Alert volumes have outpaced human response. ML and LLM systems fail in ways traditional monitoring doesn't catch. And every minute of downtime is more expensive than ever. AIOps closes that gap — when it's implemented properly.
AIOps — short for Artificial Intelligence for IT Operations — is the application of machine learning, causal inference, and pattern recognition to operational data (logs, metrics, traces, events, change records) so that IT operations teams can:
Detect anomalies in real time, before users feel them.
Correlate alerts across systems so one incident produces one ticket, not 200.
Predict incidents based on historical patterns and leading indicators.
Surface root causes in minutes instead of war-rooming for hours.
Auto-remediate known failure modes through runbook automation.
In an AI-era enterprise, AIOps also extends to the AI systems themselves — monitoring LLM latency, agent task completion, RAG retrieval quality, model drift, and inference cost. This is what we call AI-system observability, and it's the layer most AIOps providers don't touch.
Six pillars — from assessment through 24×7 managed operations — implemented by the same engineering team that runs your AI systems.
A 2–3 week diagnostic of your observability stack, alert hygiene, automation maturity, and AI workload coverage — ending in a phased implementation roadmap with target MTTR, noise-reduction, and ROI metrics.
Design and deployment on Datadog, Dynatrace, Splunk, New Relic, ServiceNow ITOM, or the OpenTelemetry + Grafana + Prometheus open-source stack. We're toolchain-agnostic and recommend based on your scale, budget, and existing investment.
ML models tuned to your environment — not generic templates. Time-series anomaly detection, topology-aware event correlation, and noise suppression that cuts alert volume by 60–90% without missing real incidents.
Predictive models for capacity, latency, and failure-mode forecasting, paired with runbook automation in ServiceNow, PagerDuty, or custom workflows. Known incidents get fixed before a human sees them.
LLM observability with LangSmith, LangFuse, or Arize AI. ML pipeline monitoring with MLflow, Evidently AI, and Weights & Biases. Agent monitoring for task success, tool-use accuracy, and cost-per-task. The capability most AIOps shops don't have.
24×7 AIOps operations run by Focaloid SREs — alert triage, model retraining, dashboard maintenance, and continuous tuning. Tiered SLAs based on environment criticality.
A side-by-side, so you can place AIOps in the broader operational landscape.
These are complementary, not competing. A mature AI-era enterprise runs all four. Most start with DevOps, add AIOps as scale demands, and layer MLOps/LLMOps as AI workloads enter production.
A 14–22 week phased rollout with measurable outcomes at each gate. Quick wins inside the first 60 days.
Audit observability coverage, alert hygiene, on-call data, MTTR baselines, AI workload exposure. Output: gap analysis + phased roadmap with target metrics.
01Stand up the observability platform — telemetry pipelines, unified data lake, log/metric/trace correlation, role-based dashboards. Eliminate the dark spots.
02Deploy anomaly detection, event correlation, and topology mapping. Tune ML models to your environment. Add AI-system observability for any LLM, agent, or ML workload in production.
03Wire in auto-remediation runbooks. Train teams. Transition to a managed-service model or hand off to internal SRE — your choice.
04Four sectors where every minute of downtime — and every minute of war-rooming — is costliest.
High-throughput trading, payments, core banking. Downtime is regulated and revenue-critical.
Clinical systems, EHR, telehealth. HIPAA-grade observability with patient-safety SLAs.
Multi-tenant platforms where one alert storm can break customer trust.
Connected operations, IoT fleets, supply-chain control towers.
Engineering depth, AI-native observability, and a vendor-neutral stance — the three things AIOps actually rides on.
LLM observability, agent monitoring, ML pipeline reliability — the layer most AIOps providers don't touch. Built on Arize, LangSmith, MLflow, and the broader AI observability stack.
We implement Datadog, Dynatrace, Splunk, ServiceNow, or open-source stacks based on your scale and economics — not a vendor relationship. Our recommendation comes with a written decision matrix.
AIOps lives or dies on engineering depth — telemetry pipelines, custom integrations, model tuning. Focaloid brings 13 years of platform engineering to every deployment.
Pick the engagement that matches where you are. Most customers begin with the Assessment and layer the rest as outcomes prove out.
Diagnostic + roadmap of your observability stack, alert hygiene, AI workload coverage.
Observability platform stood up and operational — telemetry, correlation, dashboards.
24×7 operations, tuning, and incident response. Tiered SLAs based on criticality.
Dedicated team. Long-running AIOps + SRE program ownership.
Whether you're drowning in alerts, scaling AI into production, or trying to bring MTTR down Focaloid's AIOps service is designed to take you there.