We know that the Opsani AI works because we rigorously test our optimization engine across a wide variety of synthetic client applications. Our AI optimizes these applications tens of thousands of times, and records how well it performs in aggregate and worst case scenarios.
A useful analogy is a race car. Manufacturers build different tracks and test different types of tyre in order to test how our a car performs on different terrains. Why? Because they need to be sure their race car functions as powerfully as possible in all conditions. The same goes for our Opsani AI engine. Our application is the race car and our testing and production environments are the race tracks. We test hard, because we need to know that our optimization system works across a wide range of environments and settings.
Opsani’s AI optimization engine then fine tunes the race car to ensure maximum performance in any scenario.Testing these synthetic applications generates evidence for our ML team, who use that data to analyze our AI’s performance. We use that analysis to make sure our Opsani AI engine will be able to handle any real client application we throw at it.
In theory, we could encounter an application in the wild that we’ve never seen before. However, as we continue to generate both de novo and data-driven synthetic applications, we cover more of the space of potential application response modalities, especially those most similar to the applications we’ve already seen.
If you’re interested in running your apps for half the cost at higher performance, please reach out for a demo of Opsani AI.