Design extraction schemas and prompts that return consistent, structured data from documents.
Digital Transformation & Artificial Intelligence
Intelligent Document Processing with Multimodal Language Models
Extract structured data from invoices, forms and contracts using multimodal language models, with confidence scoring and human review built into the pipeline.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
A multimodal language model can now read an invoice, a claim form or a contract page directly, which removes much of the template-building that intelligent document processing used to require, but reading a document is not the same as producing data an organisation can act on. This course works through designing extraction schemas and prompts that return consistent, structured output, deciding when a classical OCR pipeline still beats a general multimodal model, and handling the cases that break naive approaches: multi-page contracts, dense tables, handwriting and multilingual forms. Document classification and triage sit alongside confidence scoring, reconciliation against source systems and a human-in-the-loop review queue built to catch exactly what the model gets wrong. The closing module moves from pipeline to production: monitoring drift as suppliers change their templates, and meeting the data protection, audit trail and explainability expectations that regulated sectors apply to automated extraction. What delegates take away is a pipeline design they can defend on accuracy, not just a demonstration that once worked on a clean sample document.
Expected Learning Outcomes
Choose between classical OCR and multimodal language models for a given document type.
Extract tables, multi-page content and handwritten entries with defined confidence scoring.
Classify and triage incoming documents before routing them to the correct extraction schema.
Build a human-in-the-loop review queue prioritised by confidence score and business impact.
Reconcile extracted fields against source systems to measure straight-through processing rate.
Apply data protection, audit trail and drift monitoring controls to a document pipeline.
Who Should Attend
Automation and operations teams processing high volumes of invoices, forms or claims.
AI engineers building document extraction pipelines with multimodal language models.
Shared services and finance teams responsible for invoice and document workflows.
Compliance and audit staff overseeing document processing accuracy and data protection.
Insurance, banking and healthcare teams digitising claims, applications or records.
Product managers evaluating intelligent document processing tools for their organisation.
Course Modules
Select any module to see its sessions and points.
01From Traditional OCR to Multimodal Document Understanding
2 sessions · 8 points
Session 1Limitations of Template-Based Extraction
- Contrasting rigid template-matching pipelines with prompting a multimodal model on unseen layouts.
- Identifying where classical OCR still outperforms a multimodal model on dense or low-quality scans.
- Combining OCR text output with layout coordinates to ground a multimodal model's reasoning.
- Benchmarking cost and latency differences between a specialised model and a general multimodal model.
Session 2Designing Extraction Schemas and Prompts
- Defining a structured output schema so extracted fields return as consistent, parseable data.
- Writing field-level instructions that specify format, units and handling of missing values.
- Prompting the model for evidence, such as the page and region a given field was read from.
- Testing schema robustness against documents carrying unexpected extra fields or unfamiliar layouts.
02Extracting Structured Data from Complex Documents
2 sessions · 8 points
Session 1Tables, Multi-Page Documents and Handwriting
- Extracting multi-row, multi-column tables while preserving row-to-record relationships correctly.
- Stitching context across multi-page documents such as contracts or extended claim forms.
- Reading handwritten entries and flagging low-confidence characters for targeted human review.
- Processing multilingual documents where fields mix languages or scripts within one page.
Session 2Document Classification and Triage
- Classifying incoming documents by type before routing them to the matching extraction schema.
- Detecting duplicate or near-duplicate submissions in a mailroom or claims intake queue.
- Separating documents needing full extraction from those needing only a quick status check.
- Prioritising urgent document types into faster processing lanes ahead of routine correspondence.
03Validating Accuracy and Managing Human Review
2 sessions · 8 points
Session 1Confidence Scoring and Reconciliation
- Generating field-level confidence scores that decide which extractions bypass human review.
- Reconciling extracted totals and identifiers against source systems such as purchase orders.
- Measuring straight-through processing rate as the share of documents needing no human touch.
- Tracking field-level precision and recall against a manually verified reference sample.
Session 2Designing the Human-in-the-Loop Review Queue
- Building a review interface that highlights the source region behind each extracted field.
- Prioritising review queue items by confidence score combined with business impact.
- Capturing reviewer corrections as structured feedback for prompt and pipeline improvement.
- Setting service-level targets for review turnaround on time-sensitive documents such as claims.
04Scaling, Governance and Compliance for Document Pipelines
2 sessions · 8 points
Session 1Operating the Pipeline at Volume
- Batching document processing to manage cost and throughput against provider rate limits.
- Caching extraction results for resubmitted or duplicate documents to avoid repeat processing.
- Monitoring drift when a supplier or partner changes an invoice or form template unexpectedly.
- Load-testing the pipeline against seasonal peaks in document volume before they arrive.
Session 2Data Protection, Audit and Explainability
- Applying data residency and retention rules to documents carrying personal or financial information.
- Redacting or masking sensitive fields before documents reach downstream analytics systems.
- Maintaining an audit trail linking each extracted field back to its source image and model version.
- Documenting extraction accuracy and known failure modes for compliance and audit review.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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