Pinecone Systems team (Photo Pinecone Systems)
Pinecone Systems, an Israeli startup that develops new types of search infrastructure, raised $28 million in a Series A funding round led by Menlo Ventures. The company raised $10 million in seed funding in January 2021. So, Pinecone System’s total investment to date is $38 million.
Founded in 2019 by former Amazon developers, Pinecone offers a fully managed vector database that the company says makes it easy to add vector search to production applications. It combines state-of-the-art vector search libraries, advanced features such as filtering, and distributed infrastructure to provide high performance and reliability at any scale.
“No more hassles of benchmarking and tuning algorithms or building and maintaining infrastructure for vector search.”
Will you offer us a hand? Every gift, regardless of size, fuels our future.
Your critical contribution enables us to maintain our independence from shareholders or wealthy owners, allowing us to keep up reporting without bias. It means we can continue to make Jewish Business News available to everyone.
You can support us for as little as $1 via PayPal at office@jewishbusinessnews.com.
Thank you.
“Search technology has revolved around keywords for hundreds of years; books contained search indexes before the invention of the printing press. Amazingly, today’s predominant search infrastructure still works the same way,” said Edo Liberty, Founder and CEO of Pinecone. “Today’s users expect more. They want search results that anticipate and understand their needs, and not just match keywords.”
“We founded Pinecone to make it easy for engineers to build vector search applications,” he added. “That meant creating a completely new kind of infrastructure and indexing algorithm, standing it up as a managed service, and exposing it through a simple API. We needed to call it something, so we came up with ‘vector database.’”
Pinecone Systems explains that Vector databases are purpose-built to handle the unique structure of vector embeddings. They index vectors for easy search and retrieval by comparing values and finding those that are most similar to one another. A vector database indexes and stores vector embeddings for fast retrieval and similarity search, with capabilities like CRUD operations, metadata filtering, and horizontal scaling.
Vector databases, says Pinecone Systems, are a solution for powering ranking and recommendation engines. For online retailers, they can be used to suggest items similar to past purchases or a current item the customer is researching. Streaming media services can apply a user’s song ratings to create perfectly matched recommendations tailored to the individual rather than relying on collaborative filtering or popularity lists.
