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Contextual Anomaly Detection is a versatile solution designed to identify irregularities across various applications. It can seamlessly integrate with an organization’s Business Processes and Information Systems infusing in them with data-driven intelligence capabilities. This transformation not only streamlines decision-making but also fortifies the systems, ensuring a proactive and informed operational strategy.
Context Aware Modeling: Models are built for distinct segments or aspects of an organization’s operation to better capture and learn from the specific characteristics of each segment.
Hybrid Ensemble Modeling: Solution combines an ensemble of Deep Learning, Statistical, Rule based models for decision-making.
Insight-Augmented Decision Making: Decisions are augmented with insights, providing a layer of understanding or explanation alongside the decision-making process.
Pluggable Architecture: Key elements of the solution, including Contextualization, Models, and Pre-processing, are structured as a collection of interchangeable components known as “plugins”. This modular design allows the system to adapt and evolve, facilitating updates or enhancements to individual components without disrupting the overall system integrity.