What is Document Indexing?

Organizations accumulate vast document repositories — contracts, policies, reports, correspondence, technical documentation — that become increasingly difficult to navigate. Traditional file-based organization and keyword search fail because users don't always know the right keywords, documents may be poorly named, and relevant information may be buried within documents rather than reflected in titles.

Document indexing uses AI to analyze document content and create rich metadata: document type, topics covered, entities mentioned, sentiment, date references, related documents, and semantic summaries. This metadata enables advanced retrieval — users can find documents based on meaning rather than keywords: 'Find contracts with non-standard liability clauses' or 'Show me engineering reports that mention thermal stress.'

For AI agents, document indexing is essential for Retrieval-Augmented Generation (RAG). When an agent needs to answer a question using organizational knowledge, it searches the document index to find relevant documents, retrieves the most pertinent passages, and uses them to generate accurate, grounded responses.