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Pinecone

Pinecone Assistant: A Managed Knowledge Layer for Production AI Applications Multi-domain RAG in n8n: why one knowledge base is not enough Allspice Transforms the Culinary Experience with Semantic Search Powered by Pinecone | Pinecone Building RAG workflows in n8n: choosing the right Pinecone node Knowledge needs a meta-knowledge layer Garbage Day: How Pinecone Safely Deletes Billions of Objects at Scale When "Performance" Means Two Different Things Pinecone BYOC: Pinecone in your AWS, GCP, or Azure account, no vendor access True, Relevant, and Wrong: The Applicability Problem in RAG Use the Pinecone Plugin for Claude Code to develop AI Applications Faster Millions at Stake: How Melange's High-Recall Retrieval Prevents Litigation Collapse Powering High-stakes Patent Search at Scale: How Melange Built a Reliable AI System on Pinecone | Pinecone Pinecone Assistant Node in n8n: Turn Any Data Source Into Knowledge RAG with Access Control Pinecone Dedicated Read Nodes are now in Public Preview Inside Pinecone: Slab Architecture New Bulk Data Operations: Update, Delete, and Fetch by Metadata The Hidden Cost of Building: Lessons from Aquant Simplifying Vector Embeddings with Pinecone Integrated Inference Capabilities Pinecone joins Microsoft Marketplace as a Launch Partner GTM Engineering: Clay + Pinecone for AI-powered Sales Outbound Build an AI knowledge assistant with Google Docs and Pinecone Moving Pinecone forward with Ash Ashutosh as CEO and Edo spearheading our growing AI ambitions as Chief Scientist Pinecone Founder Edo Liberty to Spearhead Pinecone’s Growing AI Ambitions; Appoints Ash Ashutosh as CEO to Expand Vector Database Market Leadership Fast, Accurate Retrieval for Creators at Scale: Delphi’s Path Toward a Million Conversational Agents with Pinecone | Pinecone Announcing Pinecone Pioneers: A Program for Builders, Organizers, and Community Leaders What is Context Engineering? Chunking Strategies for LLM Applications Beyond the hype: Why RAG remains essential for modern AI Obviant Makes 30% More Accurate Defense Acquisition Recommendations Combining Sparse and Dense Retrieval with Pinecone | Pinecone Build more knowledgeable AI applications with new LLMs and greater control in Pinecone Assistant #NYTECHWEEK 2025 Retrieval-Augmented Generation (RAG) Accurate and Efficient Metadata Filtering in Pinecone’s Serverless Vector Database | Pinecone Terminal X AI Agents, Powered by Pinecone, Turn Complex Financial Data Into Production-grade Insights at Scale | Pinecone Aquant Delivers Scalable, Expert-level Service Intelligence with Pinecone | Pinecone Cascading retrieval with multi-vector representations: balancing efficiency and effectiveness Vector databases aren't just for large-scale enterprise AI Unveiling DIME: Reproducibility, Scalability, and Formal Analysis of Dimension Importance Estimation for Dense Retrieval | Pinecone Fast and Effective Early Termination for Simple Ranking Functions | Pinecone Domain-specific AI Agents at Scale: CustomGPT.ai Serves 10,000+ Customers with Pinecone | Pinecone Using Pinecone asynchronously with FastAPI A Flexible Resource for Top-Weighted Comparisons Between Sets and Rankings | Pinecone Build secure, scalable agentic AI workflows with Rubrik Annapurna and Pinecone Tool up: Pinecone’s first MCP servers are here Add context to your agent with Pinecone Assistant MCP remote server E2Rank: Efficient and Effective Layer-wise Reranking | Pinecone ColBERT-serve: Efficient Multi-Stage Memory-Mapped Scoring | Pinecone Efficient Constant-Space Multi-Vector Retrieval | Pinecone How Vanguard Worked with Pinecone to Boost Customer Support with Faster Calls and 12% More Accurate Responses | Pinecone Pinecone Named to Fast Company's Annual List of the World's Most Innovative Companies of 2025 Launch Week: Pinecone for agents, search, recommendations, and more Optimizing Pinecone for agents (and more) Retrieval Inference for scale and performance How 1up Turns Sales Reps Into Product Experts with Pinecone | Pinecone Don’t be dense: Launching sparse indexes in Pinecone Unlock High-Precision Keyword Search with pinecone-sparse-english-v0 Evolving Pinecone's architecture to meet the demands of Knowledgeable AI Pinpoint references faster with citation highlights in Pinecone Assistant Bringing the leading vector database to your cloud Getting started with llama-text-embed-v2 Natural Language Counterfactual Explanations for Graphs Using Large Language Models | Pinecone Easily build knowledgeable chat and agent-based applications in minutes with Pinecone Assistant, now generally available How to build an agentic, chat or RAG knowledge system using Pinecone Assistant Real-time RAG with Pinecone and Estuary Flow BigQuery to Pinecone in Real-Time with Estuary Flow Stravito Turns Market and Consumer Data Into Actionable Insights with Pinecone Inference | Pinecone Accelerate prototyping and development with Pinecone Local First-of-its-kind Pinecone Knowledge Platform to Power Best-in-class Retrieval for Customers Introducing integrated inference: Embed, rerank, and retrieve your data with a single API Strengthening security and increasing control with CMEK and API key roles Introducing Pinecone Rerank V0 Introducing cascading retrieval: Unifying dense and sparse with reranking From Idea to Action: How Pinecone Assistant Meaningfully Accelerates AI Business Building AI apps on Azure with Pinecone just got a lot easier Building a reliable, curated, and accurate RAG system with Cleanlab and Pinecone Four features of the Assistant API you aren't using - but should Deploying Pinecone with Infrastructure as Code (IaC) Streamlining CI/CD with Pinecone Local September 2024 Product Update Results of the Big ANN: NeurIPS'23 competition | Pinecone Introducing import from object storage for more efficient data transfer to Pinecone serverless Simplify, enhance, and evaluate RAG development with Pinecone Assistant, now in public preview Vectors and Graphs: Better Together August 2024 Product Update Pinecone Helps Deep Talk Deliver World-Class AI Assistants with Lower Engineering Overhead | Pinecone Assembled Delivers Better, Faster AI- Driven Support with Pinecone | Pinecone Llama 3.1 Agent using LangGraph and Ollama Build knowledgeable AI with Pinecone serverless, now generally available on Microsoft Azure Pinecone serverless is now generally available on Google Cloud, adding knowledge to AI assistants and other applications Accelerating Legal Discovery and Analysis with Pinecone and Voyage AI Bridging Dense and Sparse Maximum Inner Product Search | Pinecone Refine Retrieval Quality with Pinecone Rerank Introducing reranking to Pinecone Inference to simplify building accurate AI July 2024 Product Update Connect to Pinecone within your platform to enable a seamless AI development experience Introducing Pinecone API Versioning RAG Brag with Inkeep Co-Founder Nick Gomez LangGraph and Research Agents Introducing Pinecone Inference to streamline your AI workflow
Pinecone leaves stealth with $10M, launches first serverless vector database for machine learning
2021-01-28 · via Pinecone

