Your intelligent anomaly detection assistant
We identify outliers and unusual behaviour using unsupervised machine learning techniques that are selected through our proprietary algorithm.
Our technology
AI Assistant
Streem.ai is ready to help anyone facing the daunting task of monitoring large amounts of time series data. It acts as a magnifying glass for domain experts, reducing manual data inspection efforts to allow more time for root cause analysis.
Intelligent
A continuously improving algorithm that trains on a variety of diverse data sets. Through testing and calibration with new machine learning models, our benchmarking algorithm has been designed to automatically analyse all available data in real time.
Adaptive
Algorithm agnostic by design to ensure a scalable and effective anomaly detection solution. Attention is directed towards irregular areas within any data set for inspection and previously unknown anomalies are uncovered.
Meet Streem.ai
Team
We are a highly skilled and diverse research team in the manufacturing heartland of Europe. We have academic and professional experience in a variety of fields including computer science, engineering, computational neuroscience, physics and psychology.
Technology
At the core of our product is the Streem.ai Benchmarking Algorithm. This system enables automated selection of the right anomaly detection algorithm(s) based on the given scenario and input data. The Streem.ai Benchmarking Algorithm runs the input streaming data through multiple algorithms varying from simple statistical methods to complex neural networks and scores them based on their performance.
Vision
Research is key to our success. In order to build a reliable and best in class benchmarking system, it is critical to work with heterogeneous data from across industries.
“StreemAI has successfully helped us reduce the time our engineers spend analyzing test data and directing our attention to the previously unknown. This is a major improvement of our development process and provides real cost savings.”
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