The automation debt hiding in plain sight
The most expensive automation is the automation you haven't built yet. These are the patterns we see repeatedly.
The same runbook steps, executed manually, over and over
Database restarts, cache flushes, deployment rollbacks, cert renewals — tasks with documented steps that a human runs every time, hoping nothing goes wrong.
Incident triage takes longer than incident resolution
Engineers spend 20 minutes correlating logs, checking dashboards, and reading Slack history before they understand what's happening — then 5 minutes fixing it.
You know AI could help, but you don't know where to start
AI tools for ops are everywhere, but deploying them correctly — without creating new risks — requires experience. Most teams end up doing nothing.
What's included
Concrete deliverables — not vague "advisory" work.
Automation opportunity audit
A structured assessment of your ops workflows to identify the highest-value automation candidates by effort, frequency, and risk reduction.
Runbook automation
Convert your top-10 manual runbooks into automated workflows using Lambda, Step Functions, or your existing toolchain.
AI-assisted incident triage
LLM-powered incident summaries that correlate logs, recent deployments, and system state into plain-English context for on-call engineers.
Auto-scaling and auto-remediation policies
Event-driven automation that responds to infrastructure signals — scaling before you need it, remediating known issues before they page someone.
Infrastructure-as-Code migration
Convert click-ops and manual configurations to Terraform or CDK — so your infrastructure is reproducible, reviewable, and automatable.
Automated reporting and cost summaries
Scheduled generation of the weekly cost summaries, reliability reports, and deployment metrics that currently require manual assembly.
CI/CD integration for automation testing
Automated tests for your automation — so a change to a Lambda function doesn't silently break the incident response workflow.
AI tooling evaluation and integration
Assessment and integration of AI tools relevant to your ops stack: GitHub Copilot for infrastructure code, AI-assisted query generation, LLM-powered alert summaries.
How it works
A structured approach, not trial-and-error.
Toil inventory
We shadow your team for a sprint, documenting every manual, repetitive task with its frequency, duration, and error rate.
Prioritize and design
We rank automation candidates by ROI — time saved per month, risk reduction, and confidence level — and design the first batch of automations.
Build and validate
Automations are built with tests, documentation, and rollback procedures. We run them in parallel with manual processes until confidence is established.
Expand and maintain
Each month we identify new automation opportunities and maintain existing workflows as your infrastructure evolves.
What you can expect
Specific, measurable results — not "improved efficiency."
8–15h
Monthly ops hours reclaimed per engineer
Based on typical automation audits — hours currently spent on manual, repetitive tasks that can be safely automated.
75%
Faster incident context gathering
AI-assisted triage reduces the time from 'alert fired' to 'I understand what's happening' from 20+ minutes to under 5.
Zero
Manual steps for your top 10 runbooks
The tasks that require a human to babysit them at 2am become automated, tested, and self-executing.
Who this is for
This service works best for companies in a specific situation. Here's how to know if it's right for you.
Related services
Most clients combine multiple services for complete cloud coverage.
Managed DevOps & Delivery
Automation and good CI/CD go hand-in-hand. Automated deployments are the foundation everything else builds on.
Observability & Intelligence
AI-assisted incident triage is only as good as the observability data it draws from.
Cloud Cost Intelligence
Automate cost governance — tag enforcement, anomaly alerts, and savings plan analysis.