Large Tabular Models (LTM) and Confidential Computing: A New Architecture for Enterprise AI
Fundamental invites you to an evening exploring the emerging field of Large Tabular Modules (LTM) and the challenges of bringing AI securely to enterprise data.
Fresh out of stealth with $275 million raised on a $1.45B valuation, Fundamental is building the next generation of enterprise AI infrastructure and launching its Israeli engineering hub in Tel Aviv. Join founders, engineers, and AI leaders for a discussion on confidential computing, enterprise-scale AI systems, the emergence of a major new AI category: Large Tabular Models.
Beyond Textual AI: Inside the Rise of Large Tabular Models
While Large Language Models have redefined how we interact with unstructured data, the vast majority of enterprise data is structured — in millions of rows and columns of tabular data. Trillions of dollars of value remains locked in these datasets. Traditional LLMs struggle with complex schemas, quantitative relationships, and high-cardinality enterprise datasets. Large Tabular Models (LTMs) are emerging as a new, specialized class of model designed to bridge this gap.
Featured keynote: Bringing the Model to the Data: Confidential Computing for Enterprise AI
Enterprise AI has a trust problem. Companies want to use AI on sensitive internal data — customer records, financial systems, operational data, proprietary code — but they can’t afford to expose that data to external AI providers. At the same time, AI vendors don’t want to hand over their models or IP.
This talk explores how confidential computing changes that equation.
We’ll break down the emerging architecture behind “bringing the model to the data”: deploying AI directly inside customer-controlled environments using hardware-backed Trusted Execution Environments (TEEs), cryptographic attestation, and confidential inference. Instead of moving sensitive enterprise data into public AI systems, organizations can now run advanced models securely inside their own infrastructure — with guarantees for both data privacy and model protection.
The session will cover:
Why enterprise AI adoption stalls around trust, compliance, and security
What confidential computing actually is (without the buzzwords)
How TEEs and attestation work in practice
The tradeoffs and limitations of confidential AI architectures
Real-world implications for regulated industries, AI agents, and enterprise deployments
Why “data in use” may become the next major frontier in AI security
Ideal for engineers, AI builders, security leaders, and anyone interested in LTMs and the future infrastructure layer of enterprise AI.
Who should attend: AI Engineers, Applied ML Engineers, Data Engineers, Full-Stack Engineers, DevOps Developers, and technical builders working on solving complex challenges with real-world AI systems.
Fundamental is pioneering the future of enterprise decision-making. Co-founded by DeepMind alumni, Fundamental has developed NEXUS - its most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. While other AI companies focus on text and images, Fundamental addresses the tabular data that actually drives enterprise decisions. Backed by world class investors and trusted by Fortune 500 companies, Fundamental unlocks trillions of dollars of value by giving businesses the power to predict.