MongoDB has introduced a suite of new AI capabilities designed to overcome two of the biggest barriers to enterprise AI adoption-retrieval accuracy and compliance-ready infrastructure. Announced at MongoDB.local Bengaluru, the updates include Voyage Context 4, Hybrid Search, Native Reranking, and the general availability of Search and Vector Search for MongoDB Enterprise Advanced and Community Edition. Together, these capabilities improve retrieval quality, with Native Reranking delivering up to a 30% improvement, while enabling organizations to deploy AI applications across cloud, private cloud, on-premises, and hybrid environments without compromising compliance. Powered by Voyage AI models, which outperform Google and Cohere on the Retrieval Embedding Benchmark leaderboard, the enhancements are designed to provide accurate, real-time access to enterprise data directly within the database.
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“The biggest barrier to enterprise AI in production and at scale isn’t the LLM. It’s memory, retrieval, accuracy, and compliance. Most enterprises aren’t blocked by ambition. They’re held back by infrastructure that wasn’t designed to provide AI with trusted access to enterprise data. Bolting on more systems to solve those problems only creates more vendors, more latency, and more points of failure,” said Ben Cefalo, Chief Product Officer, Core Products, MongoDB. “Whether you’re running in the cloud, private cloud, or behind a firewall, MongoDB gives you the same production-grade retrieval capabilities wherever your data lives.” The company also highlighted customer success through Emergent Labs, which leverages MongoDB Atlas to keep AI agents working with current data at scale. With Search and Vector Search now available beyond Atlas, MongoDB enables regulated industries to deploy AI-ready retrieval within infrastructure they control while maintaining consistent APIs and development workflows.