Everyone has an AI strategy. Almost nobody has AI in production.
We close that gap in one quarter: pick the workflows that pay back, build them against real data, and leave your team able to run them.
The pilot is not the hard part
Most AI initiatives stall after the pilot because nobody owns the infrastructure underneath. Ours starts with an embedded AI Champion who builds that foundation first, so the first automated workflow is running in production before the quarter is out.
The path we take, every time
AI Champion
A senior engineer embedded with your team for a quarter, owning the agents, the evals and the on-call rota behind them.
Agents
Agents that do a defined job against your systems, with evals proving they still do it next month.
Agentic workflow automation
The end-to-end path — triage, drafting, approval, action — wired into the tools your team already uses.
OpsHero Skills
A map of what your team can actually do, measured against what you run — and the gaps worth closing first.
AI literacy portal
Training paths per role, so the people around the pilot understand what it is doing.
Harness engineering
The scaffolding around a model: retrieval, tool access, sandboxes, rate limits and the observability that tells you when any of it drifts.
AI rides on the delivery you already have
DevOps
Automate delivery end to end, from infrastructure code to logs. An agent that ships has to ship through something.
Go to the pageAI adoption
Literacy, then two or three workflows with a measurable payback, built against real data and handed over.
Platform
Golden paths and a developer platform your teams actually want to use.
Go to the pageThe four steps above assume delivery, observability and access control already work. Where they do not, that is the first piece of work, and it is a DevOps engagement rather than an AI one.
What people ask before they start
How long does this take?
A quarter is the typical engagement. That is the span the four steps above are sized for — literacy, targeting, building, and handing over — and it is what the AI Champion engagement runs to.
Do we need AI expertise in the team first?
No, and that is why literacy is step one rather than step three. Everyone learns what the tools can and cannot do before anyone picks a workflow to automate, because a team that cannot tell the difference cannot choose well.
What happens when you leave?
The runbooks and the skills stay with your engineers. The whole point of the quarter is that your team can run what was built without us, which is the opposite of an arrangement that needs renewing.
We already ran a pilot and it went nowhere.
That is the common case, and the reason is usually that nobody owned the infrastructure underneath it. We start with an embedded engineer who builds that foundation first, so the first automated workflow is running in production before the quarter is out.
How do you decide what to automate?
Two or three workflows with a measurable payback, chosen in step two. Not the most interesting ones and not the most visible ones — the ones where the saving can be counted afterwards.
How do you know an agent still works next month?
Evals, built at the same time as the agent rather than after it. They run against real data and real pipelines, and they are what tells you the thing you shipped in March is still doing its job in June.
Talk to an AI engineer
Not a salesperson. You will get a view of which of your workflows are worth automating, and what the quarter would actually involve.
- Which of your workflows have a payback you could measure
- What has to be true in your delivery and access control before an agent can ship
- The evals and guardrails the work would need, named
- What your team keeps, and what they would be running without us