Pinecone Blog

← Blog Pinecone BYOC: Trusted AI Knowledge in the Customer Cloud Jeff Zhu, Joerg Schad Sep 23, 2026 Product Share: Today, we are announcing the general availability of Pinecone Bring Your Own Cloud (BYOC) on AWS, Google…

← Blog VQ-bench: a Composable Vector Quantization Framework Ashwin Padaki, Amir Ingber, Edo Liberty Sep 17, 2026 EngineeringResearch Share: Before a vector database can search vectors, it has to store them. But storing…

← Blog Best Knowledge Engine Platforms in 2026 A practical guide to knowledge engines, graph platforms, enterprise search, agent memory, managed retrieval, and composable stacks Asaf Ashirov, Aaron Kao, Jasmeet Singh Gu…

← Blog Full-Text Search is Now Generally Available In Pinecone Database Manish Talreja, Gavin Johnson Sep 9, 2026 Product Share: If you run search, recommendations, RAG, or agents, the queries hitting your retrieval sys…

← Blog Our support agent now solves most tickets without a human Jackson Gold Sep 1, 2026 Engineering Share: Every support ticket used to start from nothing. A customer would write in, the default agent provided by our…

← Blog One Year In, and Just Getting Started Ash Ashutosh Aug 26, 2026 Company Share: A year ago, I joined Pinecone with a simple thesis: every company sits on data its AI cannot use, and whoever closes that gap wins th…

← Blog Designing Agent-Friendly APIs Joerg Schad Aug 12, 2026 Engineering Share: At Pinecone we develop our products with both human and agentic consumers in mind. Designing for that second kind of consumer taught the t…

← Blog The Ceiling Was Never the Model Sierra AI built an internal AI agent and named it Pinecone. We're flattered. So we took the benchmark Sierra built and beat it. Name confusion aside, Sierra and Pinecone landed on…

← Blog Nexus GA: It's the Knowledge, Not the Models Pinecone Nexus Is Now Generally Available Jasmeet Singh Gujral, Siva Ragavan Aug 6, 2026 Product Share: Five weeks ago we opened Pinecone Nexus to Public Preview. Toda…

Semantic search returns results that are close in meaning but may miss unstated context, leading agents to act on incorrect information. Pinecone's new text match filters, now in public preview, allow lexical filtering to scope search results without requiring pre-labeled metadata for every potential query dimension. This reduces errors in agentic pipelines where a bad retrieval can trigger a chain of wasted tool calls.

Pinecone Nexus is a knowledge engine for AI agents that compiles distributed enterprise knowledge into a structured layer, reducing token cost per query while improving accuracy and speed. The article explains its workings, benchmark results, and how to access the public preview.

John Ward, a Solutions Engineer at Pinecone, shares a modular workflow for generating test data using Parquet files and local embedding models. The approach separates data preparation from embedding generation, enabling scalable testing across different hardware.

Pinecone Nexus, a knowledge engine that compiles structured artifacts before queries, delivers dramatic improvements in accuracy, latency, and cost for enterprise AI. Three case studies show: Melange patent search achieved 25% higher accuracy, 77% lower latency, and 97% fewer tokens; M&A due diligence saw 14% higher accuracy, 48% lower latency, and 92% fewer tokens; Gong transcript revenue intelligence improved accuracy by 94%, with 18% lower latency and 85% fewer tokens.

Pinecone shares the journey of building AskData, an internal AI data agent that slashed token consumption by over 90% and reduced query turns by 78%. The article details the evolution from initial coding agent experiments to a knowledge-layer-driven V1 system, and finally to a unified pipeline on Pinecone Nexus, solving the 'last mile' knowledge gap between business language and SQL.

Pinecone offers a deployable template that automates the pipeline from Azure Blob Storage to a serverless Pinecone index, enabling fast semantic search and AI retrieval for enterprise data.

The article argues that the bottleneck for AI agents is not model capability but context engineering. Using the example of a market-intelligence agent analyzing 10-K filings, it illustrates the inefficiency of current approaches (Agentic RAG and Coding Agent in a sandbox). Pinecone introduces Nexus, a Knowledge Engine that automates context building via a Context Compiler and uses KnowQL declarative queries to improve accuracy, latency, and cost.

Pinecone Marketplace enables teams to turn existing knowledge—docs, manuals, policies—into AI-powered applications without engineering overhead. It uses templates, cites sources, and publishes instantly, bridging the gap between traditional search and custom-built solutions.

Pinecone introduces Nexus, a knowledge engine for AI agents that shifts from retrieval to knowledge compilation, improving task completion rates and reducing token consumption. Also launches KnowQL query language and Pinecone Marketplace.