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”MongoDB's partnership with Superlinked simplifies creating vector embeddings for enterprise retrieval, analytics, and semantic search.”
Greg Maxson
Global Lead, AI GTM @ MongoDB
”Redis is partnering with Superlinked to make vector search easier to apply to complex data for use cases like RAG with LLMs and e-commerce recommendation systems.”
Ash Vijayakanthan
SVP of Cloud Sales and Partnerships @ Redis
”Starburst is partnering with Superlinked to extract and transform complex data into vector embeddings, enabling enterprises to take full advantage of their first party data to differentiate their Generative AI offerings.”
Harrison Johnson
VP of Technology Partnerships @ Starburst
"Superlinked and Dataiku are teaming up to enhance vector search with Superlinked's embedding tech in Dataiku's LLM Mesh, enabling code-free RAG pattern implementation for safe, scalable GenAI use."
Jed Dougherty
VP of Platform Strategy @ Dataiku

THE VECTOR
COMPUTER

Superlinked is a compute framework for your information retrieval and feature engineering systems, focused on turning complex data into vector embeddings.

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Build fast and smart GenAI-powered apps with Redis & Superlinked

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The Vector Stack for Complex Data

Put vectors into production with a reliable and efficient vector compute solution.

Multi-modal Vectors
Combine text, images and structured metadata into multi-modal vectors that fully describe your entities in their complex context.
Multi-objective Queries
Smoothly navigate the trade-off between multiple competing objectives like relevance, freshness and popularity.
Infrastructure as Code
Manage the compute layer that sits between your data infrastructure and your vector database through a simple python SDK.

Multiple Use-Cases, One System

Solve your information retrieval and feature engineering challenges with vectors.

Combine semantic relevance and document freshness to reliably retrieve the optimal document chunks in your Retrieval Augmented Generation system.
Combine semantic relevance and document freshness in your search system, because more recent results tend to be more accurate.
Build a real-time personalized e-commerce product feed with user vectors constructed from SKU embeddings the user interacted with.
Discover behavioral clusters of your customers using a vector index in your data warehouse.

Implement Once,
Run Everywhere

From the initial exploration to production using the same Python SDK.

Your Vector
Compute Team

We have over 160 years of experience building machine learning infrastructure, planet-scale software and companies.

Open Roles
Marci Mayer
Senior Cloud Infrastructure Engineer
Ben Gutkovich
Co-founder & COO
Laura Lehoczki
Senior Data Engineer
Peter Szabo
Senior Data Infrastructure Engineer
Alexandra Gyetvai
Business Operations Manager
Arunesh Singh
Design & Product Insights
Daniel Svonava
Co-founder & CEO
Mór Kapronczay
Senior Machine Learning Engineer
Gergő Gulyás
Senior Software Engineer
Krisztián Gajdár
Senior Software Engineer
Balázs Kemenes
Head of Engineering
György Móra
Chief Architect
Gergely Horvath
Head of Infrastructure
Andrea Parker
Senior ML Engineer
Eli Shtraikher
Head of Professional Services
Teddy Grosz
Senior Software Engineer
Neil O'Neill
Business Operations Manager
Matt Seabourne
Founder Associate
Andrey Pikunov
Machine Learning Engineer
Harshil Patel
Sales Manager

Investors

Let’s launch vectors into production

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