Document intake
Receive files through uploads, inboxes, shared folders, scanners, or APIs and retain the original document with useful processing metadata.
OCR and document intelligence
OCR and intelligent document processing services.
Convert PDFs, scans, forms, and images into structured information your people and systems can use.
BlueMouse.ai builds document-processing pipelines that classify files, extract text and fields, validate results, and route uncertain cases for review. The goal is not simply readable text. It is reliable, traceable data that can enter a business workflow or become searchable knowledge.
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Document processingOCR and validation workflow
Review ready- Document received and classified
- Text, fields, and tables extracted
- Business rules and confidence checked
- Exceptions routed for human review
Validated data retains document provenance
What we build
Go beyond basic OCR with a complete document-processing workflow.
OCR converts pixels into characters. Intelligent document processing adds the context and controls needed to identify a document, capture the right values, check them, and deliver an approved result.
Image preparation
Handle rotation, skew, contrast, page separation, and quality checks where source condition would otherwise weaken recognition.
Classification and routing
Identify document types and direct invoices, contracts, forms, claims, reports, or correspondence to the appropriate extraction logic.
Text, field, and table extraction
Capture full text or specific values such as identifiers, dates, parties, totals, line items, clauses, and table rows.
Validation and confidence
Apply format, cross-field, reference-data, and business-rule checks while preserving confidence and provenance for each result.
System delivery
Send approved structured output to a database, review queue, business application, downstream API, or RAG knowledge pipeline.
Delivery process
Design the pipeline around your documents and acceptance rules.
Sample
Collect representative documents, including varied layouts, scan quality, languages, handwritten elements, and difficult edge cases.
Define
Agree document classes, required fields, output schema, validation rules, review thresholds, integrations, and acceptance criteria.
Build and test
Implement preprocessing, recognition, extraction, validation, and routing, then evaluate results against reviewed reference data.
Deploy and monitor
Release the workflow, track exceptions and source changes, and improve the pipeline as new formats and failure patterns appear.
Quality and evaluation
Measure the fields and decisions that matter to the workflow.
A single page-level accuracy figure can hide costly mistakes. Evaluation should reflect the specific document population, the importance of each field, and what happens when the system is uncertain.
Use representative documents and reviewed answers.
Build a reference set across common layouts and difficult cases. Compare extracted text, fields, tables, classifications, and validation results with human-reviewed values. Separate critical fields from low-impact content so acceptance reflects business risk.
Route uncertainty instead of hiding it.
Confidence, missing fields, conflicting values, unreadable pages, and failed rules can send a document to a review queue. Corrections should be recorded so teams can diagnose recurring source or model problems.
Inputs and outputs
Define what enters the pipeline and what an approved result means.
Input
Possible structured output
Invoices and purchase orders
Supplier details, dates, totals, tax, line items, references, and match status.
Contracts and policies
Parties, clauses, obligations, dates, references, sections, and searchable text.
Forms, applications, and claims
Named fields, document classes, missing information, confidence, and review status.
Reports, manuals, and scanned archives
Page text, headings, tables, figures, metadata, and chunks prepared for knowledge retrieval.
A worked example: one scanned supplier invoice
The clearest way to judge an extraction service is to see what it does with a real-looking document. This invoice is fictional; the smudged handwritten purchase order reference and the checks are typical of live invoice streams.
What was read
Supplier, invoice number, date, three line items, subtotal, VAT, and total were captured with confidence between 0.94 and 0.99. The purchase order reference, handwritten and smudged, was read as PO-7731 at 0.62. Each value carries its confidence and its position on the page.
What the checks said
Supplier matched the master record. Line amounts equalled quantity times unit price, the subtotal equalled the sum of lines, VAT was 20% of the subtotal, and the total reconciled. No duplicate in 90 days. One check failed: no open purchase order PO-7731 exists for this supplier.
What the reviewer saw
The scan crop of the handwritten reference beside the field, its confidence, and one suggestion: PO-7713, this supplier's only open order, valued at exactly the reconciled subtotal of £1,460.00. One click confirmed it. The correction was logged against the document.
What was delivered
A structured invoice record with the corrected reference, its source marked as reviewer-confirmed, every check result attached, and the original scan linked. Posted to the finance system only after that approval. Without validation the same invoice would have been rejected or posted against the wrong order.
Read the full worked example and the checks we runSecurity and deployment
Process documents within a boundary that fits their sensitivity.
Controlled document access
Design authentication, role-based access, retention, logging, and separation around originals, extracted data, corrections, and exports.
Review security and governanceFlexible processing location
Combine managed OCR services, private cloud components, on-premise models, or hybrid processing based on data residency and capability needs.
Compare deployment optionsHuman review controls
Assign reviewers by document type, risk, confidence, or failed validation and require approval before selected data reaches a system of record.
See controlled AI agentsIntegrations and RAG
Make extracted document data useful beyond the OCR result.
Deliver structured data to the next approved step.
Document workflows can receive files from email, shared drives, scanners, portals, and APIs, then send reviewed output to ERP, CRM, document management, case-management, database, or custom application interfaces. Integration scope follows the systems and permissions you already operate.
Turn validated document content into searchable knowledge.
OCR can preserve text, layout, tables, page references, document type, and validation status for indexing. A RAG service can then retrieve relevant evidence and return cited answers across scanned and digital sources without losing the path back to the original.
Explore enterprise RAG developmentDecision guidance
Choose the smallest document intelligence workflow that solves the problem.
Need
Recommended starting point
Make image text searchable
Start with OCR and basic text output.
Capture named fields and tables
Add classification, structured extraction, validation, and exception review.
Ask questions across processed documents
Feed validated content and metadata into a cited RAG knowledge service.
Route tasks or update systems
Add a controlled AI agent after extraction and retrieval, with explicit tools and approval points.
Common questions
Start with real samples and a clearly defined output.
Which documents can be processed?
Digital PDFs, image-only PDFs, scans, photographs, forms, invoices, reports, contracts, and mixed document packets can be assessed for a suitable pipeline.
How should extraction quality be judged?
Evaluate each required field or content type on representative documents and define what must be reviewed when confidence or validation is insufficient.
What do you need from us?
A representative document sample, required outputs, business rules, current process, destination system, security constraints, and expected review responsibility.
Start with one document type
Show us the documents your team still reads and rekeys by hand.
We will help define the fields, validation rules, review path, integration, and evaluation needed for a focused OCR and document-processing pilot.