Automatic Process Optimization

Analytics Are Great, But Require Repetitive Work & They Don't Capture The Whole Picture

When you look to improve any process, you typically start with a goal or an objective. Most analytics or business intelligence tools can easily tell you how you are progressing towards those goals.

But once you find out what is working and what isn't, a human has to take time to go back and update your processes based on the insights. This happens for each process and happens each time new information is gleaned.
Then, when you look at your analytics reports, most are binary. Some number of customers bought, another number clicked, some shared. But this captures only a small portion of what really happened. If you're optimizing based on something so simple, you're likely to be optimizing incorrectly.

Optimize Automatically, Based On A Spectrum Of Positive/Negative Outcomes

Instead of supervised machine learning models, Bellwethr utilizes a different form of machine learning known as reinforcement learning.

This approach enables an action and reward based modeling system that automates processes based on a range of actions along with the positve and negative rewards they generate. This approach results in dynamic, and continuously improving processes.

Most importantly, with Bellwethr anyone can design and then deploy
optimization models without needing any technical background.

Automated, Continously-Optimizing Artificial Intelligence

What's Your Reward Function?

In reinforcement learning a reward function is essentially the process that delivers feedback to a model. A reward function enables optimization to be automatic. Instead of insights that require human implementation, this solution is automatic and continuous.

Reward functions are the key to proper optimization and requires in depth domain knowledge to ensure that optimization is being tuned correctly. When reward functions are done correctly, the results can be tremendous and continously improving.

Bellwethr enables organizations to design their reward functions that drive tremendous, perpetual results.