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Superlinked Raises $9.5 Million Seed From Index Ventures and Theory Ventures to Fill the Gap Between Data and Vector Databases

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SAN FRANCISCO, March 18, 2024-- Superlinked, the solution for turning complex data into vector embeddings, today announced it has received $9.5 million in seed funding. The round was led by Index Ventures, with significant contribution from Theory Ventures. 20Sales, Firestreak and several prominent tech executives also participated. News of the funding comes as Superlinked launches a private alpha of its product and closes enterprise customer contracts to make data accessible for ML systems in information retrieval and feature engineering. The fresh capital will be used to further scale to meet market demand and expand product capabilities, building on Superlinked's technology that makes structured and unstructured data ML-compatible, and therefore useful, for creating new solutions and deriving additional value from data on top of vectors.

The fundamental issue in ML-powered information retrieval today is that its objectives and the underlying enterprise data are far too complex to be vectorized by pre-trained LLMs. Addressing this challenge with a future-proof retrieval stack entails focusing on two primary components: compute (turning data into vectors) and search (indexing and managing vectors). While considerable attention has been given to the search aspect, with over $250 million already invested in vector databases over the last two years, companies have grappled with overcoming the compute challenge. 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 that companies can build actual smart software faster and easier than ever before.

"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," said Bryan Offutt, Partner at Index Ventures. "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."

Superlinked: The ML Compute Engine of the Future

Superlinked was founded by Daniel Svonava, an ML engineer previously with Google, where he built core ML infrastructure for YouTube Ads, focusing on the prediction of user behavior, and Ben Gutkovich a former software engineer who supported Fortune 500 corporations with digital transformation as a strategy consultant at McKinsey's London office. The pair 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 actually works in hours or days versus the typical months or quarters by connecting the company's data infrastructure to a vector database. They have since assembled a team with a combined 160 years of ML, software, and company building experience to accomplish this goal.

"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," said Svonava, Superlinked CEO and co-founder. "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."

As enterprises start experimenting with GenAI models and upgrade their search systems to use vectors, they face the difficult choice between giving up control of the results and giving up on the promise of the technology of the future. With Superlinked, they can now have both.

Currently, other vendors either over-focus on textual data by only supporting pre-trained LLMs, model fine-tuning, and efficient inference OR they enable generalized model building within the PyTorch and Tensorflow ecosystems - which requires deep ML expertise. Superlinked aims to bridge both worlds on top of a solid data engineering foundation - to democratize the building of vector-powered systems.

Superlinked has already partnered with several leading tech companies, including MongoDB, Redis, Dataiku, Starburst and others on integrations to expand its reach and capabilities.

"AI has fundamentally changed the way businesses are interacting with software and data, in both operations and for end-user experiences," said Greg Maxson, Global Lead, AI GTM at MongoDB. "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."

To learn more about vectors and how they function, please visit Superlinked's VectorHub resource. To check out Superlinked's platform, how it works, and what use cases it can unlock for enterprise clients, go to

About Superlinked

Superlinked is a compute and data engineering framework for turning data into vector embeddings - the missing piece of the vector search puzzle, enabling companies to build Analytics, RAG, Search and Recommendation solutions. The company was founded by technology veterans Daniel Svonava and Ben Gutkovich and is backed by Index Ventures and Theory Ventures. It is headquartered in San Francisco with offices in London and Budapest. Find out more at

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Amber Moore, Moore Communications, 1 503-943-9381,

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