CM CloudMast

Forward-deployed AI engineering

Turn AI investment into real work.

Senior engineers embed with your teams to pick the right work, build AI into it, and leave you systems you 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.

Patrick has rolled out AI gateways and opinionated harnesses for engineering organizations with thousands of developers, after running production systems at Hulu and Under Armour. We put Claude Code, Codex, and Cursor to work on product delivery and run-the-business work. That includes DevOps pipelines and cloud infrastructure deployments.

We match each tool to the work it 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.

Forward deployed

From one use case to owned capability.

We don't hand you a strategy deck and leave. Senior engineers embed with the people who do the work, build inside your systems, and stay until the workflow runs in production. Then your team owns it.

Embedded, not advisory

We work in your environment, with your data and the people who know the job. Decisions get made where the work happens.

Production, not pilots

We build against real workflows and measure quality, adoption, and cost from day one.

Built to hand over

Runbooks, documented workflows, and a team that can run it. We count it a success when you need us less.

01

Focus

Pick where AI pays across the business and define the results.

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.

Who you work with

You work with the engineers who build it.

The engineer in your first meeting is the one writing the code and handing it to your team. Small by design, so nothing gets lost between the pitch and production.

1,000s

Developers covered by gateway and harness rollouts

Hulu, Under Armour

Production systems run before CloudMast

4

AI coding tools we deploy and govern

What we do

Four capabilities. One delivery system.

Use them together or start where the constraint is.

Next step

Tell us where AI should be doing real work in your business.

Discuss the opportunity patrick@cloudmast.dev