Pinecone Nexus Is Now in Public Preview | Pinecone

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Pinecone Nexus Is Now in Public Preview

Jasmeet Singh Gujral, Lea Wang-Tomic

Jul 1, 2026

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Seven weeks ago, we announced early access to Pinecone Nexus, to deliver knowledge to AI agents. We’ve since partnered with a broad cohort of users spanning massive data, high complexity, and stringent accuracy, latency and cost requirements. Today, we are opening up public preview.

Distributed enterprise knowledge, compiled into something agents can rely on.

Agents built around frontier models are great at world knowledge, complex reasoning, and synthesizing information across disciplines. Finding specific information buried across files is a search problem, one vector databases have been chipping away at for years.

There's a third kind of knowledge that neither of those touches: business context. It's what a three-year employee knows without searching; which internal process doc reflects how things actually work today, what the quarterly goals mean in practice, how a policy in one department connects to a decision in another. It lives distributed across your enterprise in contracts, wikis, HR docs, meeting notes, support tickets, and financial records. Most of it has been written down somewhere, often many times over, but written down isn't the same as compiled, and an agent walking into a task needs the latter.

Every time an agent starts a task, it starts from zero on all of this. It can piece things together on the fly, but the cost shows up in tokens, in time, and in answers that are half right.

Pinecone Nexus is a knowledge engine that closes that gap. It compiles an enterprise's distributed knowledge into a structured layer agents can query directly, shifting token spend out of the per-query retrieval loop and into a one-time curation step.

Today, we're opening it up to anyone with a business use case. Pinecone Nexus is now in Public Preview.

What's Ready

Starting today, request access to Pinecone Nexus and see what curated knowledge does for agentic workloads on the dimensions that matter: cost, speed, and accuracy.

Nexus is built for bounded corpora where agents need to reason across documents. It's at its best on the kind of corpus where a single question touches dozens of files and standard retrieval starts falling apart.

There are two ways in:

Preview Playground: request access today and get a live environment to connect your data, design a Context, and run real queries. It's enough to validate the approach on something real before committing to a deployment.

BYOC Deployment: for production workloads, request a dedicated deployment. The cluster runs in your VPC with no shared infrastructure. Pinecone never has access to your data, your documents don't leave your infrastructure, and there's no shared compute with other tenants. Our deliberate focus on this deployment model is to align with the needs of enterprises where data residency, security, and compliance are non-negotiable.

See Nexus in Action

The demo above uses a synthetic financial services corpus of a household's complete set of financial records including their goals, plans, meeting notes, and portfolio documents. It’s the kind of corpus where a question like, "How are the Chens tracking against their retirement goals?" touches dozens of documents across multiple years, and unlike Nexus, a standard retrieval system would return chunks instead of fully-formed answers.

The Knowledge Engine, Explained

The results enterprises in Early Access are seeing aren’t a product of better prompting or a smarter model. They come from a different approach to how knowledge is organized before any query arrives. (See our previous blog, Nexus in the Wild: Real Results from Our Early Access Customers)

Connectors handle ingestion: Local file upload, Box, and Microsoft OneLake are live today, with Google Drive, Slack, GitHub, Notion, Confluence, and S3 close behind.

A Workspace is the top-level container: the boundary around a team or business unit. Connectors and other shared resources live at the Workspace level, so the natural mental model is one Workspace per team that owns its own data sources and access controls: a support org, a legal team, a data science group.

Inside a Workspace, your data is organized into Contexts: one per dataset or knowledge domain. Sources flow through Nexus's curation layer, guided by a Manifest that turns raw documents into structured knowledge artifacts tuned to the domain. The Manifest is the blueprint for how the corpus should be understood.

Tasks run the actual work inside isolated Sandboxes: import to ingest and clean sources, curate to build artifacts per the Manifest, and search to query via KnowQL. Each workflow can employ an ensemble of models, with the right model picked for each step. Everything surfaces through a single interface: KnowQL, the Query Interface into Nexus. Agents, chatbots, AI search applications, recommendation systems all tap into the same knowledge layer through one consistent surface.

SMEs in the loop

The people who know what questions matter, and what knowledge structure would answer them, usually aren't the engineers building the pipeline.They're the patent attorney who knows how patent standards are organized. The M&A analyst who knows which document categories need to be cross-referenced for a clean diligence answer. The revenue leader who knows which signals in a Gong transcript actually predict churn.

The Manifest is how we bring that person into the loop. A subject matter expert can design a blueprint defining the artifact types and relationships that encode their domain knowledge into the curation layer before any query runs. The agent isn't left to figure out the structure of the corpus at query time. It inherits the SME's understanding of it.

This is a different model than prompt engineering. The model isn't being coached at query time, it's being handed a knowledge layer that was designed by someone who actually knows the domain. The difference is between an agent that retrieves the most relevant chunk and one that reasons over a corpus already structured by someone who understood it. The answer comes out precise and fast, with the reasoning pointed in the right direction from the start.

