In San Francisco, an enterprise data management and analytics platform called Fundamental has opened its doors with funding of $255 million, and launched its first product, NEXUS. This uses a 'Large Tabular Model' to convert clients' enterprise data into predictive intelligence and forecasts.
The company was founded in October 2024, and raised its funds in a $30m Seed round and a $225m Series A, led by Oak HC/FT and with participation from VC firms including Valor Equity Partners, Battery Ventures, Salesforce Ventures and Hetz Ventures, plus a number of angel investors. The new capital will be used to scale compute, expand enterprise deployments, and rapidly grow the team across research, engineering, and go-to-market.
NEXUS, built by alumni from Google's DeepMind, distances itself from legacy predictive analytics with a purpose-built foundation model designed specifically for tabular data, tapping recent advances in deep learning which it says have focused on LLMs and related architectures, optimized for unstructured, sequential data such as text, images and video. This enables it to handle larger volumes of data much faster, but also gives it far more power to predict risks, opportunities and generally 'What will happen next'.
The product can be integrated directly into companies' existing data stacks with 'minimal effort, often a single line of code.' It then ingests raw tabular data and automatically learns the underlying structure, patterns, and dependencies without extensive feature engineering or manual training, according to the firm.
CEO & co-founder Jeremy Fraenkel states: 'We've built a generalized foundation model specifically to leverage the world's most valuable data: the billions of tables that underpin predictions in every enterprise, across every vertical. NEXUS is the OS for business decisions.'
Fundamental has also entered into a strategic partnership with Amazon Web Services (AWS) to accelerate enterprise adoption of its model to the latter's customers.
The company is online at www.fundamental.tech .
All articles 2006-23 written and edited by Mel Crowther and/or Nick Thomas, 2024- by Nick Thomas, unless otherwise stated.
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