Monetize Artificial Intelligence and Machine Learning

Monetize Artificial Intelligence and Machine Learning

Get a 10X return on your investment AI and ML with the only data platform that enables you to monetize user events in real time, at scale.

Dramatically Boost Your ROI

How do you plan to get back the money you’ve invested in artificial intelligence and machine learning? Volt is the only data platform that enables you to monetize individual user events in real time, at scale, allowing you to fully capitalize on your AI and ML investments.

Business Overview

Making Real-World AI Models Functional

The point of AI/ML is to enable smarter, more lucrative decisions. But AI/ML is much harder to monetize than it looks. Models often work fine with static, lab-based data sets but are hard to use in production with real-world constraints and latency expectations. For real-world use cases, you need real data, in real time, for real decisions, and every decision you make influences the next one.

This means that tracking changes to the state of data at the event level in real time is critical. For example, the smartest chess program in the world would be useless if it didn’t have a constantly updated snapshot of the board that includes the latest moves.

The same principle applies to your AI/ML model. If it isn’t aware of its own decisions, it will act nonsensically. Applying AI/ML to incoming events while tracking state isn’t easy and often forces companies to build complex, expensive, latency-adding complex stacks with many layers of processing and storage.

Learn More – Getting Machine Learning into Production

A Fast Data Platform that Still Tracks State

Volt makes AI/ML easier to monetize by never losing track of state and running any deterministic, JVM-compatible AI/ML model. It can then store the result of the model, ensuring that the next invocation starts with an updated data set. It does all of  this in a single round trip to the server, which means the overhead is limited to network time plus 1-2 milliseconds, plus however long it takes the model to run.

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