CM CloudMast

AI enablement partner

Turn AI investment into real work.

We help enterprises choose the right work, build AI into real workflows, and leave teams with systems they can run and improve.

Choose the work Build the workflow Own the capability
Discuss a use case How we work

The gap

AI pilots stall between a promising demo and everyday work.

The model is rarely the whole problem. Value, integration, trust, and ownership have to move together.

01

Unclear value

No one has defined what improves or how the result will be measured.

02

Disconnected workflow

AI sits beside the work instead of connecting to the data, systems, and decisions that shape it.

03

No path to ownership

The pilot can launch, but the team cannot govern or improve it without outside help.

AI platform adoption at scale

Use the right tool. Build the operating model around it.

We've helped engineering organizations with thousands of developers adopt AI platforms and ship faster. We help teams use Claude Code, Codex, and Cursor for product delivery and run-the-business work. That includes DevOps pipelines and cloud infrastructure deployments.

We help teams choose where each tool fits, then add the context, controls, evaluation, and ownership needed to use it well.

Claude Code running in a terminal

Claude Code

Terminal-first

For teams that want an agent working directly in the repository and command line.

View Claude Code
The Codex app showing projects, threads, skills, and automations

Codex

Parallel work

For delegated tasks that need visible threads, isolated work, and reviewable changes.

View Codex
Cursor showing agents, a product plan, and implementation tasks

Cursor

Editor-first

For day-to-day coding where AI should stay close to the editor and the engineer.

View Cursor
GitHub Copilot app with sessions and a prompt

GitHub Copilot

GitHub-native

For teams that want AI help across the editor, issues, and pull requests they already run on GitHub.

View GitHub Copilot
CloudMast operating layer Context Guardrails Evaluation Cost Ownership

AI gateways and harnesses

Opinionated harnesses that make AI safe to scale, not just available.

We set up shared workflows, context, and controls around your AI tools.

A gateway sits in front of the providers, so the workflow keeps one interface when the model behind it changes.

Spend visibility

Route model and tool usage through a gateway so you can see cost by team, tool, and workflow.

Governance

Apply policy, access controls, and guardrails in one place instead of per tool.

Usage insight

Understand how people actually use AI day to day, so you know what's working and what to scale.

How we work

From one use case to owned capability.

Senior engineers work with the people who know the job, build in your environment, and transfer ownership as the system proves itself.

We call this a forward-deployed engineering model: practical implementation in your environment, with ownership transferred to your team.

01

Focus

Choose one workflow and define the result

02

Build

Connect AI to the work, data, and systems

03

Prove

Measure quality, adoption, cost, and impact

04

Transfer

Leave ownership and a repeatable pattern

What we do

Four capabilities. One delivery system.

Use them together or start where the constraint is.

Next step

Start with one workflow worth improving.

Discuss the opportunity patrick@cloudmast.dev