Based on the AI ​platform algorithm model, AISWare AIOps will focus on the three directions of quality assurance, cost management and efficiency improvement to conduct component-based packaging of intelligent solution capabilities for the entire process of fault discovery, diagnosis, self-recovery, and prediction, as well as scenarios such as intelligent decision-making, intelligent Q&A, capacity planning, resource optimization, etc., build reusable and evolvable operations learnware, and externally provide capabilities in the form of Open API. It supports the rapid implementation of intelligent operations demands, simplify the procedure, and reduce the cost of intelligence integration of the operations system.

Product superiority

Product value


Improving overall operations efficiency

  • It supports the intelligent positioning of root cause of fault and provides a treatment strategy to realize fault self-recovery, reduces the dependence on personnel experience, shortens the fault positioning time, and improves the overall operations efficiency.

System operation quality assurance

  • With the early fault warning engine, early warning of business and system risks can be carried out in time when indicators degrade to avoid production faults and ensure the quality of system operation.

Reasonable control of operating costs

  • It support intelligent evaluation and optimization of resource efficiency, plans the capacity in a reasonable manner, improves resource utilization efficiency, and further controls operating costs.

Enhancement of per capita operations capabilities

  • By introducing AIOps capabilities and technologies, operations staff is liberated from processing complicated alarms and high-frequency repetitive problems to enhance the per capita operations capabilities.

Application scenario

Quality assurance
  • Anomaly detection
  • Root cause analysis
  • Fault prediction
  • Fault self-recovery
Cost management
  • Capacity planning
  • Resource optimization
  • Resource assessment
  • Performance optimization
Efficiency improvement
  • Intelligent decision-making
  • Intelligent Q&A
  • Intelligent IPQC
  • Intelligent change

Customer success case

Anomaly detection for golden indicators

A telecom operator introduced AISWare AIOps for monitoring and alarm of multiple indicators such as Haproxy response delay, Kafka Topic traffic and business volume to achieve intelligent anomaly detection of dynamic threshold, which has successfully predicted several production faults.

  • 99% The recall ratio of anomaly detection is
  • 90% the precision ratio is
Alarm root cause analysis and convergence

A telecom operator introduced AISWare AIOps in the alarm convergence scenario of O-domain, which conducts real-time positioning of root causes for alarms and convergence through the dynamic mining of RCA rules of alarm, and effectively alleviates the alarm storm.

  • 98% Alarm compression ratio is greater than

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