Retrieval infrastructure companies, products & suppliers
Pipelines that chunk, index and fetch context for applications, not the database alone. Jobs: chunking pipelines; hybrid retrieval; context assembly for apps.
What is Retrieval infrastructure?
Pipelines that chunk, index and fetch context for applications, not the database alone. Jobs: chunking pipelines; hybrid retrieval; context assembly for apps.
What problems does it solve?
Each app reinvents chunking, connectors and fetch policies.
Typical business use cases
- Chunking pipelines
- Hybrid retrieval
- Context assembly for apps
Important capabilities
- Connectors
- Chunk policy
- Fetch APIs
What buyers should evaluate
- Chunk leakage across ACLs
- Freshness
- Who owns the pipeline
Risks and governance considerations
A shared retriever that ignores document permissions.
Procurement checklist
- ACL tests
- Freshness SLA
- Pipeline ownership
Relevant AI Trustmark assurance
AI Trustmark independent findings appear only when an assessment or certificate exists. Category membership does not imply verification.
Products and providers
Open a product profile for its catalogue details, or use the provider link for the company profile. Claimed listings appear first, then by name. Payment never changes the order.
By 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.
Replace DIY complexity with the context engineering platform built for accuracy. Ship production-grade AI that is secure, scalable, and specialized. Contextual AI Platform is Conte
By deepset
Deploy production AI with full control. Build agents and RAG systems using the Haystack Enterprise Platform, trusted by enterprise, defense, and regulated industries. Haystack is d
By Elastic
Power insights and outcomes with The Elasticsearch Platform. See into your data and find answers that matter with enterprise solutions designed to help you accelerate time to insig
By Jina AI
Embeddings, rerankers, web reader, deepsearch, small language models. Search AI for multilingual and multimodal data. Jina Reader API is Jina AI's model API, from Jina AI GmbH. Pub
By Microsoft
Foundry IQ is the knowledge and retrieval layer of Microsoft Foundry, which Microsoft describes as the evolution of Azure AI Search. It gives AI agents reusable knowledge bases bui
By Mixedbread
Make your agents work on your data. Mixedbread turns PDFs, decks, videos, code, Slack, and Drive into evidence your agent can act on, with fewer tokens. Mixedbread Search is Mixedb
By MongoDB
Get your ideas to market faster with a flexible, AI-ready database. MongoDB makes working with data easy. MongoDB Embedding and Reranking API is MongoDB's model API, from MongoDB,
By 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
By Weaviate
Bring AI-native applications to life with less hallucination, data leakage, and vendor lock-in. Weaviate is Weaviate's platform, from Weaviate B.V. Published at weaviate.io/develop
Products
Claimed products appear first. Ranking packs and payment do not change this list.
- Cohere Rerank API· Cohere
- Contextual AI Platform· Contextual AI
- Elastic Inference Service· Elastic
- Haystack· deepset
- Jina Reader API· Jina AI
- Jina Reranker API· Jina AI
- Microsoft Foundry IQ· Microsoft
- Mixedbread Search· Mixedbread
- MongoDB Embedding and Reranking API· MongoDB
- Weaviate· Weaviate
Also used in this category
These products have a different primary category so they do not compete for the same ranking queries. They are listed here because buyers still encounter them in this job.
- Pinecone Vector Database· Pinecone
Related categories
Relevant procurement and assurance guides
Frequently asked questions
What is Retrieval infrastructure?
Pipelines that chunk, index and fetch context for applications, not the database alone. Jobs: chunking pipelines; hybrid retrieval; context assembly for apps.
What should not be listed as Retrieval infrastructure?
Products whose buyer job is vector databases, RAG product UIs, or enterprise search. Those belong on their own category page so search queries are not split.
Has AI Trustmark independently assessed every Retrieval infrastructure supplier?
No. A category listing is descriptive. Independent assessment is shown only on company or product pages that carry Trustmark evidence.
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.