Production deployments solving real operational challenges.
Hundreds of WordPress sites across VPS servers with recurring high-CPU events. Traditional monitoring showed symptoms, not root causes.
AI analysed 500K+ combined access logs, error logs, and server metrics. Identified three simultaneous root causes: security scanner CPU consumption, cache preload bot saturation, and coordinated botnet attacks.
Detection in minutes vs hours. Specific remediation: scan config changes, Apache tuning, WAF rules. Recurring incidents resolved permanently.
Support engineers manually correlating server telemetry (CPU, memory, wait stats, blocking chains) with incoming tickets. Critical context buried in disconnected dashboards, slowing TTR.
AI telemetry bridge auto-enriches every ticket with real-time and historical server health data at creation time. Correlates metrics with known patterns, surfaces contextual summary with probable root cause and recommended diagnostics.
Engineers see server state at moment of issue without leaving the ticket. TTR improved 40% as manual data-gathering eliminated. System learns from resolutions, improving recommendations progressively.
Production SQL Server with 64 intermittent deadlocks across multiple trace files during batch processing. Patterns impossible to isolate manually.
AI analysis of Extended Events traces mapped the resource contention graph, identified competing processes, and pinpointed exact table/index combinations causing lock ordering conflicts.
Formal root cause documentation for vendor engagement. Specific remediation steps: index changes, batch scheduling, query optimisation targets.
Years of historical tickets, runbooks, and documentation inaccessible during live support. New engineers lacked institutional knowledge.
RAG system ingesting entire ticket history, wiki, and runbooks. Natural language interface returns contextually relevant historical solutions grounded in company data.
First-response quality improved. Onboarding time reduced dramatically. Institutional knowledge preserved regardless of staff turnover.
Seasonal SaaS platform struggling with capacity planning. Over-provisioning expensive, under-provisioning caused outages.
AI capacity modelling ingesting historical utilisation, traffic patterns, and business growth metrics. Rolling 30/60/90-day forecasts flagging breach thresholds weeks ahead.
Infrastructure spend optimised, zero capacity outages since deployment. Proactive scaling based on predicted demand.
Days per enterprise security questionnaire, manually cross-referencing policies and certifications across hundreds of questions.
RAG compliance assistant ingesting security policies, PCI-DSS docs, architecture docs, and previous questionnaires. Maps each question to source material and generates grounded draft responses.
Response time dropped from days to hours. Self-improving system builds richer response base with each questionnaire.