---
title: "The Vector Computer Company"
description: "Discover how vector computers are revolutionizing AI applications by combining text, structured data, and LLMs. Key insights for founders building ML-powered startups."
categories: ["data"]
keywords: ["vector computers","LLM optimization","Retrieval Augmented Generation","machine learning infrastructure","Superlinked","data pipelines","AI applications","structured data","MongoDB","Redis"]
ai_summary: "Explore how vector computers enhance AI by optimizing LLMs through structured data integration and efficient data pipelines."
date: 2024-03-18
lastmod: 2026-07-17
canonical_url: https://www.tomtunguz.com/superlinked/
author: "Tomasz Tunguz"
---

If you were to watch three videos on YouTube Shorts - one on Italian cooking, one on chess openings, & a third on crypto trading, YouTube Shorts’ recommendation algorithm combines the video descriptions with your dwell time.

Watching the osso bucco video to its end would trigger more Italian cooking specialty videos in your feed. 

We believe every LLM-based application will need this capability. 

Combining text & structured data in an LLM workflow the right way is difficult.  It requires a new software infrastructure layer: a vector computer. 

![image](https://res.cloudinary.com/dzawgnnlr/image/upload/ucytzbfba0cx5p2qwqip.png)

Vector computers simplify many kinds of data into vectors - the language of AI systems - and push them into your vector database.

As Spark has become the system for transforming large volumes of data in BI & AI training, the vector computer manages the data pipelines to feed models, optimizing them for a purpose or user.

Today, most vectors are very simple, but increasingly, vectors will have all kinds of data embedded in them, & vector computers will be the engines that unleash those powerful combinations.

Superlinked is building a vector computer. Founder  [Daniel Svonava](https://www.linkedin.com/in/svonava/) is a former engineer at YouTube who worked on real-time machine learning systems for a decade. 

Vector computers improve LLM accuracy by helping to surface the right data for Retrieval Augmented Generation (RAG). They allow faster optimization of LLMs by including many kinds of data that can be updated quickly. 

Other techniques for LLM optimization require retraining or fine-tuning. These work, but take time. Standard LLM stacks of the (not-so-distant) future will leverage both RAG & fine-tuning.

Superlinked is now in product preview, working with several major infrastructure partners like MongoDB, Redis, Dataiku, & others. If you’d like to learn more, [click here](https://superlinked.typeform.com/to/LXMRzHWk?typeform-source=tomtunguz.com). 

We’re thrilled to be partnering with Daniel & Ben.