Prompt
Can you recommend a retrieval API for enterprise search across internal knowledge bases?
Latest observation
Yes — for enterprise search across internal knowledge bases, I’d recommend choosing a retrieval API based on three things:
- Hybrid search quality: keyword + vector/semantic retrieval
- Connectors and ingestion: SharePoint, Confluence, Google Drive, Slack, etc.
- 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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Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
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