Pinecone Nexus

The knowledge engine for agents

Compiles enterprise data into governed knowledge once, then serves it on every call. Agents get more accurate, faster, lower cost, and trusted answers.

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More accurate, faster, lower cost, and trusted knowledge for agents

Nexus + KnowQL

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Traditional Agents

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How it works

Data in

Connectors bring in existing sources with no restructuring required. Nexus runs BYOC: curation and querying happen in your own cloud, on the models you choose, with no standing Pinecone access to the data.

Data Source

Docs & Wikis

Databases

APIs & SaaS

Object Storage

Code Repos

Pinecone Nexus

Compiled once

A Manifest defines the structure of a Context: the entities, their relationships, and the shape of the answers the work requires. An SME writes it, scoped to the job. Nexus curates the corpus once against that structure, producing the artifacts and the relationship graph retrieval throws away.

Raw Corpus

spec_0001.pdf

spec_0002.pdf

spec_0003.pdf

transcript.json

claims.xlsx

+1,795 more

Nexus

claim_idstring

filed_datedate

assigneeentity

prior_artcitation[]

statusenum

One KnowQL call

Agents query through KnowQL: the question, the output shape, the scope. One call replaces the retrieve-evaluate-re-retrieve loop and returns a typed, cited answer.

KnowQL

query PatentKnowledge {

prior_art(

claim: $claim

)

}

Tokens0.5K

Latency50ms

Steps1

Agentic RAG

embed(query)

vector_search(k=20)

llm_reason(chunks)

vector_search(k=20)

llm_reason(chunks)

vector_search(k=20)

llm_reason(chunks)

llm_assemble(context)

Tokens201K

Latency4 min

Steps8+

Grounded answers

Every response is cited per field, scored for confidence, and scoped to what the agent is permitted to see, with lineage back to source.

Answer · prior_art

Based on claims 1–4 of US10,234,891, prior artfound in EP2,891,203 (filed 2014-03-12) andJP2016-084521 directly anticipates element 2(b).Patentability risk: HIGH.

Sources

EP2,891,203§ 3.2 · claim mapping

JP2016-084521§ 1.1 · element overlap

US8,734,102§ 5 · prior art chain

typedcitedaccess-scoped

Data Source

Docs & Wikis

Databases

APIs & SaaS

Object Storage

Code Repos

Pinecone Nexus

The results hold across corpus types

Nexus was benchmarked against agentic RAG across three industries. The pattern held regardless of corpus shape, query type, or domain.

0%Faster latencyvs. agentic RAG

0%Fewer tokens per queryvs. agentic RAG

0%Accuracy improvementvs. agentic RAG

Patent search

1,800 technical spec documents · 2.3 GB

Fewer tokens: 97%
Faster latency: 77%
Accuracy improvement: +25%

M&A due diligence

90 documents · 10 folders · PDF, XLSX, markdown

Fewer tokens: 92%
Faster latency: 48%
Accuracy improvement: +14%

Revenue intelligence

217 call transcripts · structured JSON · 45 MB

Fewer tokens: 85%
Faster latency: 18%
Accuracy improvement: +94%

Nexus benchmarked against hybrid retrieval with agentic RAG loop. Accuracy scored by LLM judge on 0–1 scale against human-labeled expected answers.

KnowQL

Every application built its own data access layer before SQL existed. Agents are at that same point now. KnowQL gives them a shared interface for structured, grounded knowledge in a single call.

Intent

The goal and output schema for the query.

Filter

Deterministic predicates and access-control enforcement.

Provenance

Field-level citations with confidence tiers.

Output Shape

Typed fields returned exactly as specified.

Confidence

Grounded assertions separated from uncertain inference.

Budget

Depth tier, latency envelope, and token limit.

Trusted knowledge for agents

Every answer traces back to a source your team approved, updated the moment that source changes.

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