Three ways in: a pod that owns your operation, one more engineer inside your DevOps team, or DevOps consulting for a project or on a retainer. In all three, AI agents investigate alerts, draft the fix and watch cloud spend, and a person approves every change.
SaaS products and IT departments with systems in production, where deployments, alerts and cloud bills grow faster than the team that handles them. Teams that have tried an incident tool or an AI agent and still have one person who gets paged.
We own the operation: pipelines, infrastructure, incidents, cost and the agents. For teams with no DevOps function, or one person who cannot take a week off. Starts with the Operations baseline. Monthly.
You have a DevOps team and need another senior pair of hands. Your lead keeps the roadmap; the engineer works inside it, in your hours. Placed through our sister engineering company.
Add an engineer →A defined piece of work with a start and an end: a migration, a pipeline rebuild, observability from zero, an incident process, putting the first agents on your alerts. Fixed scope, quoted in the session.
Or the same engineers on a retainer, a set number of days a month, for teams that need expertise more than headcount.
Every shape leaves the same things in your repository: pipelines, runbooks, alert rules and agent configurations your team can run without us.
A closed list. Anything outside it is a conversation, not a surprise on the invoice.
Delivery pipeline. Build, test and deploy automation on your repository and your CI. Every change to production goes through it.
Infrastructure as code. Your environments written down, versioned and reproducible, on AWS, Azure or Google Cloud.
Observability. Metrics, logs and traces with alerts that mean something, and a written rule for what each alert expects someone to do.
Incidents. Response during the agreed hours, a runbook per known failure, and a written post-incident note within two business days.
Cloud cost. A monthly review of what runs, what is idle and what is oversized, with the changes proposed and approved before they happen.
Agents in operation. Agents that investigate an alert before anyone is paged, propose the fix, run approved runbooks and flag cost drift. Each one has a written list of what it may do alone, what it proposes, and who it pages when unsure.
Monthly, month to month.
A senior DevOps engineer of ours, inside your team, in working hours aligned with yours. The owner of the operation.
A deployment strategist, shared across pods, who sets up the agents, the baseline and the monthly review.
The agents, running on your accounts and your tools. What they learn about your systems stays in your repository.
Before the pod, the baseline. Our engineer reads your deployment history, your alert history and your cloud bill, and sits in your daily meetings for two weeks. You receive:
How often you deploy, how long a change takes to reach production, how many changes fail, how long recovery takes, alerts per week, and cloud spend per month.
The list, in order of hours saved.
What changes each month, and who approves it.
The baseline is invoiced when you receive the six numbers. The pod is measured against them every month after.
The engineer is in your daily meetings and on your alert channel. The first runbook is written from your last incident.
The first agent investigates alerts before anyone is paged. Every finding comes with its evidence and a proposed action.
The six numbers against the baseline, the list of changes approved and shipped, the cost review, and what the agents now handle alone.
What the pod built that your team can run without us: pipelines, runbooks, alert rules, agent configurations. All of it in your repository.
Two weeks, six numbers, a plan. You decide on the pod, the extra engineer or the project when you have them.