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Fundamental

Category: AI in Analytics & BI

A pioneering AI platform centered on NEXUS, the industry’s first Large Tabular Model (LTM) designed to automate complex forecasting and predictive intelligence for structured enterprise data. Fundamental was founded in 2024. The company is led by Jeremy Fraenkel. Based in San Francisco, USA. Team size: 11-50. Total funding raised: $255.0M. Latest round: Series A. Key investors include Oak HC/FT, Valor Equity Partners, Battery Ventures, Salesforce Ventures, Hetz Ventures, MUIP (Mitsubishi UFJ Innovation Partners), Angels: Aravind Srinivas, Assaf Rappaport, Henrique Dubugras, Olivier Pomel.

Founded
2024
Headquarters
San Francisco, USA
Team size
11-50
Total funding
$255.0M

Value proposition

Unlocks the latent value in massive structured datasets by providing a 'third pillar' of AI—specialized for tables—that automates forecasting and decision-making with higher accuracy than general-purpose LLMs.

Products and solutions

NEXUS Large Tabular Model (Core Foundation Model), Enterprise Predictive Intelligence Suite, Automated Financial Forecasting Engine, Structured Data Analytics Pipeline

Unique value

Unlike LLMs (text) or computer vision (images), Fundamental focuses exclusively on 'Large Tabular Models' (LTM), treating structured spreadsheet and database data as a primary modality for generative and predictive AI.

Target customer

Fortune 100 enterprises, large financial institutions, and data-intensive organizations requiring high-precision predictive modeling.

Industries served

Financial Services, Retail & E-commerce, Supply Chain & Logistics, Healthcare, Enterprise Technology

Technology advantage

Proprietary architecture capable of processing billions of data points within tables to identify patterns invisible to traditional statistical methods; built by a core team from Google DeepMind, OpenAI, and Meta.

How they differentiate

Unlike traditional AutoML or general-purpose LLMs, Fundamental utilizes a proprietary 'Large Tabular Model' (LTM) architecture called NEXUS. This treats structured data as a primary generative modality, allowing for higher precision in forecasting and pattern recognition without the 'hallucinations' or tokenization limits of text-based models.

Main competitors

DataRobot, Abacus.ai, Ikigai Labs

Key partnerships

Oak HC/FT (Strategic Lead Investor), Amazon Web Services (strategic partnership; NEXUS on AWS/SageMaker), SAP (NEXUS in SAP Business AI / generative AI Hub, May 2026), Salesforce Ventures, Fortune 100 Early Adopter Design Partners, MUFG / MUIP (customer + equity, Jul 2026)

Notable customers

MUFG (Mitsubishi UFJ Financial Group) — NEXUS deployment across markets, trading, digital banking, Fortune 100 enterprises (seven-figure contracts at launch), Global Retail Leaders, Supply Chain & Logistics Enterprises

Major milestones

Emerged from stealth Feb 2026 with $255M total funding ($30M Seed + $225M Series A) at ~$1.2–1.4B valuation, Launched NEXUS Large Tabular Model (LTM) publicly Feb 2026, Strategic partnership with AWS; NEXUS available on Amazon SageMaker, SAP partnership (May 2026) to bring NEXUS into SAP Business AI platform, MUFG customer win + MUIP equity investment (Jul 2026), Opened Tel Aviv engineering hub (Jun 2026), hiring dozens in Israel by end-2026

Growth metrics

Raised $255M total ($30M Seed + $225M Series A) at approximately $1.2–1.4B post-money valuation within ~16 months of Oct 2024 founding; seven-figure Fortune 100 contracts and ARR reported at stealth exit.

Market positioning

High-end enterprise AI foundation model provider, positioning tabular data as the 'third pillar' of AI alongside text and vision.

Geographic focus

Global (HQ in San Francisco), with a primary focus on Fortune 100 enterprises in North America and Europe.

Patents and IP

Proprietary Large Tabular Model (LTM) architecture; specific public patent filings not disclosed as of February 2026.

About Jeremy Fraenkel

Jeremy Fraenkel is the Co-founder and CEO of Fundamental. He was previously the Co-founder and Chief Product Officer of Arkifi, a generative AI startup for financial workflows. He holds a Master of Engineering (M.Eng.) in Computer Science and Machine Learning from the University of California, Berkeley, and a Bachelor's degree in Economics from Johns Hopkins University. His professional background includes experience in investment banking and business development at firms such as AmeriVet Securities and Oppenheimer & Co.

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