Superlinked, a solution for turning complex data into vector embeddings, announced it has received $9.5 million in seed funding. Index Ventures led the round, with Theory Ventures significantly participating. 20Sales, Firestreak, and several prominent tech executives also participated.
The funding news follows Superlinked’s launch of a private alpha of its product and closing enterprise customer contracts to make data accessible for machine learning systems in information retrieval and feature engineering.
The new funding round will be used to scale and meet market demand while expanding its product. The product expansion will build on Superlinked’s technology that makes structured and unstructured data ML-compatible, making it more helpful in creating new solutions and deriving more value from data on top of vectors.
The main issue in ML-powered information retrieval is that its objectives and the enterprise data are too complex to be vectorized by pre-trained LLMs. To address this challenge with a future-proof retrieval stack entails focusing on two primary components – computing (turning data into vectors) and search (indexing and managing vectors).
Even though much attention has been given to search, companies have struggled to overcome the computing challenge. And this is where Superlinked plays a pivotal role. It offers a compute framework to turn all kinds of data into vector embeddings, optimizing retrieval control, quality, and efficiency in real time so companies can build smarter software faster and easier.
Daniel Svonava and Ben Gutkovich launched Superlinked. Svonava is an ML engineer previously with Google, where he built core machine learning infrastructure for YouTube Ads, focusing on predicting user behavior. And Gutkovich is a former software engineer who supported Fortune 500 corporations with digital transformation as a strategy consultant at McKinsey’s London office.
The founders set out to build Superlinked to provide data scientists and software engineers with the ability to ship a product or feature with an ML-powered Search, a Recommender system, an Analytics pipeline or RAG interface that works in hours or days compared to the typical months or quarters by connecting the company’s data infrastructure to a vector database. And they have assembled a team with 160 years of ML, software, and company-building experience to accomplish this goal.
Superlinked has already partnered with several leading tech companies, including MongoDB, Redis, Dataiku, Starburst, and others, on integrations to expand its reach and capabilities.
KEY QUOTES:
“We’re very bullish on the future of vector databases, but a significant gap remains in the market. Most companies don’t have the ML and infrastructure capabilities needed to vectorize their full data landscape, which limits the potential of any downstream ML application. What Daniel, Ben, and the Superlinked team created bridges this gap. This is tremendously difficult to do. They’ve built a novel way for companies to turn all the unstructured and structured data they have on their users, products, or media into vectors, transforming the way companies search, analyze, and understand their data.”
— Bryan Offutt, Partner at Index Ventures
“Vectors power most of what you already do online – hailing a cab, finding a funny video, getting a date, scrolling through a shopping feed or paying with a card. But even the best companies only use vectors for a handful of tasks – it’s just too difficult. We work in tandem with vector databases to put vectors at the center of enterprise data and compute infrastructure, democratizing the power that was once exclusive to a handful of tech giants.”
— Daniel Svonava, Superlinked CEO and co-founder
“AI has fundamentally changed the way businesses are interacting with software and data, in both operations and for end-user experiences. Vector databases and embeddings have simplified generative AI and semantic search capabilities into real-time applications, changing the way developers build applications. MongoDB’s partnership with Superlinked makes it easier for customers to create and synchronize vector embeddings for complex data required for enterprise retrieval augmented generation and other use cases, including analytics or more standard semantic search and recommendation systems.”
- Greg Maxson, Global Lead, AI GTM at MongoDB
