Call an AI lab Fundamental emerged from stealth on Thursday, offering a new foundational model for solving an old problem: how to extract insights from the vast amounts of structured data generated by businesses. By combining old predictive AI systems with more modern tools, the company believes it can reshape the way large enterprises analyze their data.
“While LLMs have been great at working with unstructured data like text, audio, video and code, they don’t work well with structured data like tables,” CEO Jeremy Fraenkel told TechCrunch. “With our Nexus model, we’ve created the best base model to handle this kind of data.”
The idea has already attracted considerable interest from investors. The company is emerging from stealth with $255 million in funding at a $1.2 billion valuation. Most of it comes from the recent $225 million Series A round led by Oak HC/FT, Valor Equity Partners, Battery Ventures and Salesforce Ventures. Hetz Ventures also participated in Series A, with angel funding from Perplexity CEO Aravind Srinivas, Brex co-founder Henrique Dubugras, and Datadog CEO Olivier Pomel.
Fundamental’s Nexus, called the large table model (LTM) rather than the large language model (LLM), departs from current AI practices in several important ways. The model is deterministic—that is, it will give the same answer every time it is asked a given question—and does not rely on the architecture of the transformer which defines models from most modern AI labs. Fundamental calls it a base model because it goes through the normal steps of pre-training and refinement, but the result is something very different from what a customer would get when working with OpenAI or Anthropic.
These differences are important because Fundamental is chasing a use case where modern AI models often falter. Because transformer-based AI models can only process data that is within their context window, they often struggle to reason on extremely large datasets — for example, analyzing a spreadsheet with billions of rows. But this kind of massive structured data set is common in large enterprises, creating a significant opportunity for models that can handle scale.
As Fraenkel sees it, this is a huge opportunity for Fundamental. Using Nexus, the company can bring modern techniques to big data analysis, offering something more powerful and flexible than the algorithms currently in use.
“You can now have one model across all of your use cases, so you can now massively expand the number of use cases that you’re dealing with,” he told TechCrunch. “And in each of those use cases, you get better performance than you otherwise could with an army of data scientists.”
This promise has already generated a number of high-profile contracts, including seven-figure contracts with Fortune 100 customers. The company has also entered into a strategic partnership with AWS that will allow AWS users to deploy Nexus directly from existing instances.
