AI & AIOps
Operations that learn from every incident.
We apply machine learning and large language models to your monitoring data to cut alert noise, spot problems earlier and resolve incidents faster, and we run the infrastructure your own AI workloads depend on, on AWS, Azure or Google Cloud.
- AWS Advanced Tier Services Partner
- ISO 27001 certified
In short
VA2PT's AIOps service applies machine learning and large language models to IT operations on AWS, Microsoft Azure and Google Cloud. We correlate and de-duplicate alerts, detect anomalies in metrics and logs, predict scaling needs, automate root cause analysis and runbooks, and use LLM-assisted triage to speed up incident response, alongside MLOps and GenAI infrastructure on Amazon Bedrock, Azure OpenAI and Vertex AI.
Sound familiar?
Too many alerts, too little context
One failure triggers dozens of alerts across tools, and engineers spend the first minutes of every incident working out what is related.
Static thresholds miss real problems
Fixed limits either fire constantly or stay silent while a slow degradation builds.
AI workloads are expensive and new
GPU costs, model deployment and LLM security are unfamiliar territory for most platform teams.
What we do
The work inside AIOps, run by senior engineers.
Alert correlation and noise reduction
We group related alerts across tools into a single incident and suppress duplicates, so responders see one clear signal.
Anomaly detection
Models learn normal patterns in your metrics and logs from CloudWatch, Azure Monitor, Google Cloud Monitoring or Prometheus, and flag unusual behaviour that static thresholds miss.
Predictive scaling
Forecasts based on traffic history scale capacity ahead of demand, not after latency has already risen.
Automated RCA and runbooks
We automate evidence gathering and known fixes, so common incidents are diagnosed, and often resolved, without waiting for an engineer.
LLM-assisted incident triage
An assistant summarises the incident, links related changes and past RCAs, and suggests next steps to the on-call engineer.
MLOps and GenAI infrastructure
We build and run infrastructure on Amazon Bedrock and SageMaker, Azure OpenAI and Azure Machine Learning, or Vertex AI, control GPU costs, and add guardrails and LLM security controls.
How the engagement runs
- 1
Assess your operations data
We review your alerts, metrics, logs, incident history and runbooks to find where automation will help most.
- 2
Design the pipeline
We choose correlation, detection and automation approaches, and agree what can be automated and what needs human approval.
- 3
Implement in stages
We start with alert correlation and triage support, then add anomaly detection, forecasting and automated runbooks.
- 4
Measure and tune
We track alert volume, detection accuracy and time to resolve, and retrain and tune with every incident.
What you get
- AIOps readiness assessment
- Alert correlation and de-duplication rules
- Anomaly detection on key services
- Automated runbooks for common incidents
- LLM incident assistant integrated with your chat tool
- MLOps / GenAI infrastructure blueprint with cost controls
Tools we work with
- Amazon CloudWatch Anomaly Detection
- Azure Monitor
- Google Cloud Monitoring
- Amazon Bedrock
- Azure OpenAI
- Vertex AI
- Amazon SageMaker
- Prometheus
- Grafana
- OpenTelemetry
- PagerDuty
Questions buyers ask
What is AIOps?
AIOps, short for artificial intelligence for IT operations, uses machine learning and, increasingly, large language models to analyse the metrics, logs, traces and alerts your systems produce. It groups related alerts, detects unusual behaviour, predicts capacity needs and helps find root causes faster. The aim is fewer, clearer alerts and quicker resolution, with engineers spending time on fixes instead of sorting noise.
Does AIOps replace our on-call engineers?
No. AIOps handles the repetitive work: correlating alerts, collecting evidence, suggesting likely causes and running well-understood fixes. Engineers still make judgement calls, approve risky actions and handle new kinds of failure. At VA2PT, AIOps runs alongside our 24x7 SRE team, so automation speeds people up rather than leaving incidents to a model alone.
How do you use LLMs in incident response safely?
The LLM assistant reads incident data, recent deployments, runbooks and past RCAs to produce a summary and suggested next steps. It does not execute changes on its own; actions go through approved automation or an engineer. We limit what data the model can access, keep sensitive data inside your own cloud account where possible, for example with Amazon Bedrock, Azure OpenAI or Vertex AI, and log every interaction.
Which clouds and monitoring tools does this work with?
AIOps works on the telemetry you already have. We ingest alerts and metrics from Amazon CloudWatch, Azure Monitor, Google Cloud Monitoring, Prometheus, Elasticsearch and commercial APM tools, and route incidents through PagerDuty, Opsgenie, Slack or Teams. Multi-cloud estates benefit most, because correlation across providers is exactly where humans lose time.
Can you also run infrastructure for our own AI products?
Yes. We build MLOps and GenAI infrastructure on AWS, Azure and Google Cloud, including Amazon Bedrock and SageMaker, Azure OpenAI and Azure Machine Learning, and Vertex AI, with model deployment pipelines, GPU capacity planning and cost controls, and guardrails that address LLM security risks. For application-level GenAI work, see our AI Engineering Services.
Guides and field notes
What our engineers have written about AIOps: fixes, checklists and lessons from production.
Talk to us about AIOps.
A 30-minute call with a senior engineer. We look at your setup, name the biggest risks and outline what we would do first.