Intelligent IT operations start with AIOps
Dashboards are overflowing, alerts are piling up, and your engineers spend more time firefighting than building. AIOps brings order to the chaos: machine learning that analyses, correlates and automatically acts on your monitoring, logging and incident data. Appfront designs and implements the AIOps platform that makes your IT operations future-proof.
What is AIOps?
AIOps stands for Artificial Intelligence for IT Operations. The term was introduced in 2016 by Gartner analyst Colin Fletcher to describe a new category of tooling: platforms that use machine learning and big data analytics to automate and improve IT operational tasks. It is not a single product but an architectural approach that brings data from all your operational systems together and applies intelligence to it.
Traditional monitoring works reactively and relies on fixed thresholds. A CPU alert at 90% tells you that there is a problem, but not why, and certainly not that the problem is going to occur in three hours. AIOps replaces those static rules with adaptive models that recognise patterns, understand context and identify correlations that no human can spot in real time, not even your best SRE.
AIOps is not a synonym for enterprise AI. It focuses exclusively on IT operational processes: monitoring, alerting, incident management and capacity planning.
Key difference
Traditional:
CPU > 90% → alert → engineer looks for the cause → resolved 30 minutes later
AIOps:
Anomaly detected → root cause identified → automatically scaled up → zero impact
1. Observe
Collect and normalise data from metrics, logs, traces and events across your entire stack.
2. Interpret
Machine learning models that detect anomalies, filter out noise and identify the likely cause of incidents.
3. Act
Automated workflows that take action based on analysis, from alert routing to auto-scaling.
What does an AIOps platform include?
Anomaly detection
Unsupervised learning models learn the normal behaviour of your systems and only raise alerts for genuine deviations. No fixed thresholds, no alert fatigue. Ideal for teams that take SRE seriously.
- Dynamic baselines per service and time window
- Seasonal patterns automatically accounted for
- Multivariate detection across multiple metrics
- Adjustable sensitivity per environment
Root cause analysis
Correlation of signals from metrics, logs and traces to identify the most likely root cause within seconds. Engineers start fixing, not searching.
- Automatic correlation across service boundaries
- Dependency mapping with impact visualisation
- Probability scoring based on incident history
- Integration with existing runbooks
Predictive capacity planning
Predictive capacity models analyse trends, seasonal patterns and growth curves to accurately forecast when your infrastructure will reach its scaling limits.
- Forecasts for compute, storage and network
- Detection of capacity-related degradation
- What-if scenarios for growth and campaigns
- Cost simulation for scaling strategies
Automated incident management
Automatic classification by severity, impact and domain. Routing to the right team with all context attached. Escalations that are consistent and without delay.
- Intelligent triage by impact and criticality
- Ticket enrichment with logs, metrics and changes
- ChatOps integration (Slack, Teams, PagerDuty)
- Automatic post-incident timelines
Log analysis and pattern recognition
NLP and clustering find structure in gigabytes of unstructured log data. New patterns are flagged proactively, not only after customer complaints.
- Automatic categorisation without parsers
- Novel pattern detection for unknown issues
- Correlation with deployments and metrics
- From millions of log lines to actionable insights
Performance optimisation
Continuous analysis of the relationship between configuration, load and performance. Well-founded recommendations for query optimisation, container sizing and resource allocation.
- Identification of inefficient queries and processes
- Resource sizing based on current usage
- Regression detection across deployments
- KPI tracking against SLOs and SLAs
Your AIOps implementation partner in the Netherlands
- From proof of concept to production. We don't just build dashboards; we build end-to-end AIOps pipelines that run 24/7.
- Tool-agnostic, with no vendor lock-in. We integrate with your existing monitoring and logging stack.
- Proven in the Dutch market, with experience across SaaS platforms, MSPs and financial institutions operating under DNB regulation.
- In-house ML engineering. Our AIOps team consists of ML engineers, SRE specialists and platform architects.
How we work
Two-week sprints, direct communication via Slack, weekly demos. No consultancy overhead — engineering-first.
No vendor lock-in
We build on open standards and your own infrastructure. Everything portable, everything yours.
