BUILT FOR
Data Scientists
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.
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
Identify the Signals Others Miss
'Hidden deep in large tabular datasets within tabular data are complex interactions that manual feature engineering and traditional ML models often miss.
'Hidden deep in large tabular datasets within tabular data 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.
NEXUS identifies these latent correlations to boost accuracy across high-dimensional systems — critical for datasets with sparse observations, nonlinear dependencies, or subtle structures.
Better
Better
Performance
Performance
With Less Manual Effort
With Less Manual Effort
Achieving strong performance in tabular prediction often requires extensive feature engineering and fine tuning.
Achieving strong performance in tabular prediction often requires extensive feature engineering and fine tuning.
NEXUS reduces that burden by automating structural learning.
NEXUS reduces that burden by automating structural learning.
WHAT NEXUS UNLOCKS
WHAT NEXUS UNLOCKS
01
Stop Rebuilding From Scratch
Stop Rebuilding From Scratch
Begin with a pre-trained tabular foundation rather than training each model individually.
Begin with a pre-trained tabular foundation rather than training each model individually.
02
Support More Prediction Tasks
Support More Prediction Tasks
Model a wider range of use cases with fewer architectural constraints.
Model a wider range of use cases with fewer architectural constraints.
03
Achieve Higher Accuracy
Achieve Higher Accuracy
Deliver stronger performance and more reliable results across complex, real-world datasets.
Deliver stronger performance and more reliable results across complex, real-world datasets.
True Pattern Recognition
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.
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
Linear Regression
Blunt Trend Estimation
Blunt Trend Estimation
Fails to capture cyclicality entirely, ignoring the inherent structure of the time series data.
Fails to capture cyclicality entirely, ignoring the inherent structure of the time series data.
Traditional ML
Traditional ML
Step-wise Approximations
Step-wise Approximations
While capturing the trend, the "jagged" fit misses the continuous, fluid nature of true patterns.
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 time series 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
Recognizes the true underlying shape of the data. It doesn't just "fit" the points; it understands the cyclical system, leading to superior predictive accuracy.
NEXUS
Latent Pattern Recognition
Recognizes the true underlying shape of the data. It doesn't just "fit" the points; it understands the cyclical system, leading to superior predictive accuracy.
NEXUS
Latent Pattern Recognition
Recognizes the true underlying shape of the data. It doesn't just "fit" the points; it understands the cyclical system, leading to superior predictive accuracy.
More Precise
Decision Boundaries
More Precise
Decision Boundaries
How a model separates classes defines its real-world reliability. NEXUS learns the true underlying geometry of your data, identifying the structure that standard models miss.
How a model separates classes defines its real-world reliability. NEXUS learns the true underlying geometry of your data, identifying the structure that standard models miss.
Classic ML
Classic ML
Create rigid, blocky boundaries that miss significant data clusters.
Create rigid, blocky boundaries that miss significant data clusters.
XGboost
XGboost
Neural Nets
Neural Nets
Smoother approximations, but prone to missing irregular "edge".
Smoother approximations, but prone to missing irregular "edge".
Neural Nets
Neural Nets
Classic ML
Create rigid, blocky boundaries that miss significant data clusters.
XGboost
Neural Nets
Smoother approximations, but prone to missing irregular "edge".
Neural Nets
NEXUS
Captures true manifold, separating classes with high fidelity and finds all of the nuanced "islands" traditional models ignore.
NEXUS
Captures true manifold, separating classes with high fidelity and finds all of the nuanced "islands" traditional models ignore.
NEXUS
Captures true manifold, separating classes with high fidelity and finds all of the nuanced "islands" traditional models ignore.
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Fundamental Technologies Inc.