Pinecone

Pinecone is the knowledge infrastructure for AI at scale.

[Pinecone @pinecone]

Pinecone Nexus is in Public Preview.

A knowledge engine that compiles untapped, distributed enterprise data into a layer agents query directly. Answers get faster, cheaper, and more accurate.

Read more: https://t.co/7cEu1tfM2Z

[Pinecone @pinecone]

Come build with Pinecone https://t.co/t2EHllVJbC

[Pinecone @pinecone]

Jeff Zhu (VP of Product) and Jasmeet Singh Gujral (Principal PM) join @jennapederson and Arjun Patel on the livestream today to talk about what we've shipped and what's coming.

Then we get into the usual mix: live builds, industry takes, chat Q&A. Join us right here @pinecone.

🗓 TODAY, July 1 - 1pm ET / 10am PT

[Pinecone @pinecone]

Our Founder and Chief Scientist @EdoLiberty just kicked off the Search & Retrieval track @aiDotEngineer. "Agents don't need to be smarter. They need a Knowledge Layer." We've got a big update on this coming tomorrow...

[Pinecone @pinecone]

Take a look at the repo below, and let us know what you think!

https://t.co/DWAOLxVxrz

[Pinecone @pinecone]

Building Agent Skills is great, but testing them clearly and across agents is difficult. The Pinecone DevRel team has released Cultivar, a CLI tool and agent skill designed to solve this problem.

With Cultivar, you can:
- design a skill
- write up programmatic, and LLM-grader-like tests for skills to pass
- run it across different agents across multiple sandboxed containers using @modal or locally
- evaluate the traces and generated code, to iterate further

Install Cultivar using uv: uv tool install cultivar

[Pinecone @pinecone]

ZoomInfo rebuilt contact discovery as real-time recommendation, now running on Pinecone DRN: 50x peak requests, 2x recall, 50% more engagement.
Join engineers from Pinecone and ZoomInof as they discuss the path from POC to production. June 24, 9AM PT.
https://t.co/65vSVramZd

[Pinecone @pinecone]

ZoomInfo recommends the right contacts the moment you open a company profile. Thousands of requests per second, under a second end-to-end.

With Pinecone: 50x more peak requests, 2x relevancy and recall, 50% more engagement.

Technical walkthrough on Dedicated Read Nodes, June 24, 9 AM PT: https://t.co/peO9NqlqeT

[Pinecone @pinecone]

Builder plan now supports serverless indexes in every generally available region across AWS, GCP, and Azure. Previously limited to AWS us-east-1.
Same regions as Standard and Enterprise: us-west-2, eu-west-1, eu-central-1, ap-southeast-1, us-central1, europe-west4, eastus2, and more. Lower latency, data residency options, still $20/month flat with no usage overages.

See the release note: https://t.co/suQln8riOO

[Jennifer Reif @JMHReif]

🎙️New Breaktime Tech Talks! Talked with @RoieSchwabco from @pinecone about why your AI agents are burning excess tokens, and what to do about it. Also: naive RAG, "Franken answers," and building low-code AI apps.
🎧 https://t.co/7LDL1WVeHP
#btt #RAG #Pinecone

[Pinecone @pinecone]

Pinecone + @PulumiCorp: an evening of talks on how AI systems get built and run under the hood.

The infra behind vector search and RAG, IaC done right, and an AI running coach that lives in Slack and merges messy real-world data into context the model can use.

Demos, Q&A, hangout after.
Thu June 18, 5 PM, NYC: https://t.co/66r6yIOP1p

[Pinecone @pinecone]

Proud to have Pinecone Nexus be a part of this along with our friends at @LangChain, @tavilyai, and @guardrails_ai!

[Nebius @nebiusai]

Most #AIAgents don't fail because of the model. They fail because of the infrastructure around it.

Introducing the Nebius Agents Blueprint: an open architecture for building, operating, and continuously improving agents in production.
https://t.co/r18cYSGNRn

[Pinecone @pinecone]

Most AI agents reset every session. @JenovaAIAgent's don't.

Longest session on their platform: 16M tokens. All of it retrievable in <10ms via Pinecone vector retrieval.

Result: Fast ramp to $1M+ ARR, 200K+ users, 10x revenue in 5 months. Nearly all organic.

"For an agent platform, the quality of the knowledge layer determines whether users stay or leave. Pinecone is what lets us store everything a user has ever worked on and retrieve exactly the right piece of it in milliseconds. That's the foundation our entire product is built on." — @boriswang01, Founder

Knowledge is the moat. Case study: https://t.co/JxeGGz0v9N

[Pinecone @pinecone]

Managed SaaS needs vendor access security review won't approve. Self-hosted hands your team a vector database to run.

Pinecone BYOC runs the data plane inside your own AWS, GCP, or Azure account. Zero vendor access. Vectors stay in your VPC.

60-min session, June 17, 9 AM PT: https://t.co/VFuhEI93h8

[Holger Müller #EnterpriseAcceleration @holgermu]

And @DevangSachdev announced the @nebiusai Agents Blueprint and consists of

- @LangChain Deep Agents
- @tavilyai
- @pinecone
- @guardrails_ai Snowglobe
- Langchain Langsmith

#NebiusInflection26

[Pinecone @pinecone]

@apify How to train an AI chatbot: https://t.co/9l16fAFmxA

[Pinecone @pinecone]

Your AI chatbot is only as good as the data behind it.

This n8n template from our friends at @apify shows you how to wire up a RAG pipeline using Apify + Pinecone + Gemini so your chatbot can answer questions grounded in your actual website content, not just what the model knows.

How it works:
→ Apify's Website Content Crawler scrapes your site on a schedule
→ Content gets chunked and indexed into Pinecone as vector embeddings
→ Gemini retrieves the right context and generates accurate answers

The result: a support chatbot that stays current automatically, with no manual data wrangling.

🔗 @n8n_io template: https://t.co/IMmyreQ0Ot

And if you want to go deeper on the data layer, Apify's blog post covers the full approach (linked in the replies).

[Pinecone @pinecone]

Four weeks, three enterprise customers, same pattern across all three.
Most inference spend goes to retrieval loops. A generic index carries no knowledge of domain, query types, or task structure, so the loop runs before the model can reason. Nexus compiles before the query.
Full results: https://t.co/RhUItkc6BR

[Pinecone @pinecone]

Bulk import is now free up to 1 TB.

Reads directly from S3, GCS, or Azure Blob into the index builder. Standard and Enterprise get a $250 credit applied automatically.

After 1 TB: $0.25/GB, down 75%.

https://t.co/f812koIxLm

[ashashutosh @ashashutosh]

Today at Microsoft Build: Pinecone Nexus now integrates with Microsoft OneLake.

Your enterprise data, turned into task-scoped, governed, cited knowledge your agents can use directly. 95%+ token reduction. 30x faster execution. Completion rates above 90%.

https://t.co/R7NcRerp3Y