Company making it possible for engineering teams to take advantage of vector search

Pinecone Systems Inc., a search infrastructure company, announced the completion of a $28M Series A funding round to enable companies of all sizes to power search applications with AI. The funding round was led by Menlo Ventures, with participation from new investor Tiger Global and previous investors including Wing Venture Capital, who led the company’s seed-stage financing. Since coming out of stealth last year Pinecone has emerged as the leader of a new generation of search technology with thousands of developers signing up for their vector-database product.

“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.”

The content stored in consumer and enterprise applications is only valuable if users can find it. Traditional keyword-based search systems struggle with complex data such as unstructured text, user profiles, or images. Yet complex data is only growing, as are customers’ expectations. That puts pressure on engineering teams to modernize their search systems.

Marketing Technology News: LoginRadius launches M2M Authorization for a Seamless Business Data Access Across Multiple Systems

Recent advances in AI have led to an entirely new way of searching through data. Vector Search is focused on storing and searching through AI-generated representations of content. These AI-generated representations encapsulate the meaning of the original content in a machine-readable format, and enable developers to build better search applications. Legacy systems were not designed to work like this, so vector search requires a new kind of infrastructure called a vector database. With its vector database product, Pinecone provides the search infrastructure for engineering teams to easily implement AI-powered search into their applications without the need to build their own or modify legacy infrastructure.

“Search is in desperate need of modernization,” continued Liberty. “Machine learning has changed the way we interact with our data, and those who don’t react quickly enough will be left behind. The importance of vectors in search applications moving forward cannot be overstated, and Pinecone has removed the huge infrastructural barrier that has prevented many companies from benefiting until now.”

As part of the announcement, Tim Tully, Partner at Menlo Ventures and former CTO of Splunk, will join the board of directors. The addition of Tim’s experience builds on the incredible team Pinecone has assembled over the last year, including Peter Wagner (Founding Partner at Wing Venture Capital), Bob Wiederhold (former CEO of CouchBase), and Ram Sriharsha, former Splunk VP of Engineering, who joined the Pinecone team as VP of Engineering.

Marketing Technology News: MarTech Interview with Wendell Lansford, Co-founder at Wyng

Pinecone’s vector database already has thousands of users, with customers across a wide spectrum of industries including enterprise software, social media platforms, search engines, ecommerce, and AI/ML products. It is being used by companies ranging from small startups to enterprises, handling data ranging anywhere from hundreds of thousands to billions of items. The company has offices in New York and Tel Aviv, as well as some working remotely. Pinecone will utilize the funding to grow out its product, customer success, and R&D teams, and will invest in core research on machine learning (ML), information retrieval (IR), and natural language processing (NLP).

“Many of the largest companies in the world have already embraced the use of vector search, which has given them a distinct advantage over their competitors,” said Tim Tully, Partner at Menlo Ventures. “Pinecone is ensuring that regardless of size and budget, companies can integrate next-generation search capabilities into their applications and get to production without the need to develop and maintain an entirely new architecture.”