AI Search Platforms for Internal Documents: How to Compare Your Options
Compare search services you configure, vector databases with a RAG framework, and integrated platforms such as Seahorse Cloud for internal documents, by ingestion, access control and freshness.
Choose an internal document search platform by deciding which work your team wants to own. Keep an existing enterprise search service when its supported source connectors and permission model fit. Choose a vector database with a RAG framework for custom integration. Consider an integrated platform such as Seahorse Cloud when document parsing, storage and agent setup are the main work.
These are useful buying lenses, not fixed product boundaries. Every option still needs source integration, identity and permission design, and evaluation; an integrated platform can reduce the number of pipeline components you assemble separately.
Key takeaways
How to choose an internal document search platform
- Keep your enterprise search service when its supported source connectors and permission model fit your repositories and users.
- Choose a vector database plus RAG framework when custom integration and control over the application are priorities.
- Consider an integrated platform such as Seahorse Cloud when parsing, storage and agent setup are the main work you want to bring together.
- Treat categories as illustrative: ingestion, permission features and agent capabilities vary by product and configuration.
Three types of options
| Option type | What you configure or build | Access control | Examples |
|---|---|---|---|
| Vector database plus RAG framework | Parsing, chunking, embedding, source sync and the query or agent layer, as needed | Usually designed in the application or retrieval layer | pgvector or Qdrant with LlamaIndex |
| Enterprise search service | Source connectors or push pipelines, index settings and the search experience | Service features and/or application authorization, depending on configuration | Azure AI Search or Elasticsearch |
| Integrated RAG platform | A coordinated document, vector-search and agent pipeline | Platform and application controls; verify how each layer handles identity and permissions | Seahorse Cloud |
These categories are not fixed product boundaries. Compare the configured ingestion and extraction path for each service with your actual sources; the amount of parsing and integration work depends on the product, features and configuration.
What to evaluate
File handling and parsing. Test difficult files such as scanned contracts, tables and long manuals. Seahorse Storage combines VLM, OCR and LLM-based parsing with semantic chunking and automatic vectorization. It accepts files up to 100 MiB each; check its supported extensions against your repositories.
Access control. Seahorse Agent Users settings restrict access to an agent; API-key Read or Write permissions govern credentials. Neither establishes automatic inheritance of source-document ACLs. With Require Authentication enabled, an empty selected-user list permits all authenticated tenant users, so removing the last selected user is not a deny-all operation. Use an explicit allowlist for restricted agents and control alternate API, tool and agent routes. If the application applies document authorization, enforce it before passages reach the LLM. See the agent access settings.
Freshness. Seahorse Storage processes uploaded files and synchronizes vector data. That does not establish how external repository edits, deletions or source permissions are synchronized. Define how your source integration handles those changes, and avoid assuming results are instantly fresh. Storage detail shows Document Pipeline progress as N / M Completed and per-file Completed status with a chunk count, so upload alone does not prove processing is complete. See Storage detail.
Delivery and hosting
Seahorse Cloud is offered as an on-premises installation or a SaaS subscription. Consider hosting location and contractual operating responsibilities separately. For on-premises deployments, verify the configured inference endpoints; deployment location alone does not establish where model calls run. Seahorse Cloud deployment options
Choosing a fit
Use the enterprise search service you already have when its connectors and permission model fit. Choose a vector database and framework when custom integration is the point. Choose an integrated pipeline such as Seahorse Cloud when bringing document parsing, storage and agent setup together reduces separately assembled components. In all three cases, plan for source integration, identity design and a realistic evaluation.
FAQ
Which documents should a pilot use?
Use representative files that expose your real parsing needs, such as scanned pages, tables and long manuals, along with questions employees actually ask. Compare retrieved passages as well as final answers.
Do agent or API permissions automatically enforce source document access?
No. Agent access settings and API-key permissions are not proof of source ACL inheritance. Verify where authorization is enforced; if the application filters results, authorize before retrieved passages reach the LLM.
What should we check about synchronization?
Distinguish uploaded-file processing from external source synchronization. Define how edits, deletions and permission changes reach search results rather than assuming instant freshness.
Try it on your own files
Create a Storage, upload a sample of internal documents and run a semantic search.
