Fundamental raises 255M to build deterministic models for enterprise structured data

Fundamental has emerged from stealth with a 255 million Series A round and a 1.2 billion valuation to commercialize Nexus, a deterministic foundation model built specifically for large-scale structured enterprise data.

Fundamental, a new Artificial Intelligence lab focused on structured enterprise data, has emerged from stealth with a foundation model called Nexus designed to draw insights from the huge quantities of structured data produced by large organizations. The company positions Nexus as a way to bridge older predictive Artificial Intelligence systems with contemporary techniques, aiming to modernize how enterprises analyze massive datasets such as tables and spreadsheets. CEO Jeremy Fraenkel argues that while large language models have been effective with unstructured content like text, audio, video, and code, they perform poorly on structured formats, leaving a major gap in current Artificial Intelligence tooling.

The startup is launching with significant backing from venture investors and strategic partners. The company is emerging from stealth with 255 million in funding at a 1.2 billion valuation, with the bulk of it coming from a recent 225 million Series A round led by Oak HC/FT, Valor Equity Partners, Battery Ventures, and Salesforce Ventures, alongside participation from Hetz Ventures and angel investors including Perplexity CEO Aravind Srinivas, Brex co-founder Henrique Dubugras, and Datadog CEO Olivier Pomel. Fundamental describes Nexus as a large tabular model rather than a large language model, emphasizing that it is deterministic and does not rely on the transformer architecture that defines most contemporary Artificial Intelligence systems, even though it still follows a foundation model lifecycle of pre-training and fine-tuning.

By moving away from transformer-based designs, Fundamental is targeting scenarios where context window limits make it difficult for existing models to reason over extremely large datasets, such as a spreadsheet with billions of rows that are common in large enterprises. Fraenkel contends that using Nexus, enterprises can centralize many predictive and analytical workloads into a single model that can support a wide range of use cases and deliver better performance than legacy algorithms that often require an army of data scientists. That promise has already translated into commercial traction, including seven-figure contracts with Fortune 100 customers and a strategic partnership with AWS that will let customers deploy Nexus directly from existing AWS instances, positioning Fundamental as an infrastructure-level player for enterprise structured data analysis.

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