On Thursday, an AI laboratory named Fundamental stepped out of stealth mode, introducing a novel foundation model designed to tackle a longstanding challenge: extracting meaningful insights from the vast amounts of structured data generated by businesses. By merging traditional predictive AI systems with more modern approaches, the firm aims to transform how major corporations analyze their information. "With our model Nexus, we have built the best foundation model to handle that type of data."

This concept has already captured considerable attention from investors. The company is launching with $255 million in funding and a valuation of $1.2 billion. Most of this capital stems from a recent $225 million Series A round, which was led by Oak HC/FT, Valor Equity Partners, Battery Ventures, and Salesforce Ventures. Hetz Ventures also joined the Series A, alongside angel investments from Perplexity CEO Aravind Srinivas, Brex co-founder Henrique Dubugras, and Datadog CEO Olivier Pomel.

Rather than being a large language model (LLM), Fundamental's Nexus is described as a large tabular model (LTM), marking several significant departures from current AI methodologies. The model is deterministic, meaning it consistently provides the same answer to any given question, and it does not depend on the transformer architecture that underpins most modern AI systems. Fundamental classifies it as a foundation model because it undergoes standard pre-training and fine-tuning processes, yet the outcome is fundamentally distinct from what clients would experience with partners like OpenAI or Anthropic.

These distinctions are crucial, as Fundamental is targeting applications where contemporary AI models frequently struggle. Transformer-based models can only process data within their limited context window, often hindering their ability to reason across massive datasets—such as analyzing a spreadsheet with billions of rows. However, such extensive structured datasets are commonplace in large enterprises, presenting a substantial opportunity for models capable of managing that scale.

From Fundamental's perspective, this represents a major opening. Through Nexus, the company can apply modern techniques to big data analysis, delivering solutions that are more powerful and adaptable than existing algorithms. "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."

This potential has already attracted several prominent contracts, including multi-million-dollar agreements with Fortune 100 clients. Additionally, the company has formed a strategic partnership with AWS, enabling AWS users to deploy Nexus directly from their existing instances.