Enterprise Search & Knowledge Graphs: Guide 2026

Enterprise search — 'where's the info I need across our 50 tools' — has been broken for two decades. LLM-augmented semantic search + knowledge graphs finally deliver on the promise. Here is what modern enterprise search looks like and how it compares off-shelf vs custom.

The Modern Enterprise Search Stack

1. Connector layer — pulls content from Google Drive, Slack, Notion, Confluence, GitHub, Salesforce, etc.
2. Indexing + embedding — hybrid text + vector index.
3. Permission-aware retrieval — respects source-system ACLs.
4. Knowledge graph — entity relationships across sources.
5. LLM answer generation — natural language Q&A with citations.
6. Personalisation — ranks by user role, project, past searches.
7. Access + audit — logs all searches for governance.

Off-Shelf vs Custom

Off-shelf leaders: Glean (best-in-class), Coveo, Elastic Enterprise Search, Guru, Microsoft Copilot for M365.
DIY on OSS: Elasticsearch / OpenSearch + vector DB + LLM = 60-80% of Glean at 30-40% of cost.
Custom builds: for enterprises with unique compliance / data sovereignty needs.

Knowledge Graph: When It Adds Value

Above 500 employees + 20+ connected tools, a knowledge graph layer materially improves search quality. Below that, hybrid semantic search alone works. Neo4j, TigerGraph, Neptune are common backends; entity extraction requires ML pipeline.

Cost + Timeline

Glean / Coveo enterprise: $30-100+/user/month + implementation.
Custom build MVP: ₹30-90 lakh / $36K-108K, 16-28 weeks.
Custom enterprise platform: ₹1.5-4 Cr / $180K-480K, 28-48 weeks.
Ongoing LLM API + vector storage: $2K-15K/month for mid-market.

Permission-Aware Retrieval: The Non-Negotiable

Enterprise search must never surface content a user can't access in the source system. Building this correctly requires per-user permission checks on every query — often the hardest engineering task in the entire stack. Skip this and you'll have data exposure incidents.