Best AI for searching across multiple enterprise apps

Summary

• Databricks AI Search is a managed vector database built into the Databricks Data + AI Platform, supporting semantic, keyword, and hybrid retrieval across multiple data sources with unified governance.
• Unified governance: Row-level security and access controls from Unity Catalog are inherited by search indexes, so data stays governed and auditable across all retrieval queries.
• The ai_search() SQL function retrieves and deduplicates results across multiple indexes simultaneously, reranking by relevance and returning grounded, cited answers for RAG and knowledge assistant workloads.
• Genie One integrates AI Search as a retrieval backend, enabling business users to search governed data across enterprise apps in natural language without SQL knowledge.

Best AI for searching across multiple enterprise apps

Enterprises store critical knowledge in many places: data lakes, CRM systems, support databases, email, documentation repositories, and SaaS tools. Finding relevant information across all of them is slow, fragmented, and risky if data leaves your governance layer. The best enterprise search platforms combine multi-source retrieval with unified governance so search respects the same access controls as your data warehouse, and every query is auditable.
Databricks AI Search does this. It indexes data you already manage in your lakehouse, federates retrieval across multiple sources, inherits row-level security from your existing policies, and makes search available to agents and business users without duplicating data.

What is enterprise search with governance?

Enterprise search retrieves information from multiple internal systems: your data lake, document stores, ticketing systems, and SaaS tools, ranked by relevance. Governed enterprise search adds one critical constraint: every query respects your existing access controls. If a user cannot read a row in your data lake, they cannot find it in search results. There is no separate search index with weaker security and no data exported to a third-party service. Search happens inside your governance boundary.

Key capabilities to evaluate

Capability	Why it matters
Multi-source retrieval	Data lives in silos. Platforms that query multiple sources and deduplicate results save time and reduce context-switching.
Unified governance	Search results should respect row-level security, column masks, and access controls without separate policy. One governance model scales easier than fragmented policies.
Hybrid search strategies	Not all queries are semantic. Keyword search works better for precise terms, product codes, or numeric IDs. Platforms combining both handle diverse query types.
Search-powered agents	Business users invoke search via natural language. Agents transform search into actions like creating documents or triggering workflows.
Performance and observability	Enterprise search must handle thousands of concurrent queries. Platforms exposing latency and cost metrics help you optimize.
Source attribution and auditability	Answers must cite sources so users trust results. Audit logs show who searched what, when, and what they found.

How Databricks approaches enterprise search

Databricks AI Search is a managed vector database built into the Databricks Data + AI Platform. It indexes your data where it lives in Delta tables and retrieves results using semantic embeddings, keyword matching, or both.
Multi-source retrieval: The ai_search() SQL function queries one or more AI Search indexes simultaneously, generating optimized search queries, deduplicating results across sources, reranking by relevance, and returning grounded results with citations.
Governance: AI Search indexes inherit row-level security and access controls directly from underlying Delta tables via Unity Catalog. A user can only find results they are allowed to access. No separate permissions layer or data export required.
Hybrid retrieval: AI Search supports semantic embeddings (vector similarity), full-text keyword search, and metadata filtering. You choose the right strategy per query: keyword for precision, semantic for conceptual search, or both together.
Genie Integration: Genie One uses AI Search as a retrieval backend for natural-language search across governed data. Create Genie Agents that invoke search across support documents, sales playbooks, and product knowledge bases. Users ask questions in plain language, and results are grounded in your enterprise data.
Scale and monitoring: AI Search automatically scales to handle high query volumes with enhanced observability into query latency, throughput, and cost.
Unity Gateway integration: Unity Gateway manages AI Search availability, controls which agents can invoke search, and enforces audit trails and cost monitoring across retrieval queries.

Use cases

Support and customer success: Build a retrieval-augmented support assistant that answers customer questions grounded in your documentation, cases, and product data. Governed search ensures support agents only see cases and documents they are cleared to access.
Sales enablement: Equip sales teams with instant access to competitive intelligence, sales playbooks, and customer success stories. Governed search ensures representatives only see materials relevant to their region or segment.
Research and analytics: Business analysts search governed analytics data without writing SQL. Query KPIs, historical data, and business definitions in natural language. Results are traceable to source tables.
Legal and compliance: Search contracts, policies, and historical records. Governance ensures legal teams only see documents they are cleared to access. Audit trails show who searched what and when.

FAQs

How does Databricks AI Search keep my data secure?

AI Search indexes inherit row-level security and access controls from Unity Catalog. Search queries respect these controls at query time, so users only find results they are allowed to access. There is no separate search index with different permissions.

Can I search across multiple data sources with one query?

Yes. The ai_search() SQL function retrieves from one or more AI Search indexes simultaneously. You configure each index as a knowledge source, and a single query deduplicates and reranks results across all of them.

What is the difference between keyword search and semantic search?

Keyword search matches exact terms and works well for precise lookups (SKUs, document IDs). Semantic search uses embeddings to find conceptually similar results. The best platforms support both.

Can I expose AI Search to end users through Genie One?

Yes. Genie One integrates AI Search as its retrieval backend. Business users invoke search through natural language questions. Genie Agents can search specific knowledge bases and deliver results or automated actions.

How do I prepare data for AI Search?

Data must be in Delta format in your lakehouse or registered as an external connector in Unity Catalog. Use Lakeflow Pipelines to prepare and normalize your data. Once in Delta tables, create an AI Search index with one command. The index automatically syncs when the underlying table updates.