# The best rag platforms and ai knowledge base software

> Systems that index a company's documents and data so a language model can answer from them with citations, covering ingestion, chunking, retrieval, permissions and evaluation.

Last reviewed 2026-09-16. Canonical page: https://launch500.com/best/rag-knowledge-platforms

## What has to be true to appear here

- Handles ingestion and retrieval, not just vector storage
- Enforces per-document access control that survives retrieval
- Returns citations that resolve to a specific source location
- Offers a retrieval evaluation or at least exportable retrieval traces

## What every product here was put through

1. Index 12,000 mixed documents (PDF, HTML, spreadsheets, scanned contracts) and measure ingestion failures and silent truncation
2. Run a 250-question retrieval benchmark and score citation correctness, not just answer plausibility
3. Permission test: confirm a restricted document never surfaces for a user without access, including via summarisation
4. Update 50 source documents and measure how long until answers reflect the change

## The shortlist

Ranked by Crash Test score. Untested products appear below tested ones regardless of community rating.

| Product | Crash Test | Verified reviews | From (5 seats) | Best for |
| ------- | ---------- | ---------------- | -------------- | -------- |

## Verdicts

## Frequently asked questions

### Do I need a RAG platform or just a vector database?

If you have engineers and a narrow corpus, a vector database and a hundred lines of code is often enough and much cheaper. Platforms earn their fee on the unglamorous parts: parsing bad PDFs, keeping permissions intact through retrieval, and re-indexing on change. Our test found the parsing gap to be the largest single quality difference between products.

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Launch500 independently tests SaaS and business software. Inclusion and order are not for sale: https://launch500.com/trust/how-we-make-money