Use AI to classify documents automatically based on content and context, improving speed, consistency, and retrieval accuracy while reducing manual effort.
Key Features
Implementation
Implementation Steps
- Define classification categories and decision criteria.
- Train models using historical documents and labeled data.
- Integrate classification into existing document workflows.
- Monitor outcomes and refine model accuracy periodically.
Flow
- Documents are ingested from business systems.
- AI analyzes content and metadata to assign classes.
- Classified documents are routed or stored automatically.
- Users retrieve documents through search and filters.
Use Cases
How AI-Powered Document Classification Works
Contellect's smart document classification engine combines machine learning models with natural language processing (NLP) to automatically identify, label, and route documents the moment they enter your organisation. Unlike rule-based systems that require manual maintenance, Contellect learns from every document processed — growing more accurate over time.
The classification pipeline runs in three stages: ingestion (PDF, TIFF, Word, email, scanned images), analysis (OCR, entity extraction, layout understanding), and classification (multi-label taxonomy mapping against your business rules). Average throughput exceeds 10,000 documents per hour on a standard Azure deployment.
Types of Documents Contellect Classifies
- Invoices, purchase orders, and remittance advice
- Contracts, NDAs, and legal agreements
- Medical records, lab reports, and insurance claims
- KYC / AML identity documents (passports, licences, utility bills)
- HR files (CVs, offer letters, payroll records)
- Engineering drawings and technical manuals
- Correspondence, email attachments, and web forms
Key Benefits of Automated Document Classification
- 99%+ accuracy on structured document types (invoices, forms)
- 80% reduction in manual document sorting labour
- 60% faster downstream process initiation
- Zero misfiling — misrouted documents eliminated from day one
- Compliance-ready — full audit trail on every classification decision
- Scales to millions of documents per day on cloud infrastructure
Industry Use Cases
Healthcare
Classify patient discharge summaries, lab results, and referral letters automatically — routing them to the correct EHR fields and care team queues. Supports HL7, FHIR, and legacy formats.
Financial Services
Automate KYC document onboarding, loan application sorting, and trade confirmation processing. Meets MiFID II, Basel III, and GDPR archival requirements out of the box.
Government & Public Sector
Digitise and classify legacy paper archives at scale. Route citizen correspondence to the correct agency department automatically, slashing response SLAs.
Legal & Compliance
Identify and tag contract clauses, privilege documents, and regulatory filings during discovery or audit preparation — cutting review hours by up to 75%.
Frequently Asked Questions
- What is smart document classification?
- Smart document classification uses artificial intelligence — combining OCR, NLP, and machine learning — to automatically identify the type and content of a document and assign it to the correct category, workflow, or storage location without human intervention.
- How does AI document classification differ from manual sorting?
- Manual sorting relies on staff reading and filing each document, which is slow, error-prone, and unscalable. AI classification processes thousands of documents per minute with consistent accuracy, 24/7, and gets smarter as it ingests more data.
- What file formats can Contellect classify?
- Contellect ingests PDF, TIFF, JPEG, PNG, DOCX, XLSX, MSG (Outlook), EML, and scanned image formats. Multi-page and mixed-format batches are handled natively.
- How accurate is Contellect's document classification?
- Accuracy depends on document type and training data volume. For structured forms (invoices, ID documents), out-of-the-box accuracy exceeds 97%. For unstructured documents, accuracy typically reaches 90–95% after an initial training period of 2–4 weeks.