AI is moving from the application layer into the engineering workflow itself. The most forward-thinking engineering organisations are already using AI to accelerate delivery, reduce on-call burden, and optimise infrastructure spend - not as a future investment, but as a production capability today.

Predictive test selection uses AI models trained on your test history and code change patterns to identify which tests are actually relevant to a given commit. Instead of running your full test suite on every change, only the tests most likely to catch a regression are executed. This typically cuts CI build times by 50–70% while maintaining equivalent defect detection confidence.

LLM-assisted incident response changes the first minutes of an incident from frantic log triage to structured diagnosis. We build integrations that automatically surface relevant dashboards, recent deployments, correlated error patterns, and past post-mortems when an alert fires - giving on-call engineers a head start on every incident. The same tooling can draft post-mortem documents, generate runbook suggestions, and propose remediation steps based on incident characteristics.

AI-generated infrastructure code accelerates platform engineering work. Engineers describe what they need in natural language and receive Terraform or Helm configuration as a starting point, reviewed and refined through the standard IaC workflow. This dramatically reduces the time from requirement to provisioned infrastructure.

Cloud cost intelligence applies ML to your billing data to identify waste, model the cost impact of architectural decisions, and generate specific right-sizing recommendations across your compute fleet.

What it does

• AI-powered predictive test selection to cut CI build time without losing coverage
• LLM-assisted incident triage, runbook generation, and post-mortem drafting
• Intelligent cloud cost analysis and automated right-sizing recommendations