- Missing values and exceptions are noticed only after downstream work has started.
- Existing OCR produces text but not a usable, validated business record.
- Sensitive documents are being pasted into tools without a documented data path.
Example workflow
What the end-to-end path can look like.
For an invoice or supplier form, the workflow stores the original, identifies the document type and extracts a defined field set. It checks totals, formats, supplier identifiers and duplicates against known data. Valid records move to the target system; uncertain values stay beside the source for a reviewer.
- 01
Receive
Collect files from an inbox, upload area, shared drive or existing business system.
- 02
Identify
Determine the document type and select the correct extraction and validation rules.
- 03
Extract
Read the agreed fields, preserving page or source references where the tooling allows.
- 04
Validate
Check formats, totals, identifiers, duplicates and required values against explicit rules.
- 05
Review
Present low-confidence or policy-sensitive fields beside the source document for a person.
- 06
Route
Write the approved record to the destination and store a clear processing outcome.
Human controls
Automation does the repeated work. People keep the decision.
- Extraction confidence is not treated as approval; business thresholds define what needs review.
- The original document remains available so a reviewer can verify every important field.
- Payments, legal conclusions, hiring decisions and other consequential actions stay outside the automated step.
- Retention, access and provider choices are scoped around the document sensitivity and your policies.
Definition block
Inputs and outputs
The scope names what enters the workflow, what leaves it and which system remains the source of truth.
Expected outputs
- Document classification
- Selected structured fields
- Validation status
- Review queue
- Approved system record and audit trail
Measurable outcomes
Measure the process before claiming the improvement.
Quality is measured on your representative documents and field definitions. A sample that excludes difficult pages or suppliers is not an honest production benchmark.
- 01
Median handling time per document type
- 02
Field-level accuracy after human review
- 03
Straight-through processing rate for low-risk records
- 04
Exception rate by reason, document source and template
- 05
Rework caused by missing, duplicated or incorrectly entered data
What is excluded
Clear boundaries make the workflow safer.
This is not autonomous legal interpretation, payment approval, financial advice or a promise that every scan can be read reliably. Handwriting, damaged files and changing templates may require separate treatment. If a fixed template or native import already solves the task, a model adds needless cost and risk.
Decision criteria
Choose the smallest approach that solves the problem.
| Direction | When it fits |
|---|---|
| Use document processing automation | The field set and destination are stable, volume is meaningful and people already follow a recognisable review process. |
| Use deterministic parsing | Documents are machine-generated with a consistent layout or structured export that can be read without AI. |
| Run a benchmark first | Document quality varies, language is complex or the cost of a wrong field is high. |
| Do not automate yet | The required fields, retention policy or human approval owner are still undecided. |
Delivery approach
A bounded path from evidence to handover.
- 01
Sample
Review representative documents, including poor-quality and unusual examples, plus the destination fields.
- 02
Define
Agree field definitions, validation rules, confidence thresholds, retention and review ownership.
- 03
Prototype
Test the smallest viable extraction path before committing to the full integration.
- 04
Integrate
Connect intake, storage, review and destination systems with explicit failure states.
- 05
Handover
Document the workflow, thresholds, known limits and process for adding new document variants.
Common questions
Questions before scoping document processing automation
Is this the same as OCR?
OCR turns an image into text. A complete workflow also classifies the file, maps specific fields, validates them, handles exceptions and updates the business system.
Can the documents remain in Europe?
Provider and hosting choices are assessed from your sensitivity and contractual requirements. The architecture is documented before implementation; no geography is assumed from a product label alone.
How is accuracy tested?
We define fields and expected values, build a representative held-out set and report results by field and document type, including failures rather than only an average score.
Can it process contracts?
It can extract agreed clauses or metadata for review. It does not replace counsel or make legal conclusions, and complex contract analysis may need a separate architecture review.
Bring us the workflow, not a finished specification.
Describe what happens today, the systems involved and what a better outcome would look like. A short outline is enough.