Vector searches are all about increasing the results of search applications through artificial intelligence (AI). That is where Pinecone comes in. Pinecone makes it easy for developers to build high-quality vector search applications. Now, with $28 million in Series A funding, Pinecone seeks to take search applications to the next level. Here is what the company’s Founder and CEO, Edo Liberty, had to say.
As demand grows for better search results and recommendations, the path to better search applications is through AI, specifically vector search. Last year, we launched Pinecone to make it easy for developers to build high-performance vector search applications — at any scale and without infrastructure hassles.
Today I’m excited to announce we raised $28M in Series A funding. This investment, along with our rapidly growing number of users and customers, is an undeniable testament to what we believed from day one: The future of search is vector search. And the future of vector search is Pinecone.
I’d like to share how we got here and where we’re headed. It all started in the year 1200…
Search from 1200 AD to Today
In the 13th century, close to 200 years before the invention of the printing press, the cardinal Hugh of Saint-Cher created the first concordance of the Latin Bible by listing important keywords along with the page or passage numbers where they appear.
It would seem that search technology has changed a lot since then — we have modern indexing and ranking methods, with databases that can store and search through billions of records in milliseconds. And yet the core idea hasn’t changed: Eight centuries after the Concordantiae Sancti Jacobi was penned, search technology still revolves around keywords.
In recent years, advancements in AI/ML have made it possible to capture the meaning of any data in a machine-readable format called vector embeddings. That opened the door to vector search, a revolutionary information-retrieval method that searches through data using meaning and not only keywords.
The biggest tech companies have already adopted this technology. When you search on Google, get recommended products on Amazon, and read relevant stories on your Facebook feed, you see vector search in action.
It’s no coincidence this revolution started at the largest, most advanced tech companies: Leveraging vector search inside large-scale and high-performance applications requires a new kind of infrastructure to be built and maintained, along with extensive engineering and data science work. In other words: It’s hard.
We founded Pinecone to make it easy for engineers to build vector search applications. 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.”
The original article comes from Pinecone.