Enterprise Search Glossary
Every term you need to understand enterprise search, knowledge management, and AI-powered retrieval — explained in plain language.
Enterprise Search
Software that indexes content across multiple business applications and lets employees query all of them from a single interface. Modern enterprise search uses semantic understanding to match meaning rather than just keywords.
Learn more →Semantic Search
A search approach that understands the meaning behind queries rather than matching exact keywords. Semantic search uses vector embeddings to find documents that are conceptually related, even when they use different terminology.
Learn more →Keyword Search
Traditional search that matches the exact characters typed against the characters stored in documents. Keyword search misses synonyms, abbreviations, and conceptually related content because it matches strings, not meaning.
Learn more →RAG (Retrieval-Augmented Generation)
An AI architecture that combines vector-based semantic retrieval with large language model generation to answer questions over proprietary data. Enterprise RAG platforms include connectors, permission controls, and audit logging beyond the basic retriever-and-LLM pattern.
Learn more →Federated Search
Search that queries multiple data sources simultaneously and returns results from all of them in a single ranked list. Unlike centralized search that requires all data in one index, federated search connects to sources where they already live.
Learn more →Cross-Platform Search
A search tool that connects to multiple SaaS applications via OAuth and indexes content across all of them. Users query once and get results from Gmail, Google Drive, Confluence, SharePoint, Jira, and other connected platforms.
Learn more →Unified Knowledge Search
An approach to enterprise search that treats all organizational knowledge — across email, documents, chat, tickets, wikis, and repositories — as a single searchable corpus with semantic understanding and source citations.
Learn more →Permission-Aware Search
Enterprise search that respects source-system access controls at query time. When a user searches, results only include documents they are authorized to access in the original system. This prevents data leakage through AI search tools.
Learn more →Vector Embeddings
Numerical representations of text meaning. When documents are converted into vector embeddings, semantically similar content gets similar numerical representations — enabling search by meaning rather than keywords.
Learn more →Vector Database
A specialized database that stores and queries vector embeddings efficiently. Vector databases enable fast similarity search across millions of document chunks, forming the retrieval layer of semantic search and RAG systems.
Hybrid Search
A retrieval approach that combines semantic (vector) search with keyword (lexical) search. Hybrid search uses semantic understanding for conceptual queries while preserving exact-match capability for specific terms like function names or error codes.
Workspace
A logical container in enterprise search that scopes search to specific data sources and restricts access to specific users. Workspaces enable multi-tenant isolation — a consulting firm can create separate workspaces per client, each with its own connectors and members.
Learn more →Connector
An integration module that connects an enterprise search platform to a specific SaaS application via OAuth. Connectors handle authentication, data sync, incremental indexing, and permission metadata extraction.
Learn more →OAuth
An open standard for access delegation. In enterprise search, OAuth enables one-click connection to SaaS applications like Gmail, Google Drive, and Confluence without sharing passwords or API keys.
Chunking
The process of splitting documents into smaller segments optimized for embedding and retrieval. Chunk size, overlap, and boundary detection directly affect search quality in semantic search systems.
Citation
A reference linking an AI-generated answer back to the source document. Citations include the source system, author, timestamp, and a direct link, enabling users to verify AI responses against original content.
AI Synthesis
The process of generating a direct answer from multiple retrieved documents rather than returning a list of links. AI synthesis combines information from across platforms into a coherent, cited response.
Knowledge Management
The practice of creating, sharing, using, and managing organizational knowledge. Modern knowledge management focuses on making existing knowledge findable rather than creating new documentation systems.
Learn more →Institutional Knowledge
The accumulated experience, decisions, processes, and context that an organization develops over time. Research shows 42% of institutional knowledge exists only in employees' heads — disappearing when they leave.
Learn more →Tribal Knowledge
Unwritten expertise, workflows, and informal practices that exist only in the minds of experienced employees. Tribal knowledge is the most valuable and most vulnerable form of organizational knowledge.
SaaS Sprawl
The proliferation of SaaS applications across an organization. The average company uses 112 SaaS apps, each creating a separate data silo with its own search bar. SaaS sprawl is the root cause of the enterprise search problem.
Learn more →Data Silo
A repository of information that is isolated from other systems. Each SaaS application creates a data silo — Confluence search cannot see Gmail, SharePoint search cannot see Jira. Enterprise search breaks down silos by searching across all of them.
E-Discovery
The process of identifying and collecting electronically stored information for legal proceedings. Enterprise search accelerates e-discovery by finding relevant documents across all platforms in a single query.
Learn more →MCP (Model Context Protocol)
A protocol that enables AI agents and tools to query external knowledge sources. An MCP server for enterprise search lets AI assistants like Claude access organizational knowledge directly during conversations.
Learn more →Audit Trail
A chronological log of all search queries, results returned, and AI-generated answers within an enterprise search system. Audit trails are required for compliance in regulated industries like healthcare, finance, and legal.
Learn more →MTTR (Mean Time to Resolve)
The average time from incident detection to complete resolution. Enterprise search reduces MTTR by enabling support and engineering teams to find past incident resolutions, runbooks, and troubleshooting docs in seconds instead of hours.
Learn more →See Enterprise Search in Action
RetrieveIT connects to your existing tools and makes every document, email, and conversation searchable from a single interface.