An AI lab called Fundamental emerged from stealth on Thursday, offering a new foundation model to solve an old problem: how to draw insights from the huge quantities of structured data produced by enterprises. By combining the old systems of predictive AI with more contemporary tools, the company believes it can reshape how 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 model Nexus, we have built the best foundation model to handle that type of data.”
The idea has already drawn significant interest from investors. The company is emerging from stealth with $255 million in funding at a $1.2 billion valuation. The bulk 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 the Series A, with angel funding from Perplexity CEO Aravind Srinivas, Brex co-founder Henrique Dubugras, and Datadog CEO Olivier Pomel.
Called a large tabular model (LTM) rather than a large language model (LLM), Fundamental’s Nexus breaks from contemporary AI practices in a number of significant ways. The model is deterministic — that is, it will give the same answer every time it is asked a given question — and doesn’t rely on the transformer architecture that defines models from most contemporary AI labs. Fundamental calls it a foundation model because it goes through the normal steps of pre-training and fine-tuning, but the result is something profoundly different from what a client would get when partnering with OpenAI or Anthropic.
Those differences are important because Fundamental is chasing a use case where contemporary AI models often falter. Because Transformer-based AI models can only process data that’s within their context window, they often have trouble reasoning over extremely large datasets — analyzing a spreadsheet with billions of rows, for instance. But that kind of enormous structured dataset is common within large enterprises, creating a significant opportunity for models that can handle the scale.
As Fraenkel sees it, that’s a huge opportunity for Fundamental. Using Nexus, the company can bring contemporary techniques to big data analysis, offering something more powerful and flexible than the algorithms that are currently in use.
“You can now have one model across all of your use cases, so you can now expand massively the number of use cases that you tackle,” he told TechCrunch. “And on each one of those use cases, you get better performance than what you would otherwise be able to do with an army of data scientists.”
That promise has already brought in a number of high-profile contracts, including seven-figure contracts with Fortune 100 clients. The company has also entered into a strategic partnership with AWS that will allow AWS users to deploy Nexus directly from existing instances.
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