Our predictive analytic solutions are based on Swarm Intelligence; our proprietary AI/ML engine that delivers superior pattern recognition and event prediction on complex, heterogeneous, dynamically changing data. Our solutions deliver results where traditional AI/ML has been limited by the complexity and expense of data science. User Applications are developed using standard templates, while APIs provide fast and easy integration into 3rd party partner workflows.
At the core of our solutions is Swarm Analytics™; thousands of individual algorithms that continuously analyze pieces of data, share their results with other algorithms and learn from them. This individual analysis delivers better pattern recognition across mixed combinations of structured and unstructured data. The model iteratively processes new data and feedback, rather than reanalyzing the whole upon changes, allowing the system to continuously converge on the best answer.
Paired to each algorithm is Swarm Learning™; individual machine learning that continuously tunes each algorithm based on unsupervised error correction or supervised human feedback. This individual tuning identifies local variance in the data to deliver increased accuracy compared to global machine learning. The engine tunes iteratively, rather than relearning the dataset and discarding prior insight, allowing the system to continuously learn in dynamic environments. Our solutions also explain what they learned which reduces need for interpretation.
Our applications are based on standard templates with a focus on results. Our user interfaces take the mystery out of ML, showing you what the machine has learned as well as its forecast. Our solution uses standard APIs which provide easy integration into partner UI Applications and workflows. Our platform is designed to capitalize on distributed IT architectures. While traditional AI/ML approaches are limited by centralized data storage and processing, the distributed nature of this solution inherently works across servers and systems to scale as needed.