Knowledge doesn't stay static. Regulations change, products evolve, and a corpus that was accurate six months ago develops what we call knowledge drift. Nexus lets SMEs re-curate: update the Manifest to reflect new requirements, rebuild the knowledge layer, ship. The agent stays current because the person who understands the domain stays in control of the knowledge layer.

More Benchmarks

Since opening Early Access, we've kept benchmarking. Different industries, different corpus shapes, same question: does the Manifest-driven curation model hold up when the knowledge problem gets harder or stranger? Three of those benchmarks are below.

Q2: Support Knowledge Base for Financial Services

Domain: Digital Banking / Financial Technology

Q2 builds digital infrastructure for financial institutions. Their support team works through thousands of technical knowledge base articles as part of standard triage, searching them every time a client calls in with an issue.

The articles themselves are easy to find. Most real support questions, though, have answers that live across several of them, and a wrong answer to a financial institution carries real downside, so accuracy ends up being the deciding criterion.

Q2 ran this benchmark themselves. They got early access to the preview environment, ingested their sources, designed a Manifest from a pre-built template, curated the artifacts, and ran the evaluation without Pinecone team in the loop.

Corpus: A sample of Q2's internal technical support knowledge base, covering the breadth of questions their support team handles daily.

Eval set: 20 questions across 6 categories: single-article lookups, two-article reasoning, multi-article synthesis, persona-based support scenarios, and customer cases. Designed to mirror the real question types Q2's support teams face daily.

Results:

Metric Result

F1 Score 95%

Avg Recall (1–5) 4.70

Avg Precision (1–5) 4.70

For Q2, accuracy was everything, and 95% on their hardest questions was the number that made the case. Jesse Barbour, Chief Data Scientist at Q2, put it best:

Q2 Quote

"We can easily stand up a vector database and run RAG (and agentic search) over our documentation corpus. The hard part is getting an agent to reliably and efficiently assemble the right knowledge for genuinely difficult questions. In our own evaluations, Pinecone Nexus answered a set of complex support and compliance questions with 95% accuracy. And the fact that it has the ability to do it while keeping token costs down, not driving them up, makes it even more compelling." — Jesse Barbour, Chief Data Scientist, Q2

A Legal Research AI Company: Coverage and Reliability Over EU Case Law

Domain: Legal Research / LegalTech

This benchmark, run with a company building AI tools for legal research, tested Nexus on the task at the center of their product: answering substantive legal questions over EU case law and legislation. Nexus ran against an agentic-RAG baseline and a coding agent, with the same model composing every answer. Only the retrieval architecture changed, so any difference in the results is attributable to it.

The questions span the shapes a real legal researcher asks: looking up what a specific provision requires, tracing how a doctrine has been applied across cases, assembling every relevant precedent on a topic, and reasoning across multiple regimes at once. These are hard for standard retrieval for the same reason municipal records and support tickets are: the answer is rarely sitting in one document.

Corpus: 30,000 documents (~1.1 GB) of CJEU and General Court case law plus EU legislation, spanning 2021–2026.

Eval set: 35 legal research questions across 8 categories, including provision lookups, doctrine synthesis, cross-case and cross-regime reasoning, coverage and enumeration questions, precedent chains, and out-of-corpus negative controls.

Results:

Metric Nexus Agentic RAG Coding Agent

Completion rate 100% 66% 6%

Accuracy 87% 45% 4%

Avg Tokens per query 9k 80k (~9x) 135k (~15x)

The completion gap tells most of the story. The coding agent completed just 2 of 35 questions, browsing 30,000 documents by listing and reading files doesn't scale to a corpus this size. The RAG baseline did better but fell apart on doctrine synthesis, cross-case reasoning, and coverage questions, the shapes that require many sources assembled into one answer. On 12 of those, it never made it to composing a response at all. Nexus completed every question in the set, and even on the ones both systems answered, it was more accurate.

On a control question about U.S. securities law, asked of a corpus that contains only EU law, Nexus recognized the question was out of scope and said so. The other two systems fabricated answers.

A Leading Data Protection and Security Vendor: Cross-Document Reasoning Over Municipal Records

Domain: Enterprise Data Management

This benchmark, run with a data protection and security vendor, tested Nexus on a corpus where the documents themselves aren't technically complex, but they're structured so that almost no question can be answered from a single one of them.The corpus is 598 documents of municipal meeting minutes from a major city, spanning 2022–2025 across 13 governing bodies. Almost no question in the eval set can be answered from a single document. Counting a council member's attendance requires reasoning across roughly 40 documents. Resolving a project location requires joining an agenda item to its parcel number, applicant, and owner. Reading a motion's full vote requires extracting a structured roster from a narrative paragraph.

These are exactly the queries that expose the problem with standard re

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