AI TrustMark

Semantic search companies, products & suppliers

Vector or embedding search that ranks by meaning rather than keywords. Jobs: similar-document find; embedding retrieval apis; hybrid keyword-plus-vector ranking.

What is Semantic search?

Vector or embedding search that ranks by meaning rather than keywords. Jobs: similar-document find; embedding retrieval apis; hybrid keyword-plus-vector ranking.

What problems does it solve?

Keyword retrieval misses paraphrases in large unstructured collections.

Typical business use cases

  • Similar-document find
  • Embedding retrieval APIs
  • Hybrid keyword-plus-vector ranking

Important capabilities

  • Embeddings
  • ANN index
  • Hybrid ranking

What buyers should evaluate

  • Domain fit of embeddings
  • Index ops
  • Whether generation is bolted on

Risks and governance considerations

Semantic neighbours that leak across permission boundaries.

Procurement checklist

  • Embedding model identity
  • ACL on vectors
  • Rebuild process

Relevant AI Trustmark assurance

AI Trustmark independent findings appear only when an assessment or certificate exists. Category membership does not imply verification.

Methodology · How verification works

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.

  • Algolia NeuralSearch

    By Algolia

    Algolia NeuralSearch is a hybrid search capability that combines keyword matching with vector-based semantic search on an Algolia index. Once activated, it handles natural-language

  • Chroma Cloud

    By Chroma

    Chroma Cloud is Chroma's platform, from Chroma Inc. Published at trychroma.com/changelog/introducing-chroma-cloud. Directory filing: Semantic search. Vendor legal name on this prod

  • Cohere Rerank

    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.

  • Elasticsearch

    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

  • Glean Enterprise Search

    By Glean

    Glean sells enterprise AI search over workplace applications. Public pages cover connectors, permission-aware retrieval and an assistant for employees. Glean Enterprise Search is G

  • Agent Search

    By Google

    Agent Search is Google Cloud's enterprise search and recommendations service, which Google is renaming from Vertex AI Search; earlier names include AI Applications, Vertex AI Searc

  • Marqo Cloud

    By Marqo

    AI-native search, recommendations, and merchandising trained on your catalog. Marqo delivers $130M+ in proven revenue for enterprise retailers. Marqo Cloud is Marqo's platform, fro

  • Pinecone Semantic Search

    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

  • Qdrant Cloud

    By Qdrant

    Qdrant is an Open-Source Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API. Qdrant Cloud is Qdrant's platform

  • Typesense Cloud

    By Typesense

    Typesense is an open-source search engine for fast, typo-tolerant site and app search, with simple APIs, vector search, and self-hosting. Typesense Cloud is Typesense's platform, f

  • Vespa Cloud

    By Vespa.ai

    Vespa is the AI Search Platform for fast, accurate AI search, AI agents, personalization, recommendations, and retrieval. Vespa Cloud is Vespa.ai's platform, from Vespa.ai AS. Publ

  • Zilliz Cloud

    By Zilliz

    Zilliz offers a fully managed Vector Lakebase powered by Milvus, unifying real-time vector search, lake-scale discovery, and AI data operations. Available across AWS, Google Cloud,

Products

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

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.

Related categories

Relevant procurement and assurance guides

Frequently asked questions

What is Semantic search?

Vector or embedding search that ranks by meaning rather than keywords. Jobs: similar-document find; embedding retrieval apis; hybrid keyword-plus-vector ranking.

What should not be listed as Semantic search?

Products whose buyer job is full enterprise search suites, knowledge graphs, or RAG generators. Those belong on their own category page so search queries are not split.

Has AI Trustmark independently assessed every Semantic search supplier?

No. A category listing is descriptive. Independent assessment is shown only on company or product pages that carry Trustmark evidence.

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.