DATA & DOCUMENT AUTOMATION

Turn Documents and Unstructured Data Into Usable Business Workflows

Extract, classify, summarise, validate and route information from PDFs, emails, forms, spreadsheets, scans and business documents so people spend less time retyping and sorting information by hand.

Document automation is quoted by scope. Accuracy depends on document quality, layout consistency, required fields, language, volume, validation rules and the systems that need to receive the result.

Typical Document Workflows

PDF data extraction
Email classification
Form processing
Invoice / receipt workflows
Application processing
Document summarisation
File routing
Spreadsheet cleanup
Structured data creation

Work With the Documents You Already Receive

The input can come from many sources. The first step is understanding the document quality, structure and what information the business actually needs from it.

PDFs

Extract text, fields, tables or meaning from digital documents and reports.

Scanned Documents

Use OCR or document-recognition steps where the source is an image or scan rather than selectable text.

Emails

Classify incoming messages, extract key information and route them into the right workflow.

Forms

Turn submitted answers into structured records, approvals, tasks or downstream actions.

Spreadsheets

Clean, reshape, classify or move data from spreadsheet-based processes into more structured workflows.

Business Documents

Process applications, statements, orders, receipts, contracts, reports or other recurring document types.

What Can Be Automated?

A document workflow can combine extraction, AI interpretation, business rules and system actions in one controlled process.

Text Extraction

Read available text from digital documents or OCR-enabled scans.

Field Extraction

Pull specific values such as names, dates, totals, references or other required fields.

Classification

Identify document type, topic, category, intent or routing destination.

Summarisation

Produce concise summaries of longer documents, email threads or records.

Validation

Check required fields, formats or business rules before the result moves downstream.

Routing

Send documents or extracted data to the correct folder, queue, person or business system.

Structured Output

Turn unstructured information into JSON, database records, spreadsheet rows or other predictable formats.

Record Creation

Create or update CRM, database, ticketing or internal-system records from processed documents.

Human Review

Send uncertain or high-risk cases to a person instead of forcing automation to guess.

OCR and AI Solve Different Parts of the Problem

OCR is useful when information is trapped inside an image or scan. AI can then help interpret, classify, summarise or structure the text after it has been read.

OCR reads visible text from images or scans
Extraction identifies required fields
AI can interpret less predictable layouts or language
Business rules validate the result
Human review handles uncertain cases

Not Every Document Needs AI

If a document has a consistent layout and exact fields, conventional parsing or rule-based extraction can be faster, cheaper and more predictable.

AI is most useful when the document structure, language or meaning varies enough that fixed rules alone become brittle.

Accuracy Needs More Than a Good Demo

Document automation should be designed around real examples, including poor-quality inputs and exceptions — not only perfect sample files.

Source Quality

Blurry scans, poor photos and inconsistent layouts can reduce extraction reliability.

Field Confidence

Important extracted values may need confidence checks or validation rules before use.

Required Fields

The workflow should detect missing information instead of silently creating incomplete records.

Human Review

Low-confidence or high-risk cases can be routed to a person for confirmation.

Audit Trail

Important processing steps should be traceable enough to investigate errors.

Duplicate Protection

Repeated documents or events should not create accidental duplicate records where possible.

A Typical Document Automation Flow

Receive

A document, email, form, file or record enters the workflow.

Read

Text is collected directly or through OCR / document recognition.

Interpret

Fields, categories, summaries or other meaning are extracted as required.

Validate

Rules, confidence checks or human review confirm important results.

Send

The result is stored, routed, emailed or written into the next system.

Document Data Can Be Sensitive

Documents may contain personal, financial, contractual or confidential information. The project needs clear boundaries around what is processed, where it is sent and who can access the result.

Document source
Data sensitivity
Storage location
Provider accounts
Retention / logging
User permissions
Downstream destinations

Technical Controls Do Not Replace Legal Review

SiteLumo can design access controls, data flow and technical safeguards, but the client remains responsible for determining the legal, regulatory, contractual or industry-specific requirements that apply to the documents and data being processed.

