Who we are

Technology and consulting services for the public and private sectors

Monitexia is specialized in data management, processing and analytics, with experience in the development and implementation of monitoring solutions, business intelligence, big data & small data, machine learning and deep learning. Building on this foundation, Monitexia focuses on transforming raw technical signals into actionable insights that improve the performance, reliability and efficiency of business‑critical systems such as SAP, Oracle and HANA.

Solutions

Monitoring

Real-time infrastructure and application tracking for high-availability systems.

Our solutions are designed to be lightweight, open and accessible, combining a Java‑based collection layer with a modern metrics and visualization stack to give teams a clear view of how their applications and databases behave in real time. This approach allows operations, development and business teams to share a common performance view, shorten incident resolution times and make better, data‑driven decisions.

Linux Windows SAP Oracle Hadoop Elasticsearch

Observability

Full stack visibility to understand complex distributed architectures and behaviors.

Monitexia provides deep visibility into metrics and signals flowing through your SAP and database landscape, enabling you to understand not only whether a component is healthy, but how its behavior changes over time and under different workloads.

By correlating key technical indicators such as response times, resource usage and throughput our platform helps teams quickly identify bottlenecks, performance regressions and emerging risks without requiring complex manual configuration.

Anomaly Detection

Advanced ML-driven patterns identification to proactively prevent incidents.

Monitexia leverages advanced time‑series algorithms, including approaches similar to ADTK models and time‑shift comparisons, to automatically learn normal behavior from historical data.

Alerts are triggered using dynamic thresholds that adapt to seasonality and workload variations, removing the need to maintain static limits and drastically reducing noise while highlighting truly abnormal situations.

Anomaly Detection Visualization

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