OmniSci enables data scientists to render, cross-filter and explore massive datasets in a fraction of the time of mainstream data science visualization tools.

OmniSci is the only GPU-accelerated data science platform that allows faster feature engineering pipelines for machine learning model creation and the ability to "unmask the black box" by visualizing what your black box models see in the data.

Accelerate the Feature Engineering Process

Data scientists must pick data features to train their algorithms. Because OmniSci makes it easier and faster to explore big tables, data scientists train models more quickly, with better outcomes. 

Shed Light on Black-box Models

Explaining why AI models make the predictions they make is a notorious challenge. Now, with OmniSci, data science and data visualization are integrated and explaining models to decision-makers, engenders greater trust and adoption of AI.


Visualize Predictions and Outcomes Together

ML models make predictions, but actual outcomes will vary and models can become less predictive over time. Data science using OmniSci visually shows the difference between expected and actual, so you know when to retrain the model.

Deliver Financial Models More Quickly

The Challenge

Artificial intelligence can outperform traditional techniques for predicting market movements, but greater AI adoption has a cost. Without interactive, visual ways to do feature engineering, data scientists spend too much time manually exploring hundreds of market variables, searching for the most predictive features to train their models and delaying the date when that model goes into production.

OmniSci Solution

The OmniSci platform gives data scientists at hedge funds and Investment Management firms a far faster method to generate their AI models resulting in smarter investment analysis. When visual exploration of billion-row data sets becomes 100x faster than before, asset managers and brokers can capture more opportunities, avoid more risks, and create a compounding competitive advantage through predictive analytics in Investment Management.

McKinsey & Company

Analytics in banking: Time to realize the value

Tech Emergence

Machine Learning in Finance – Present and Future Applications

MIT News

Auto-tuning data science: New research streamlines machine learning

Predict Machine Failure with IoT Data

The Challenge

Artificial intelligence is increasingly used to predict machine failure, to minimize downtime and maintenance costs for automobiles, ships or aircraft. Yet engineers have trouble explaining an AI model’s black-box recommendations, so leaders fall back on human experience and "gut" guesses in favor of predictive analytics in logistics industry to keep field equipment running as intended.

OmniSci Solution

OmniSci visual analytics makes AI models more accessible to the scientists who create them and to the broader audiences of logistics leaders, military commanders or regulators who must understand those models. With immediate visual exploration of the same underlying elements that trained AI models, anyone can trust their predictions.

GTC Presentation

Volkswagen Uses OmniSci to Visualize and Interrogate Black Box AI Models


Blending Man And Machine To Get The Most From AI

IoT Agenda

Five ways IoT is transforming the manufacturing industry

Predict Hospital Staffing Levels

The Challenge

Hospitals around the world look to optimize their operational efficiency by predicting optimal staffing levels without putting lives are at risk during periods of peak demand. Healthcare administrators lack a common visualization platform to track results and extend their machine learning models for precise staffing predictions.

OmniSci Solution

The OmniSci platform lets hospital staffing analysts visualize a model’s predictions and compare them to actual staffing outcomes. Every click on a chart generates SQL queries that complete in milliseconds, then all visual elements instantly refresh, giving you the best clinical pathway for your patients. Likewise, the number one goal of a hospital is to keep readmission rates low. Imagine using all of this data to create ML models that guide staffing decisions and predict readmission and recommend preventative treatment.

New England Journal of Medicine

Nurse-Staffing Levels and the Quality of Care in Hospitals

Harvard Business Review

Why Hospitals Need Better Data Science