Sensitive or regulated workflows may require specialist legal, security or compliance review.

What Determines the Data & Document Automation Quote?

The quote depends on document types, extraction requirements, quality, volume, integrations, validation and human-review needs.

Document Variety

One consistent template is easier than many layouts, languages and document types.

Image / Scan Quality

Low-resolution scans, handwriting or inconsistent photos can increase complexity.

Fields & Accuracy

The number of fields and the accuracy required affect validation and review needs.

Volume

Daily or monthly document volume affects architecture, provider choice and operating cost.

Integrations

CRM, databases, storage, email, accounting or custom systems may require additional connection work.

Review Logic

Confidence thresholds, human approval, exception routing and audit needs can add important scope.

Test With Real Documents Before Scaling

For document-heavy workflows, sample files are important. The scope should be based on real document variation rather than assuming every input will look like the cleanest example.

Still Processing Documents Manually?

If staff repeatedly open files, copy fields, rename documents, update spreadsheets and forward results, there may be a strong automation opportunity.

Already Have a Fragile Document Workflow?

Existing OCR, extraction or automation systems may need better validation, new document types, improved accuracy, lower cost or stronger integrations.

A Written-First Document Automation Workflow

Sample documents, screenshots, field definitions, test cases, progress updates and approvals can all be handled asynchronously in writing. Meetings are optional.

Share Real Examples

Provide representative documents and explain what people currently extract or do manually.

Define the Output

We identify required fields, categories, validation rules, review points and destination systems.

Build the Workflow

OCR, extraction, AI logic, rules and integrations are implemented around the agreed scope.

Test Difficult Cases

We test low-quality files, missing fields, unusual layouts, duplicates and uncertain results.

Launch & Improve

The workflow goes live, with later tuning, new document types and integrations handled as new scope or support.

Providers, Storage & Ownership Are Defined

OCR providers, AI services, storage, API credentials, processed data, workflow ownership, deployment and third-party fees are confirmed according to the project.

Document Workflows Change Over Time

New document layouts, providers, fields and business rules can appear later. SiteLumo can continue maintaining and improving the workflow after launch.

Data & Document Automation Questions

What kinds of documents can be automated?

Examples include PDFs, scanned forms, emails, invoices, receipts, applications, reports, spreadsheets and other recurring business documents.

What is OCR?

OCR converts visible text in an image or scan into machine-readable text. It can be one step in a broader extraction or document-processing workflow.

Do you always use AI for document extraction?

No. Fixed layouts and predictable fields may be better handled with conventional extraction or rules. AI is useful when layouts or language vary and interpretation is required.

Can you extract fields into Excel, CRM or a database?

Potentially. Extracted data can be structured and written to spreadsheets, databases, CRM, ticketing systems or custom software where suitable integrations are available.

Can the system process scanned documents?

Yes, depending on image quality and document type. Low-quality scans or handwriting may require additional testing and review.

Can you guarantee 100% extraction accuracy?

No. OCR and AI can make mistakes. Important fields may need validation, confidence thresholds or human review depending on the risk.

Can documents be routed automatically after processing?

Yes. Rules can send files or records to folders, users, queues or other systems based on extracted information or document category.

Will there be ongoing costs?

Possibly. OCR, AI, storage, automation platforms and third-party APIs may charge usage or subscription fees separate from SiteLumo project fees.

Do we need meetings?

No. SiteLumo uses a written-first workflow. Real sample documents and written field definitions are usually more useful than meetings for this type of project.

What should I send for a quote?

Send representative document samples, explain which fields or results you need, the current manual process, expected volume, accuracy requirements and where the processed data should go.

Have Documents Your Team Processes by Hand?

Send representative examples and explain what people currently read, copy, classify or enter into another system. We’ll reply in writing with the questions needed to define the automation scope.