Happening now: Our Founder and Chief Scientist, Edo Liberty, on stage at TechCrunch Disrupt with Senior Reporter Rebecca Szkutak. 🔥 Fireside discussion: 🧠 What's needed most right now to unlock AI? Knowledge and infrastructure purpose-built for AI. 🔍 What use case will we see advance the most because of this? Search. Yep, old school search. 📖 Read how Pinecone's latest infrastructure architecture powers accurate and performant AI search (and agents and recommenders) at scale: https://lnkd.in/evAtesxS
About us
Pinecone is the leading vector database for building accurate and performant AI applications at scale in production. Pinecone's mission is to make AI knowledgeable. More than 5000 customers across various industries have shipped AI applications faster and more confidently with Pinecone's developer-friendly technology. Pinecone is based in New York and raised $138M in funding from Andreessen Horowitz, ICONIQ, Menlo Ventures, and Wing Venture Capital. For more information, visit pinecone.io.
- Website
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https://www.pinecone.io/
External link for Pinecone
- Industry
- Software Development
- Company size
- 51-200 employees
- Headquarters
- New York, NY
- Type
- Privately Held
- Founded
- 2019
Locations
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Primary
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New York, NY 10001, US
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San Francisco, California, US
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Tel Aviv, IL
Employees at Pinecone
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Milen Dyankov
Developer Relations and Engineering Executive focused on empowering developers and teams. Experienced in leading enterprise projects, enhancing…
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Jenna Pederson
Developer relations @ Pinecone | Keynote speaker | Software engineer
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Andrew Naber
Fractional Marketing & Strategy Leader
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Mike Sefanov
Leading global communications, analyst relations, and various marketing streams at Pinecone
Updates
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💡 The real question isn’t “build or buy.” It’s: What’s the cost of building? In our latest article, we uncover the hidden cost of building AI infrastructure yourself: ⏰ 6–12 months in development before users see value 👩💻 1+ full-time engineer just to maintain it (≈$200K/year) ⌛ Lost time building infrastructure instead of improving the product 🤕 And the biggest risk of all: success — because that’s when the system starts to break This isn’t just Aquant’s story. It’s the reality for every team moving from AI prototype to production. 🔗 Read: https://lnkd.in/gW7Ti3cZ
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Introducing: Agentic Quickstart for Pinecone ‼️ Build vector search, RAG, and recommendation systems using AI coding agents instead of manual code snippets. Work with Claude Code or Cursor to: ✅ Implement best practices automatically ✅ Build faster with conversational workflows ✅ Focus on logic, not boilerplate Try it: https://lnkd.in/gGXDaszR
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Catch the n8n Community Livestream this Thursday! Our Staff Developer Advocate Jenna Pederson will demo a new n8n template with Pinecone Assistant for super easy and effective RAG. See below for details and to register 👇 and let us know in advance if you have any questions!
n8n Community Livestream: AI Guardrails, Pinecone and Community Highlights Join us on Thursday, October 30 for the October n8n Community Livestream to catch up on what’s new in the product and community. From our Nodes Team, n8n Product Manager David Arens will share early designs for Guardrails and Human-in-the-Loop nodes and give a first look at the new Command Bar. We’ll also hear from the Pinecone team with a live demo using their new n8n node, and meet Marrallisa Kreijkes, our new Amsterdam Ambassador, who will tell you about the n8n coworking sessions she’s been hosting. Come hang out, learn something new, and connect with others in the n8n community. 🙂 👉 Register here: https://luma.com/03p8f5ws
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Pinecone reposted this
Head of Developer Relations @ Appsmith, Founder/ Freelancer @ GreenFlux, LLC greenflux.github.io
What happens when you combine AI assistants with coffee? 𝐇𝐞𝐚𝐭𝐞𝐝 𝐏𝐢𝐧𝐞𝐜𝐨𝐧𝐞 𝐦𝐮𝐠! 𝐓𝐇𝐀𝐍𝐊𝐒 Pinecone! Last month for our local Code & Coffee meetup, I led a workshop on building AI assistants with Pinecone’s REST API and Python SDK. I’m a member of Pinecone’s Pioneers, a program that recognizes builders and educators in their community, and this is one of the awesome ways they show their support! Thank you Jocelyn Z. Matthews, Aaron Kao, Jenna Pederson, Arjun Patel and the rest of the Pinecone team! If you’re building with Pinecone and want to get more involved in the community, check out the Pinecone Pioneers! https://lnkd.in/eREhYXw5
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Importing from Google Cloud Storage to Pinecone is a game-changer! ◦ Enjoy streamlined development—just set it and forget it. ◦ Iterate faster with production-scale data. ◦ Save up to 6x on costs compared to traditional methods! Learn more 🔗 https://lnkd.in/gBxPY5QF
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Want a beginner's look at how to easily create a RAG agent with n8n and Pinecone? Check out Pinecone Pioneer Maddy French's short tutorial below that demonstrates podcast transcripts as a use case! 👇
Content Process Consultant | I help content teams streamline and scale efficiently through optimized content operations. | Founder @ The Blogsmith content agency | Bestselling Author "Writing for Humans and Robots”
Friday Show & Tell: Building a RAG upload agent in n8n for podcast transcripts (ft. Pinecone + Google Sheets) Several weeks ago, I shared my Replit RAG pipeline for processing URLs and web content. Aaron Kao, VP of Marketing at Pinecone, included it in his roundup of cool projects (link in comments 🙏). This week: A totally different approach for a different data type. The challenge: A client needed to process podcast transcripts stored as Google Docs, not web URLs. Each transcript needed to be chunked, embedded, and stored in Pinecone, along with associated episode metadata, for later retrieval by another agent. What I built: ✅ Automated Google Drive → Google Sheets → Pinecone pipeline ✅ Smart chunking with episode names, URLs, and metadata preservation ✅ Duplicate prevention (only embeds new transcripts to save API costs) ✅ Web scraping component that auto-finds episode URLs from titles ✅ Feeds into a social media agent that recommends relevant episodes (complete with the URL) Why n8n vs Replit this time? Client handoff. I needed something more maintainable for someone else to manage. n8n workflows are more modular, making them easier to troubleshoot without diving into code. The full system: This RAG component connects to agents that monitor social conversations, identify relevant topics, and auto-generate branded responses with episode recommendations. If you're building with RAG, the beauty is in the flexibility — same core concepts (chunk → embed → store → retrieve), totally different tools and data types depending on the use case. What are you building this week?
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Make knowledge hidden in your Google Docs discoverable and actionable. This post by John Ward, a Solutions Engineer here at Pinecone, shows how to load Google Docs into Pinecone Assistant, then ask natural-language questions and quickly surface answers across your notes, PRDs, design specs, and whatever else you store in Docs.
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In case you missed it: get 3 weeks of access to Pinecone’s Standard plan for free. Here's what you get: - $300 in credits to kickstart your journey - Full access to all Standard plan features for a comprehensive experience - Higher limits for testing at scale, ensuring you can innovate without constraints Learn more 🔗 https://lnkd.in/gd5vmngG
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Ready to revolutionize your AI strategy? RAG is evolving into complex, dynamic systems for AI. - It enhances decision-making with real-time data. - Supports accurate, grounded decisions for agents. - Provides fresh data without costly retraining. - Optimizes training for specialized models. - Integrates with other methods for greater accuracy. 🔗 Read more about why RAG remains relevant in 2025: https://lnkd.in/gYABq_eQ
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