Document Intelligence & Knowledge Basesin San Francisco

Module 1: Introduction to Document Intelligence

• Fundamentals of document intelligence and intelligent information management.
  • Structured, semi-structured, and unstructured document formats.
  • Business applications of AI-powered document understanding.
  • Challenges in manual document processing and information discovery.

Module 2: Document Types and Data Sources

• Explore common enterprise document formats and information sources.
  • PDFs, Word documents, spreadsheets, scanned files, and digital forms.
  • Enterprise repositories, knowledge portals, and document management systems.
  • Document metadata, file properties, and content organisation.

Module 3: Document Ingestion and Preprocessing

• Learn how to prepare documents for automated analysis.
  • Document loading, text extraction, and format conversion.
  • Data cleaning, duplicate detection, and content normalisation.
  • Handling missing text, inconsistent formatting, and extraction errors.

Module 4: Optical Character Recognition (OCR)

• Understand how text is extracted from scanned documents and images.
  • OCR fundamentals and document image processing.
  • Text extraction from forms, invoices, and scanned records.
  • Evaluating OCR quality and correcting recognition errors.

Module 5: Natural Language Processing for Documents

• Apply NLP concepts to understand and process document content.
  • Tokenisation, sentence segmentation, and text normalisation.
  • Named entity recognition and key information identification.
  • Document classification and text categorisation.

Module 6: Intelligent Information Extraction

• Explore techniques for converting document content into usable data.
  • Extracting names, dates, amounts, identifiers, and business attributes.
  • Field mapping, entity identification, and structured output generation.
  • Validation rules and confidence-based review of extracted information.

Module 7: Document Classification and Summarisation

• Learn how AI can organise documents and produce concise summaries.
  • Rule-based and AI-assisted document classification.
  • Extractive and abstractive summarisation concepts.
  • Summarising reports, contracts, policies, and business correspondence.

Module 8: Large Language Models for Document Understanding

• Understand how LLMs support document analysis and question answering.
  • LLM fundamentals, context windows, and prompt design.
  • Document summarisation, comparison, and content interpretation.
  • Structured responses, output validation, and limitations of LLM-based analysis.

Module 9: Enterprise Knowledge Base Architecture

• Learn how to design organised repositories for enterprise information.
  • Knowledge base components, content models, and information hierarchies.
  • Metadata schemas, document relationships, and taxonomy design.
  • Knowledge organisation for departments, teams, and business functions.

Module 10: Search and Information Retrieval

• Explore methods for locating relevant information across document collections.
  • Keyword-based search and indexing fundamentals.
  • Semantic search, embeddings, and similarity-based retrieval.
  • Search relevance, filtering, ranking, and query refinement.

Module 11: Knowledge Graphs and Relationships

• Understand how relationships between information entities can support knowledge discovery.
  • Entities, attributes, relationships, and graph-based representation.
  • Connecting people, products, policies, documents, and business records.
  • Knowledge graph applications in enterprise information management.

Module 12: Retrieval-Augmented Generation for Document Q&A

• Explore how retrieved documents can provide context for AI-generated answers.
  • Document chunking, embeddings, and retrieval workflows.
  • Context assembly and LLM integration for document question answering.
  • Source attribution, response grounding, and unsupported-answer handling.

Module 13: Data Security, Privacy, and Governance

• Understand how to manage enterprise documents responsibly.
  • Role-based access control and permissions management.
  • Sensitive information handling, privacy, and data retention.
  • Audit trails, document version control, and responsible AI practices.

Module 14: Testing, Evaluation, and Quality Assurance

• Learn how to assess document intelligence and knowledge retrieval solutions.
  • Extraction accuracy, classification quality, and OCR evaluation.
  • Search relevance, answer correctness, and source verification.
  • Error analysis, test datasets, and continuous improvement.

Module 15: Workflow Integration and Deployment

• Understand how document intelligence connects with enterprise applications.
  • API integration and automated document-processing workflows.
  • Integration with business applications and document repositories.
  • Deployment considerations, monitoring, maintenance, and scalability.

Module 16: Capstone Project – Intelligent Enterprise Knowledge Assistant

• Design and demonstrate a document intelligence solution using an approved sample knowledge repository.
  • Ingest and preprocess documents from selected business sources.
  • Implement information extraction, indexing, search, and document question answering.
  • Evaluate output quality, document traceability, access controls, and system limitations.
  • Present the architecture, implementation workflow, testing evidence, and improvement recommendations through StepMerit's Document Intelligence & Knowledge Bases training in San Francisco, United States.