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venturebeat
Why MongoDB thinks better retrieval — not bigger models — is the key to trustworthy enterprise AI

Agentic systems and enterprise search depend on strong data retrieval that works efficiently and accurately. Database provider MongoDB thinks its newest embeddings models help solve falling retrieval quality as more AI systems go into production.As agentic and RAG systems move into production, retrieval quality is emerging as a quiet failure point — one that can undermine accuracy, cost, and user trust even when models themselves perform well.The company launched four new versions of its embeddings and reranking models. Voyage 4 will be available in four modes: voyage-4 embedding, voyage-4-large, voyage-4-lite, and voyage-4-nano.  MongoDB said the voyage-4 embedding serves as its general-purpose model; MongoDB considers Voyage-4-large its flagship model. Voyage-4-lite focuses on tasks [...]

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venturebeat
Enterprises are measuring the wrong part of RAG

Enterprises have moved quickly to adopt RAG to ground LLMs in proprietary data. In practice, however, many organizations are discovering that retrieval is no longer a feature bolted onto model inferen [...]

Match Score: 261.05

venturebeat
Databricks' Instructed Retriever beats traditional RAG data retrieval by 70% — enterprise metadata was the missing link

A core element of any data retrieval operation is the use of a component known as a retriever. Its job is to retrieve the relevant content for a given query. In the AI era, retrievers have been used a [...]

Match Score: 127.42

venturebeat
Agents don't replace vector search - they make it harder to get right

What's the role of vector databases in the agentic AI world? That's a question that organizations have been coming to terms with in recent months.<br /> <br /> The narrative had [...]

Match Score: 111.88

venturebeat
This tree search framework hits 98.7% on documents where vector search fails

A new open-source framework called PageIndex solves one of the old problems of retrieval-augmented generation (RAG): handling very long documents.The classic RAG workflow (chunk documents, calculate e [...]

Match Score: 111.59

venturebeat
From shiny object to sober reality: The vector database story, two years later

When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” in March 2024, the industry was awash in hype. Vector databases were positioned as the next big thing [...]

Match Score: 111.17

venturebeat
Perplexity takes its ‘Computer’ AI agent into the enterprise, taking aim at Microsoft and Salesforce

Perplexity, the AI-powered search company valued at $20 billion, announced on Wednesday at its inaugural Ask 2026 developer conference that its multi-model AI agent, Computer, is now available to ente [...]

Match Score: 109.77

venturebeat
How xMemory cuts token costs and context bloat in AI agents

Standard RAG pipelines break when enterprises try to use them for long-term, multi-session LLM agent deployments. This is a critical limitation as demand for persistent AI assistants grows.xMemory, a [...]

Match Score: 80.26

venturebeat
GAM takes aim at “context rot”: A dual-agent memory architecture that outperforms long-context LLMs

For all their superhuman power, today’s AI models suffer from a surprisingly human flaw: They forget. Give an AI assistant a sprawling conversation, a multi-step reasoning task or a project spanning [...]

Match Score: 79.60

venturebeat
GitHub leads the enterprise, Claude leads the pack—Cursor’s speed can’t close

In the race to deploy generative AI for coding, the fastest tools are not winning enterprise deals. A new VentureBeat analysis, combining a comprehensive survey of 86 engineering teams with our own ha [...]

Match Score: 74.71