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It’s called the Artificial Intelligence Laboratory essential It emerged from stealth on Thursday, offering a fundamental new model for solving an old problem: how to extract insights from the huge amounts of structured data produced by companies. By combining legacy systems of predictive AI with more modern tools, the company believes it can reshape how large companies 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 Frankel told TechCrunch. “Using our Nexus model, we have built the best underlying model to handle this type of data.”
The idea has already attracted significant interest from investors. The company emerges from stealth with $255 million in funding at a $1.2 billion valuation. The bulk of it comes from a 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.
It is called the Large Tabular Model (LTM) rather than the Large Language Model (LLM), and it breaks from contemporary AI practices in a number of important ways. The model is deterministic – that is, it will give the same answer every time it is asked a certain question – and does not depend on Transformer architecture Which defines the models of most contemporary AI laboratories. Fundamental calls it a basic model because it goes through the normal steps of pre-training and fine-tuning, but the result is very different from what a customer would get when partnering with OpenAI or Anthropic.
These differences are important because Fundamental seeks a use case where contemporary AI models often stumble. Because transformer-based AI models can only process data within their context window, they often have trouble reasoning about very large data sets — for example, analyzing a spreadsheet with billions of rows. But this type of large structured data sets is common within large organizations, creating a huge opportunity for models capable of handling this volume.
As Frankel sees it, this is a huge opportunity for Fundamental. With Nexus, the company can bring contemporary techniques to big data analysis, providing 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 dramatically expand the number of use cases you address,” he told TechCrunch. “And in each of these use cases, you get better performance than you would have been able to achieve with an army of data scientists.”
This promise has already brought in 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.