Monitoring & Observability

Use AI-powered monitoring to enhance IT system visibility and automate issue resolution.

Business impact

• Handling time — AI reduces time to resolve incidents by providing real-time insights and root cause analysis
• IT costs — Automation and early detection lower operational expenses and resource waste
• Customer satisfaction — Improved system uptime and faster issue resolution enhance user experience
• Process efficiency — Streamlined workflows and automation increase throughput and reduce manual effort
• Regulatory compliance — Observability ensures audit trails and adherence to industry regulations
• System availability — Continuous monitoring maintains high uptime and reduces downtime
• Process latency — Real-time monitoring identifies bottlenecks to minimize delays
• Security incident response time — Faster detection and response to threats improve security posture
• Threat detection accuracy — AI models enhance precision in identifying security anomalies
• CRM efficiency — Monitoring AI in CRM improves task automation and customer interaction quality
• Automation rate — Increased automation through AI-driven orchestration accelerates process execution
• AI model uptime — Observability ensures continuous availability and performance of AI models

Data requirements

• Telemetry data (Numeric) — Collect metrics, logs, and traces to monitor system health and performance
• Application logs (Text) — Analyze logs for error detection and root cause analysis
• Event streams (Structured) — Track real-time events for anomaly detection and workflow monitoring
• User interaction data (Numeric) — Monitor user experience and detect issues impacting customers
• Security alerts (Structured) — Ingest security event data to identify threats and vulnerabilities
• AI model outputs (Text) — Evaluate AI responses and performance metrics for observability

AI methods and techniques

• Predictive AI — Forecast potential system failures and performance degradation before they occur
• Generative AI — Generate diagnostic explanations and remediation suggestions for detected issues
• Agentic AI — Autonomously execute remediation actions and orchestrate workflows based on observations

AI models and model families

GPT-4, Claude, Llama 2, Arize Phoenix, LangSmith

Industries

Real-world evidence

5 documented case studies on record.

Companies using this: Cisco, NORD LB, RBC Clear, Salesforce, Vodafone Three.