BUILT FOR

Data Scientists

A New Foundation

For Data Science

NEXUS is a foundation model specifically designed for tabular data. Pre-trained on billions of tables, NEXUS applies prior knowledge of tabular systems and an understanding of the real world to empower data scientists with the ability to make more accurate predictions, faster.

Identify the Signals Others Miss

Hidden deep in large tabular datasets are complex interactions that manual feature engineering and traditional ML models often miss.

NEXUS identifies these latent correlations to boost accuracy across high-dimensional systems — critical for datasets with sparse observations, nonlinear dependencies, or subtle structures.

Stop Building Models From Scratch

Traditional ML workflows build a new model for every task. NEXUS starts with a pretrained tabular foundation and adapts it to each problem.

Traditional ML

Every new problem requires building and tuning a model from scratch.

Data

Feature Engineering

Model Selection

Training

Hyperparameter Tuning

Deploy

NEXUS

A pretrained tabular model that adapts to new datasets without rebuilding the entire pipeline.

Pre-Trained NEXUS

Tune

Deploy

Better

Performance

With Less Manual Effort

Achieving strong performance in tabular prediction often requires extensive feature engineering and fine tuning.

NEXUS reduces that burden by automating structural learning.

WHAT NEXUS UNLOCKS

01

Stop Rebuilding From Scratch

Begin with a pre-trained tabular foundation rather than training each model individually.

02

Support More Prediction Tasks

Model a wider range of use cases with fewer architectural constraints.

03

Achieve Higher Accuracy

Deliver stronger performance and more reliable results across complex, real-world datasets.

True Pattern Recognition

Most models just observe data points; NEXUS sees the underlying system. While conventional methods struggle with non-linear dependencies and complex seasonality, NEXUS captures the relationships that traditional models miss.

HOW IT COMPARES

Linear Regression

Blunt Trend Estimation

Fails to capture cyclicality entirely, ignoring the inherent structure of the data.

Traditional ML

Step-wise Approximations

While capturing the trend, the "jagged" fit misses the continuous, fluid nature of true patterns.

Linear Regression

Blunt Trend Estimation

Fails to capture cyclicality entirely, ignoring the inherent structure of the data.

Traditional ML

Step-wise Approximations

While capturing the trend, the "jagged" fit misses the continuous, fluid nature of true patterns.

NEXUS

Latent Pattern Recognition

Captures the underlying shape and structure of the data, learning complex feature interactions directly rather than relying on manually engineered features.

NEXUS

Latent Pattern Recognition

Captures the underlying shape and structure of the data, learning complex feature interactions directly rather than relying on manually engineered features.

NEXUS

Latent Pattern Recognition

Captures the underlying shape and structure of the data, learning complex feature interactions directly rather than relying on manually engineered features.

Help Build the Future of Tabular Intelligence

We’re building a new foundation for data science. Join a team working at the frontier of Large Tabular Models, large-scale datasets, and real-world predictive systems.

Explore Open Roles

Fundamental Technologies Inc.

Fundamental Technologies Inc.

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