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Superlinked: The Vector Computer

Turn your data into vector embeddings for your information retrieval stack.

🤖 What we do

Controlled with a typed & clean Python SDK, our customers get a compute engine that:

  1. Connects to their data sources.
  2. Loads, transforms and filters their data.
  3. Converts data into vector embeddings.
  4. Creates & updates the state in their Vector Database.
  5. Manages the vector retrieval with a proxy running on top of their Vector Database.
  6. Retrains / fine-tunes the embedding models using the on-the-task feedback.

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Our design principles:

  1. Infrastructure: Build for production, not trivial demos.
  2. Developer experience: Make simple things easy and complex things possible.
  3. Quality & efficiency: Combine vectors from multiple focused models, instead of one large one.
  4. Right tool for the job: Turn any signal into an embedding segment - text, image, click data, freshness, popularity, location, audio etc. Structured + unstructured data, streaming + batch.

🚀 Who we are

We are a small team of engineers funded by Tier-1 US VCs, working with large UK & US-based platforms as clients. We have strong relationships with Vector Databases and Data infrastructure companies who are excited to work with us as we connect them to each other.

We follow the Linear Method, talk to users daily and focus on solving their problems. We are remote-first, but our engineers work 2 days a week from the office.

🤩 You