Deployment Labs is a small team that is part consultant, part systems designer, part trainer, part implementation partner. We help companies onboard teams to OpenAI, Claude and Codex, design the internal workflows, build the skills and plugins, and leave behind an AI operating system the team actually runs.
Most teams have access to powerful AI tools. Very few have deployed them into the way work gets done. AI is only valuable once it is part of the daily work.
We don't just build workflows. We train your team to use, maintain, and improve them, because a workflow nobody owns is a demo with extra steps.
OpenAI, Claude, Claude Code, Codex, Cursor. We help teams understand which platform to use, where it fits, and how to build around it as the market moves.
The outcome of an engagement is reusable infrastructure: workflows, skills, plugins, documentation, standards, and habits. Not a slide deck.
Before Deployment Labs, we were deploying AI into a business we run day to day. Not a pilot. The front desk, the documentation, the follow-up, the reporting, and the people who had never opened a chatbot and did not want to.
The tools that stuck had three things in common: a named owner, a place to live inside the work, and a review step people understood. Everything without those went back to being someone's chat history within a month.
That is the playbook. Platform onboarding that starts with the tools you already pay for, the first workflow chosen for volume rather than glamour, governance written next to the work instead of in a document nobody opens, and two days in the room rather than a webinar.
One runs the engineering, one runs the business and the room. On the two-day program you get both, plus the engineers who pair with each pod.
Runs the technical side of every engagement: platform setup, Claude Code and Codex onboarding, and the skills, automations and internal tools a team leaves with. Writes the Claude Code hub.
Runs the business and the client side: scoping, discovery calls, the two-day training program, and making sure what gets built is still in use a quarter after we leave.
Training and hackathons happen on site, with our engineers next to your team the whole time.
Nothing ships without a named person who keeps it running after we leave.
Review depth is tiered by risk, written next to the workflow, so people know which tier they are in.
If the honest answer is "you don't need us for this", that is the answer you get.
The default for most non-technical teams. Projects, custom GPTs, admin controls.
Long documents, careful writing, and Projects for shared context across a team.
The coding agent where most of our engineering onboarding happens.
OpenAI's coding agent. Paired with Claude Code depending on the stack.
The editor teams already have open. We wire it into review and repo conventions.
For the automations and internal tools that outlive the chat window.
Almost nobody in this category publishes numbers. Here is what we can say before a call, and what has to wait for one.
One specific question, prepared for and written up. Credited toward any engagement booked within 60 days.
About the consultationTwo days on site for a whole team or department. The proposal includes the number of engineers we bring and the day-two backlog.
About the trainingTwo to four weeks with an engineer embedded in one team, building the first workflows and handing over ownership.
About the sprintA discovery call is free and is the right place to start for training, a sprint, or a retainer. If you have one specific question, the consultation is built for it.