SUNNYVALE, California, Jan. 27, 2021 /PRNewswire/ -- PineconeSystems Inc., a machine learning (ML) cloud infrastructure company, left stealth today with $10m in seed funding. The investment was led by Wing Venture Capital, and Wing's Founding Partner, Peter Wagner, will be joining the Pinecone board. Built by the team behind Amazon SageMaker and founded by scientist and former AWS Director Edo Liberty, Pinecone makes large-scale real-time inference as simple as querying a database. It is available for self-onboarding today.

Pinecone builds infrastructure to enable the next wave of artificial intelligence (AI) applications in the cloud. The company's vector database supports large scale production deployments of real-time applications such as personalization, semantic text search, image retrieval, data fusion, deduplication, recommendation, and anomaly detection. Each of these is a multi-billion dollar market today and projected to grow 30%+ YoY for the foreseeable future as more and more companies adopt ML technologies.

Modern ML and AI use vectors (aka embeddings) to represent data such as documents, videos, and user behaviors. Applications that need to accurately filter and rank large collections of such vectors in real time require a highly specialized data infrastructure. Existing databases or search engines aren't a good fit; they are designed for tables and documents, not vectors. In-house systems that use open source libraries are expensive to build and hard to maintain. This forces developers to constantly compromise between speed, accuracy, stability, and scale.

Using Pinecone's unique vector database, ML and data infrastructure engineers can dynamically transform and index billions of high-dimensional vectors. They can answer queries like nearest neighbor and max-dot-product search accurately and in milliseconds. Pinecone's database is 100% serverless and API-driven, which means customers always have the computing resources they need, when they need them, without having to worry about infrastructure or maintenance. Simple self-onboarding and consumption-based pricing let companies build proofs of concept with very little overhead and then scale effortlessly.

One of the largest retailers in the world reports using Pinecone to serve real-time shopping recommendations based on their own deep learning models. They saw an immediate 18.5% lift in revenue per recommendation compared with their previous solution. "Visionary leaders in many companies are working hard to transform their business with machine learning. Pinecone gives them technology they need which, until today, was reserved to a few tech giants,'' said Edo Liberty, Founder and CEO of Pinecone.

"The modern Enterprise is built on data and powered by AI. The Data Cloud has emerged as its foundation with the ascendance of Snowflake. Pinecone is poised to unleash data teams and their ML-based applications in a similar fashion," said Peter Wagner, Founding Partner, Wing Venture Capital.

The Pinecone team knows how to build production grade ML systems. Pinecone's founder and CEO, Edo Liberty, ran Yahoo's Scalable Machine Learning Platforms group and later Amazon AI Labs which included a team building Amazon SageMaker. Engineering is led by Amir Sadoughi, a senior AWS engineer and leader who spearheaded the creation of Amazon SageMaker.

About Pinecone

Pinecone has built the first serverless vector database to enable the next generation of artificial intelligence (AI) applications in the cloud. Its engineers built ML platforms at AWS (Amazon SageMaker), Facebook, Yahoo, and Google and its scientists published more than 100 academic papers and patents on machine learning, data science, systems, and algorithms. Pinecone is backed by Wing Venture Capital and operates in Silicon Valley, New York and Tel Aviv. For more information, see https://www.pinecone.io.

About Wing Venture Capital

Wing works with ambitious founders to enable the Modern Enterprise, which is an agile workplace built on data and powered by AI. We invest early, before it's obvious, leading Seed and Series A financings and engaging deeply with our signature company-building skills and resources. The body of work of Wing's award-winning team spans more than two decades and dozens of successful early-stage companies, 22 of which have gone on to achieve billion-dollar-plus outcomes following IPOs or acquisitions. The current Wing portfolio includes some of today's most important enterprise technology companies such as Snowflake, Cohesity and Gong. Web Twitter LinkedIn Medium

Media Contact
Mike Sefanov
mike.s@pinecone.io
Sr. Director, Communications