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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
Meet Pinecone’s New VP of Engineering, Dr. Ram Sriharsha
2021-11-22 · via Pinecone

Pinecone is rapidly growing, which means we need people driving our teams that know how to not only lead at the helm but support us from the bottom up, so we can grow from a strong foundation. With experience in engineering, product management, and VP roles at the likes of Yahoo, Databricks, and Splunk, our new VP of Engineering, Dr. Ram Sriharsha, brings a wealth of knowledge to our team and is uniquely positioned to help execute on our lofty vision.

At Yahoo, he was both a principal software engineer and then research scientist; at Databricks, he was the product and engineering lead for the unified analytics platform for genomics; and, in his three years at Splunk, he played multiple roles including Sr Principal Scientist, VP Engineering and Distinguished Engineer.

With this wealth of experience, he could have done anything. So, why did he choose to join us? The decision was easy, Ram said.

“I tend to choose companies and technologies that are very cutting edge,” he said. “I try to place my bet on technologies that are going to be the way things are done. … From my own personal experience, I knew that semantic search over vector embeddings wasn’t a solved problem, and I recognized the significance of such a technology to unlocking new use cases around unstructured data. … Pinecone is working on exactly this.”

Jumping onto the Databricks and Splunk teams after seeing their potential, Ram’s intuition is keen and impressive. But, again, we were curious as to why a seemingly small, scrappy company was so attractive to him.

You can make more moves working on cutting edge technologies at a company of this size, where everyone’s contributions are ground-shaking, than you could even as VP at a big company, he said. Pinecone is the place to be, in his eyes.

“I really want to build systems and interface with users,” he said. “At Pinecone, you’re continually iterating and learning. In a big company, Version 0 can take many years to get out, and by then you’re not connected to the product anymore. At Pinecone, that’s not a problem. For example, one of our scientists fixed something and got the fix into production the very same night. This rarely happens at a big company.”

In addition, the technology Pinecone is creating was a no brainer for Ram, someone whose finger is always on the pulse. He doesn’t just want to work at a company that is pursuing greatness; he wants to get his hands dirty too. He watched as CEO Edo Liberty was on the forefront of Machine Learning research and knew he had to be involved. He wasn’t making the old stuff better; he was building a different type of database in a new domain.

The team is innovating on science, as well, Ram said.

“We are focused on building the world’s best vector database,” Ram said. “The use cases you can build on top of it are fascinating to me. We are writing our entire database in Rust, which is interesting — if we can shave off 20% memory usage, that’s savings in cost for customers. It’s very valuable for us to do that. What we are building is pushing the limits of databases themselves. How do we build algorithms that are accurate but allow us to scale? These are really amazing challenges in redesigning databases.”

After working on teams as the first employee, as well as stepping into high-level leadership roles right away, Ram knows what it takes to create a strong team and maintain that team for years to come. From the outset of his role, he wants to ensure he builds the best team of scientists possible and is looking for people who are just as excited as he is about transforming search using machine learning and vector databases.

When thinking about hiring, it’s not just about coding skill, either. Having an open culture and strong teamwork is vital, and that’s why he chose Pinecone, he said. Everyone has the opportunity to build and contribute, and he wants all engineers to feel like they’re getting their hands dirty and owning their own projects.

Even with a strong culture coming in, though, the team cannot remain complacent, especially as it begins to think about expansion and reaching out to different audiences for hiring. This includes working with a recruiting company focused on diversity, building relationships with organizations that support women in coding, like Grace Hopper, and constantly evolving to be welcoming and inclusive. The team has a long way to go with this, he said, but he’s excited to work on it.

Ram is set to take Pinecone into its next phase of greatness, as we work to build out and up. He hopes everyone is as excited about this journey as he is, and he invites anyone who resonates with it to come along.

“We are truly trying to build something fundamentally new here,” Ram said. “I really want that to resonate with people reading this. If you resonate with this, this is the place to be.”

We are hiring. Come work with Ram!