AIOps vs. traditional monitoring
Moving to AIOps isn't about replacing a single tool. It's a fundamental shift in how your organisation handles operational data, incidents and capacity. Read also how this fits within a DevOps strategy.
| Aspect | Traditional | AIOps |
|---|---|---|
| Alerting | Fixed thresholds, set manually per metric. Alert fatigue from high volumes. | Dynamic, self-learning baselines. Alerts only when genuine deviations occur. |
| Incident diagnosis | Manual searching through dashboards and logs. Dependent on senior engineers. | Automatic correlation across the entire stack. Root cause identified within seconds. |
| Capacity management | Periodic reviews, reactive scaling after problems occur. | Continuous forecasting, weeks ahead. Including cost projections. |
| Log processing | Searching for known patterns. Unknown issues only discovered after complaints. | Automatic clustering and anomaly detection. Proactive signalling. |
| Incident response | Manual triage and routing. Quality varies by shift. | Automated classification and routing. Consistent, 24/7. |
| Knowledge retention | Held in the heads of team members. Vulnerable to staff turnover. | Captured in models and workflows. Doesn't leave with employees. |
Model management: MLOps under the bonnet
An AIOps platform is only as good as the models driving it. Those models degrade when your infrastructure changes or traffic patterns shift. Without structural model management, your platform gradually loses its effectiveness. This also affects your broader data engineering.
Model monitoring & drift detection
Continuous monitoring of model accuracy. When incoming data shifts away from training data, the system flags this and triggers a retraining pipeline.
Automated retraining
Scheduled and event-driven retraining on recent data. New model versions are validated against a holdout dataset and only rolled out once they perform better.
Feature store & data quality
Centralised feature store ensuring consistency between training and inference data. Built-in quality checks prevent contaminated data from undermining model performance.
Test your idea first: a working prototype in 1 day
With OneDayBuild, we turn your idea into something tangible in one day for €1,150, so you can see whether further development is worth the investment. Decide to go ahead with the full build? Then we credit the full cost.
Explore OneDayBuild →Fits your existing stack
We don't build a replacement for your monitoring or logging stack — we build the intelligence layer that sits on top of it.
Monitoring & observability
Datadog, Grafana, Prometheus, New Relic, Dynatrace, AWS CloudWatch, Azure Monitor, GCP Operations Suite
Logging & search
Elastic Stack (ELK), Splunk, Grafana Loki, Fluentd, CloudWatch Logs, Google Cloud Logging
Incident management
PagerDuty, Opsgenie, ServiceNow, Jira Service Management, Slack, Microsoft Teams
Infrastructure & orchestration
Kubernetes, Docker, Terraform, AWS, Google Cloud, Azure, Ansible, ArgoCD
Where we deploy AIOps
SaaS & cloud platforms
Uptime is directly linked to revenue. AIOps monitors your multi-tenant environment per tenant, predicts scaling moments and reduces MTTR for incidents that affect multiple customers.
E-commerce & retail
Flash sales, seasonal peaks, unpredictable traffic. AIOps learns the relationship between campaigns and system load, scales proactively and detects anomalies in payment flows.
Fintech & banking
Zero tolerance for unauthorised deviations. Continuous compliance monitoring, transaction pattern analysis and audit-ready reporting with full traceability.
Logistics & supply chain
IT systems directly connected to physical processes. Downtime has immediate consequences for deliveries. AIOps monitors the operational chain end-to-end.
Healthcare & critical infrastructure
IT failures can put lives at risk. AIOps provides an additional layer of protection for EHR systems, laboratory integrations and communication platforms.
Data residency and compliance
All AIOps models can run on-premises or in a European cloud. Appfront ensures that logging and telemetry data is never processed outside the EU, in line with GDPR and sector-specific regulations such as NEN 7510 (healthcare) and DNB guidelines (financial sector). Your operational data never leaves your own infrastructure.
Our approach in five steps
Assessment and data strategy
We map out your tool landscape, data flows and operational bottlenecks. Based on this, we define which AIOps use cases will have the greatest impact.
Architecture design
Together, we design the target architecture: data pipelines, model choices, integration patterns and infrastructure requirements, resulting in a technical blueprint.
Integration and data pipelines
Integration of monitoring, logging and incident tools with the platform. Data is normalised, enriched and made available for ML models.
Model training and validation
Models are trained on your data: your infrastructure, traffic patterns and incident history. They are validated against historical incidents before going live.
Rollout and ongoing development
Live with a shadow period, followed by full operation. Your team receives training and documentation, and optionally ongoing managed services.
Frequently Asked Questions
Let your IT operations work for you, not against you
Every minute spent on manual triage is a minute not spent improving the platform. In a no-obligation conversation, we will show you what AIOps can concretely mean for you, based on your stack, your data and your operational challenges.