For companies with serious industrial applications who are dissatisfied with slow legacy systems that don’t deliver actionable data, Uptake is a new kind of industrial platform. Unlike legacy systems that are built on old technology like Hadoop which inhibit responsiveness and innovation, Uptake is built on a patented Intelligent Data Grid which is powerful and flexible enough to respond and deliver predictive results from massive amounts of data in real-time, and learn from the results.



Uptake’s Intelligent Data Grid completely changes the calculation for storing data, so that we can put ever-increasing amounts of data into it while making the incremental costs negligible. Our platform can scale out to a theoretically unlimited amount of data, the scale of data which the Industrial Internet demands and which slows down a traditional legacy systems.



Uptake embeds predictive intelligence directly into the data storage grid itself, allowing it to make prediction and find actionable insights in real-time. Through the use of advanced data science techniques, and a comprehensive roster of models, Uptake is able to produce the highest predictive accuracy with the lowest technical overhead across structured and unstructured data.

Smart Predictions

Uptake leverages the most advanced machine learning techniques simultaneously, enabling us to find the right prediction technique for any situation. Whether using decision tree logic or binary logistic regressions, we automatically find the most accurate model for whatever our customers are trying to predict.

Supervised Machine

Uptake's modeling techniques proactively evaluate the accuracy of their predictions, adjusting and learning based on their own past performance. As more data is ingested, the models themselves will approach a true representation of the real world, allowing for the highest predictive accuracy possible.



Underlying the Intelligent Data Grid is an army of lightweight intelligent agents that swarm new data as it is ingested into the grid to immediately analyze it, join it to other data to add context, and make predictions based on it. When an intelligent agent recognizes a situation that needs to be addressed--say, a part that is about to fail—it triggers a workflow customized to handle that situation.

Easy Monitoring

The technician in the field can be proactively alerted to the fact that a part is about to fail and can schedule the asset for service at a convenient time. The worker in the shop is provided with step-by-step instructions for the fix and the part can automatically be picked from inventory and sent to the shop so that the downtime is cut down as much as possible.

Improved Efficiency

Finally, the Intelligent Data Grid acts as a powerful feedback loop, using its own output to learn and become smarter. As maintenance is performed and event outcomes are fed back into the system, it uses that data to become more intelligent, refining its predictions and making continually smarter choices about how to deal with them.