USE CASE ARCHITECTURE
Enterprise Search & Intranet Retrieval
Unified semantic search connecting company data in Google Drive, Notion, Slack, Jira, and GitHub.
Architecture Flow
1. REQUIREMENTSTask Objective
2. CONCEPTS4 Mechanisms
3. CATEGORIES2 Segments
4. VENDORS4 Providers
Required Stack Capabilities
- •Semantic search & BM25 hybrid indexing
- •Role-based access control (RBAC)
- •Enterprise connectors
- •Entity resolution
Relevant Concepts
Semantic Search
Search techniques that interpret user intent and conceptual meaning rather than relying solely on literal string or keyword matching.
Hybrid Search
A unified retrieval approach that combines sparse lexical matching and dense vector search, typically merged via scoring algorithms like Reciprocal Rank Fusion (RRF).
Semantic Layer
An abstraction layer that maps complex, technical database schemas and raw data into consistent, business-friendly concepts accessible to both humans and AI agents.
Entity Resolution
The algorithmic process of identifying, disambiguating, and linking disparate records that refer to the same real-world entity across different datasets.
Relevant Market Categories
Enterprise Search & Semantic Layers
Unified enterprise discovery platforms and semantic data virtualization layers that index corporate knowledge silos, enforce access controls, and translate data warehouses into consistent context for AI.
Semantic Search & Discovery Engines
Search platforms that combine sparse lexical indexes (BM25), dense vector embeddings, and neural re-ranking into unified hybrid retrieval engines for intent-driven discovery.
Featured Solution Providers
Glean
Enterprise AI work assistant providing unified semantic search across internal corporate apps.
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Dust
Custom AI assistant platform providing context connections into Slack, Notion, and GitHub.
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Writer
Full-stack enterprise generative AI platform featuring Knowledge Graph RAG context integration.
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Cohere
Enterprise AI company delivering state-of-the-art embedding models and cross-encoder rerankers.
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