Advanced technologies for anomaly detection in multivariate time series
With mAInthink's DeepAnT consultancy, we have set new standards in real-time anomaly detection.
Data volumes are growing exponentially - but valuable insights can often be gained from them. Companies collect millions of data points every day, but the real challenge is to recognise relevant deviations in this data noise, before they lead to safety risks, production downtime or financial losses.
This is where DeepAnT Consulting comes in - our deep learning-based solution for anomaly detection in multivariate time series, developed by mAInthink.ai.
Why DeepAnT Performance?
Multivariate intelligence
Unlike conventional models, DeepAnT not only recognises outliers in individual variables, but also understands the interaction of multiple data streams over time. This makes even complex, context-dependent anomalies visible - for example, when individual metrics behave normally but their combination indicates a system failure.
DeepAnT Performance works in real time and is scalable - and fast. Very fast.
While conventional methods such as ARIMA, rPCA or LSTM often only work retrospectively or require considerable computing effort, DeepAnT recognises deviations in real time - even in highly dynamic IT environments with thousands of data channels.
Ready for practical use
Whether IT security, predictive maintenance or financial risk management - DeepAnT is used by companies that have no time for speculation.
Your advantages at a glance:
- Rapid response to impending failures or attacks
- Significantly fewer false alarms thanks to context-based analysis
- Scalability for growing data volumes and complex infrastructures
Clarity instead of chaos.
If you are responsible for stability, security or efficiency in your organisation, it's time to consider intelligent anomaly detection. Because
Knowing is good. Recognising patterns is better.
Let's talk.
With DeepAnT, we'll show you how to recognise risks before they get worse.
