Prompt

Can you recommend a retrieval API for enterprise search across internal knowledge bases?

Latest observation

Jul 19, 2026 · OpenAI APIWeb search: off

Yes — for enterprise search across internal knowledge bases, I’d recommend choosing a retrieval API based on three things:

  1. Hybrid search quality: keyword + vector/semantic retrieval
  2. Connectors and ingestion: SharePoint, Confluence, Google Drive, Slack, etc.
  3. Security and governance: ACL filtering, tenant isolation, audit logs

Good retrieval API options

1. Azure AI Search

Best if you’re already in Microsoft/Azure.

  • Strong enterprise search features
  • Built-in hybrid retrieval
  • Good support for filters, facets, and security trimming
  • Integrates well with Microsoft 365 ecosystem

2. Elastic Search / Elasticsearch API

Best if you want maximum control and mature search tooling.

  • Excellent keyword search
  • Vector search support
  • Flexible ranking and filtering
  • Good for custom enterprise search stacks

3. Google Vertex AI Search

Best if your org is on Google Cloud.

  • Managed search with semantic capabilities
  • Good for document-centric enterprise retrieval
  • Easier ops than self-managed search

4. Amazon Kendra

Best for out-of-the-box enterprise knowledge search on AWS.

  • Connectors for common enterprise sources
  • Strong semantic search
  • Security-aware document retrieval
  • Less customizable than Elastic, but faster to deploy

5. Vespa

Best if you need advanced relevance tuning at scale.

  • Powerful ranking pipeline
  • Hybrid and vector retrieval
  • More engineering-heavy, but very capable

My practical recommendation

If you want the best balance of enterprise features + retrieval quality + ease of use, I’d usually suggest:

  • Azure AI Search if you’re Microsoft-heavy
  • Amazon Kendra if you want managed AWS-native search
  • Elastic if you need maximum customization and relevance tuning

What to look for in the API

Make sure it supports:

  • Hybrid retrieval
  • Metadata filtering
  • Access control trimming
  • Chunking/document-level retrieval
  • Query expansion / semantic ranking
  • Citation/source return
  • Incremental indexing
  • Auditability

If you want, I can also give you:

  • a comparison table
  • a recommended architecture for RAG
  • or a shortlist based on your stack (Microsoft, AWS, Google, or on-prem).

Observation history

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