We help teams use AI to speed up incident investigation, log analysis and alert triage — an assistant that correlates signals and suggests next steps alongside your engineers, not a replacement for their judgment.
AIOps — applying AI/ML techniques to operations data — is most useful where there is a lot of signal to sift through quickly: correlating an alert with recent deploys and related service errors, summarizing a wall of log lines into a likely cause, or flagging a metric that looks abnormal against its historical pattern.
We are deliberate about the boundary: AI assists a human investigation — surfacing correlations and suggesting a starting point — it does not autonomously change production infrastructure. That distinction matters for both safety and trust in the tooling.
Too many alerts fire with too little context, so real issues get lost in the noise.
Finding the cause of an incident means manually correlating metrics, logs and recent deploys.
Production log volume has grown well past what an engineer can scan during an incident.
Only one or two engineers know how to investigate certain classes of incidents.
Correlate alerts with recent deploys, related errors and infrastructure changes.
Summarize and surface the log entries most likely relevant to an active incident.
Group and prioritize alerts so engineers see the signal, not the noise.
Flag metrics that deviate from historical patterns, where the underlying data supports it.
Give engineers a starting hypothesis for investigation instead of a blank dashboard.
Apply AI assistance to code review, deployment risk assessment and operational documentation.
Review current observability data and incident process.
Connect AI-assisted analysis to existing signals.
Tune correlation between alerts, deploys and logs.
Surface AI-generated hypotheses to on-call engineers.
Validate AI suggestions against real incident outcomes.
Refine based on what actually helps investigations.
Review current observability data and incident process.
Connect AI-assisted analysis to existing signals.
Tune correlation between alerts, deploys and logs.
Surface AI-generated hypotheses to on-call engineers.
Validate AI suggestions against real incident outcomes.
Refine based on what actually helps investigations.
AI-assisted correlation gives engineers a starting point, not a blank dashboard.
Triage surfaces what matters instead of every threshold crossing.
Less reliance on one or two engineers who "just know" the system.
AI assists the decision; engineers still make it.
We bring metrics, logs and traces together — using Prometheus, Grafana, AWS CloudWatch and the ELK Stack — to help engineering teams understand system behavior, identify issues and respond with real operational context.
We help teams improve production reliability through measurable service objectives, incident response processes, capacity planning and practical operational automation.
We build repeatable delivery workflows that connect source control, testing, infrastructure, security and deployment into a reliable engineering process — using Jenkins, GitHub Actions, GitLab CI/CD and Docker.
More on this and related topics.
A complete guide to Amazon CloudWatch Omni, AWS's AI-powered observability platform for applications and AI agents, built on OpenTelemetry.
Modern systems need more than basic monitoring. Learn how metrics, logs and traces work together to provide useful operational visibility.
Reliability starts at architecture. Explore the practices that help teams build dependable systems and respond effectively when things fail.
No — and we are direct about this. AI-assisted DevOps in this context means faster analysis and correlation of signals to help your engineers investigate; it does not mean unsupervised automation making changes to production infrastructure. Human judgment stays in the loop.
Tell us what you're building, where you're facing infrastructure challenges, and what you want to improve.
