AI and Machine Learning for networks
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Software product engineering

Monitoring & alerting

Building monitoring solutions for complex network environments

Our experience in a nutshell

CodiLime helps companies build solutions for effective network monitoring in complex heterogeneous environments. Regardless of workload and device type, our solutions ensure 100% monitoring coverage.

Network analytics

Network analytics

Monitoring data visualization

Monitoring data visualization



Machine learning / AI

Machine learning / AI


Network analyticsNetwork analytics

Modern network environments can be very complex and include bare metals (on-prem solutions), data centers, private and public clouds. Additionally, automation, orchestration and SDNs can greatly enhance your network management, but at the same time add yet another level of complexity. All this poses great challenges to enterprises that want to build an effective network monitoring and analytics solution.

The two most common scenarios for such a solution include:

  • expand the monitoring system to almost all devices/system/services
  • integrate existing (and used) systems into a single tool

We have experience in both approaches.

How we help

  • Design and implement solutions for anomaly detection and root cause analysis based on complex dependencies in the network
  • Develop adapters, plugins, integrations with software and hardware to gather data for network analysis
  • Expand monitoring systems to all devices, systems or services
  • Integrate multiple monitoring systems into a single tool
  • Develop backend solutions, including log monitoring and data analytics

Monitoring data visualizationMonitoring data visualization

Network applications generate tons of data that need to be analyzed quickly. The analysis should produce actionable insights that can be easily put into practice.

The brain processes visual data 60,000 times faster than it does text. Sound UX design with clear information-architecture can greatly improve the work of the operators of complex network monitoring systems and help them manage information-heavy environments.

A properly designed monitoring tool should have two characteristics. First, it should help users analyze the data. Second, it should navigate them towards the results they want to obtain. Data should be visualized so it adapts to the users’ actions and allows for an in-depth analysis, which is the ultimate goal of every data tool.

How we help

  • Prepare visual tools for experts to understand and examine network areas considered suspicious
  • Design and build an effective and user-friendly visualization of network topology and network monitoring data
  • Design and implement dashboards and widget customizations
  • Integrate multiple monitoring systems into a single tool with a single comprehensive UI
  • Build one-click integrations
  • Build a UX prototype of a network monitoring solution


As the application complexity resulting from interconnected ephemeral, heterogeneous and distributed workloads grows exponentially, so too do concerns about the security of such applications. Using cloud and third-party services and exchanging sensitive information at Layer 7 over a network all create dangerous blind spots:

  • Lack of real-time visibility of the interactions between various workloads, cloud and third-party services
  • Lack of control over the flow of sensitive data both internally and externally
  • Undetectable anomalous behavior and unsanctioned changes in applications
  • No real-time detection of lateral threats and vulnerabilities at runtime

How we help

  • Design and develop security monitoring systems
  • Ensure security and proper monitoring of cloud-native solutions
  • Secure Kubernetes deployments
  • Aggregate application and network security data to visualize it in a comprehensive UI

Machine learning / AIMachine learning / AI

AIOps is the application of machine learning to network operations. Be it for detecting malicious nodes, predictive maintenance of networking devices, or traffic or IT infrastructure monitoring—AI-powered solutions can improve efficiency, reduce labor and human error, and tighten security.

How we help

  • Develop anomaly prediction solutions based on:
  • historical data
  • device topology
  • event chains and correlations
  • device warnings
  • Design and implement solutions for anomaly detection and root cause analysis based on complex dependencies in the network
  • Create scalable solutions for network traffic monitoring resulting in actionable insights for traffic forecast and capacity planning
  • Automate and accelerate processes allowing infrastructure issues to be quickly detected