{"id":93449,"date":"2026-07-02T16:24:43","date_gmt":"2026-07-02T16:24:43","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/93449\/"},"modified":"2026-07-02T16:24:43","modified_gmt":"2026-07-02T16:24:43","slug":"pinecone-releases-nexus-into-public-preview-to-bring-business-knowledge-to-ai-agents","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/93449\/","title":{"rendered":"Pinecone releases Nexus into public preview to bring business knowledge to AI agents"},"content":{"rendered":"

Pinecone Systems Inc.<\/a>, an artificial intelligence infrastructure company providing fully managed vector databases, today announced the launch of Pinecone Nexus into public preview<\/a>, which curates and distributes enterprise knowledge for AI agents.<\/p>\n

The era of agentic AI is here and it is built on top of data. How that data is delivered to AI agents is becoming the pipeline that decides the difference between accurate answers and forgetfulness. The industry continues to work hard on how to take raw information, package it and deliver it in a way that AI can use.<\/p>\n

Employees already readily handle data from multiple sources by knowing where to look, thanks to an internal compass rooted in institutional memory. Ask them a question and they recollect that they need to look in wikis, human resources docs, meeting notes, support tickets and financial records to pull out what they need. AI agents don\u2019t come with this level of scrutiny, instead they require frameworks, graphs and systems of knowledge to rebuild this sort of sense of \u201cI don\u2019t know this off the top of my head, but I know where to find it\u201d every time you call them.<\/p>\n

Starting today, Pinecone Nexus is available in public preview and it is designed to curate access to knowledge for AI agents, allowing them to reason across dozens of files at once, without grabbing small chunks.<\/p>\n

What Nexus brings to the industry<\/p>\n

AI agents need access to data, so Nexus first provides connectors to retrieve and import it. This includes local file upload, Box and Microsoft OneLake live today. In the pipeline coming soon are Google Drive, Slack, GitHub, Notion, Confluence and S3.<\/p>\n

The engineering team setting up an AI agent works within a project container called a Workspace that organizes data into Contexts, which define data as knowledge sets or domains. Individual teams build their own workspace and use that to maintain their own cognitive knowledge base for their AI agents.<\/p>\n

This flows through Nexus\u2019s curation layer, which uses templates called Manifests to turn raw documents into knowledge artifacts that match their contents.<\/p>\n

Essentially, Nexus provides a framework that explains the underlying \u201cwhere to find what you\u2019re looking for\u201d for the AI agent. This is similar to how an employee with over three years at the company knows which part of the financial archives to consult for acquisition data or where to look for the most recent business logic for the web portal.<\/p>\n

This is different from prompt engineering, where users or engineers must teach the agent where to look at query time, whereas Manifests allow the agent to understand where knowledge lives during its curation. This brings domain experts into the loop as information is categorized, ensuring it\u2019s ready when the AI agent reaches for it.<\/p>\n

In benchmarks, Pinecone said Nexus demonstrated high performance and accuracy.<\/p>\n

\u201cWe can easily stand up a vector database and run RAG (and agentic search) over our documentation corpus,\u201d Jesse Barbour, Chief Data Scientist of Q2 Holdings Inc., an Austin-based financial technology solutions company. \u201cThe hard part is getting an agent to reliably and efficiently assemble the right knowledge for genuinely difficult questions.\u201d<\/p>\n

According to Barbour, Nexus answered complex support questions with 95% accuracy. It also kept token costs low, making it an enticing knowledge layer as AI inference prices rise.<\/p>\n

In another example, an unnamed data protection and security vendor tested Nexus on a tranche of documents with widespread information, including municipal meeting minutes, meaning that questions could cross multiple documents and chunks.<\/p>\n

Curating 598 documents into 12 structured artifact types cost $2.31 and took 34 minutes. Subsequent queries achieved around 90% accuracy, compared to a 65% baseline for the industry-standard retrieval-augmented generation pipeline.<\/p>\n

Image: Shutterstock\/Nepool<\/a><\/p>\n

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SiliconANGLE Media is a recognized leader in digital media innovation, uniting breakthrough technology, strategic insights and real-time audience engagement. As the parent company of SiliconANGLE<\/a>, theCUBE Network<\/a>, theCUBE Research<\/a>, CUBE365<\/a>, theCUBE AI<\/a> and theCUBE SuperStudios \u2014 with flagship locations in Silicon Valley and the New York Stock Exchange \u2014 SiliconANGLE Media operates at the intersection of media, technology and AI.<\/p>\n

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