RAG systems companies, products & suppliers

RAG systems retrieve organisation-approved sources and then generate an answer with citations. They are bought as knowledge products with permissioning, not as a raw LLM. The failure mode is an unsourced or over-permissioned answer.

What are RAG systems?

RAG systems retrieve organisation-approved sources and then generate an answer with citations. They are bought as knowledge products with permissioning, not as a raw LLM. The failure mode is an unsourced or over-permissioned answer.

What problems does it solve?

Staff ask models questions the intranet already answered, or the model invents citations.

Typical business use cases

  • Policy Q&A
  • Support knowledge
  • Research assistants with a corpus

Important capabilities

  • Indexing
  • ACL
  • Citations
  • Eval

What buyers should evaluate

  • Permission inheritance
  • Citation fidelity
  • Corpus coverage

Risks and governance considerations

Cross-permission leakage and hallucinated sources.

Procurement checklist

  • ACL mapping
  • Citation policy
  • Eval set
  • Index scope

Relevant AI Trustmark assurance

Grounding is tested against the live corpus, not a vendor demo collection.

Methodology · How verification works

Companies and providers

Claimed suppliers appear first so buyers can start with listings the company has taken ownership of. Payment does not buy this order.

  • Amazon Web Services

    Amazon Web Services is Amazon's cloud division selling compute, storage, Bedrock model hosting and related AI infrastructure. Public pages cover regional cloud services for builder

  • AnythingLLM

    A free, private AI assistant that runs on your device. No accounts, no API keys, no token limits. AnythingLLM is used for RAG systems work. AnythingLLM publishes product informatio

  • Cohere

    Cohere is a Toronto-headquartered model company selling text, embed and RAG-oriented APIs for enterprises. Public pages emphasise private deployment options alongside hosted APIs.

  • deepset

    Build agents and RAG systems using the Haystack Enterprise Platform, trusted by enterprise, defense, and regulated industries. deepset publishes deepset AI Platform as named AI pro

  • Dify

    Dify is the platform for production-ready agentic workflows. Build agents, knowledge pipelines, models, and tools on one canvas, deployable on Cloud, in your VPC, or self-hosted. D

  • LlamaIndex

    LlamaIndex publishes LlamaExtract, LlamaCloud, and LlamaParse as named AI products. LlamaIndex is used for RAG systems and AI SDKs work. LlamaIndex publishes product information at

  • Pinecone

    Pinecone is the trusted AI knowledge company. Its AI knowledge platform—Database, Nexus, and Marketplace—powers accurate, fast, cost-effective AI for 10,000+ customers and 1M devel

  • Progress Software

    Progress provides AI-powered software solutions to automate processes to develop, deploy and manage apps, and make critical data more accessible and secure. Progress Software publi

  • Ragie

    Ragie is the Context Engine for Agents, Assistants, and Apps — a fully managed RAG-as-a-Service platform with real-time indexing, retrieval with citations, multimodal support, and

  • Rogue Digital

    Rogue Digital is a London technical partner for scaling businesses: a senior team advising and building AI (RAG, observability, evaluation) under one roof for mid-market buyers. Ro

  • Vectara

    Vectara publishes Hughes Hallucination Evaluation Model as named AI products. Vectara is used for Hallucination detection and RAG systems work. Vectara publishes product informatio

Products

Claimed products appear first. Ranking packs and payment do not change this list.

Related categories

Relevant procurement and assurance guides

Frequently asked questions

Is RAG the same as enterprise search?

Search ranks documents. RAG generates an answer. Some products do both; the procurement test differs.

What are RAG systems?

RAG systems retrieve organisation-approved sources and then generate an answer with citations. They are bought as knowledge products with permissioning, not as a raw LLM. The failure mode is an unsourced or over-permissioned answer.

What should buyers ask about AI data retention and deletion?

Establish how long prompts, files, traces and embeddings are kept, who can access them, and how deletion is evidenced. Retention in a debug or eval store can outlast the customer contract if it is not in scope.

What model or provider changes should a buyer insist on being told about?

Material change usually includes a new model family, new region, new subprocessor, new write-capable tool, or a change that affects logging, privacy or human oversight. Those changes should trigger evidence refresh rather than a silent release.

How should buyers verify where AI customer data is processed?

Ask for the named processing locations, cloud regions and any subprocessors that see prompts, files or outputs. A directory listing is not evidence of residency. Independent assessment records the locations that were in scope on the assessment date.

Does a TrustMark on one product cover the rest of the company?

No. Independent assessment is scoped to the named organisation and, where relevant, the named product. Category pages list suppliers as a topic label. They do not imply that every listed company has been assessed.

What incident-handling evidence is useful for AI suppliers?

Buyers should see how AI-specific failures are detected, contained and notified — including unsafe outputs, data leakage and unauthorised agent actions. An incident policy that never mentions models, prompts or tools is incomplete for this class of product.

What security testing evidence should buyers request for an AI product?

Ask what was tested, against which version, whether prompt-injection, data-exfiltration and tenant isolation were in scope, and where failed prompts were stored. A generic ISO certificate or a vendor scanner screenshot is not by itself an AI TrustMark assessment.