Where are you?
Not sure? The call is thirty minutes and ends with an answer, including “not yet”.
Why a strategy is not adoption
Without people
Tools arrive before training. Since February 2025 the EU AI Act has asked employers to support staff AI literacy; most still cannot show a record.
Without rules
Agents follow instructions from wherever they find them. A third of skills on public registries have a security issue, and some are built to steal credentials.
Without a place to run it
The pilot lives on a laptop with one person’s keys. Production needs an owner, limits, and a log.
The gap shows up in the numbers
Share of generative-AI pilots that stall before production.
Source: MIT NANDA, The GenAI Divide, 2025, as reported by Fortune
Share of 3,984 public agent skills with at least one security issue, in February 2026. 76 malicious payloads were confirmed by human review.
Source: Snyk, ToxicSkills
Train, govern, build, run
Six modules, a knowledge check each, a record per person. Free for your whole organisation.
Hands on: records
One approved catalogue of the skills, commands, agents and MCP servers your people and agents may use, served from a version you can point at. We run it on ourselves.
Hands on: a catalogue
One senior engineer, embedded for a quarter. Finds where AI pays back, ships the first agents with evals and guardrails, hands over.
Hands on: agents
Agents, models and sandboxes on your own machines, any provider or your own rack, with one gateway and a record of every call.
Preview
You can enter at any stage. Nobody is asked to buy the stack.
AI Champion, a new DevOps role
Most AI programmes fail on ownership, not technology. The AI Champion is one senior OpsHero engineer, embedded in your team two to five days a week, typically for a quarter, whose entire remit is finding where AI pays back and shipping it.
- Find the leverage. Shadow the delivery lifecycle, measure the toil, and rank workflows by payback rather than novelty.
- Build with your engineers. Pair on the first agents so capability transfers instead of leaving with the consultant.
- Set the guardrails. Evals, approval gates, data boundaries, spend caps and an escalation path that a human owns.
- Make themselves unnecessary. Handover is the deliverable: runbooks, skills and a named internal owner before we step back.
What stays yours, and what we hold
The repositories and the skills library
The training records
The agents and their evals
The machines and the data on them
What we hold
The control plane for your clusters, if you use the Platform. We say so plainly.
Compared with a consultancy, a dev shop or a platform
An AI consultancy
Best fit when you need the strategy before any build.
OpsHero
One senior engineer, embedded for a quarter, finds where AI pays back, ships the first agents with evals and guardrails, and hands over.
A dev shop
Best fit when you want the software built and do not need to own the practice.
OpsHero
We deliver all three, with one senior engineer embedded in your team, and everything stays yours.
An enterprise AI platform
Best fit when you are above a thousand people and have a platform team to run it.
OpsHero
People who know what the tools can do. Rules about what the tools may touch. A place to run them that you control.
Is your AI adoption governed?
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The ones people actually ask
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.
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.
We 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.
Do we have to buy all four stages?
No.
Can this run entirely on our own machines?
Yes. The Platform is in preview.
Is this compliance work or engineering?
Engineering that leaves compliance evidence behind.
Talk to an AI engineer. Not a salesperson.
- Which stage you are at
- Which workflows pay back
- What must be in place first
- What your team keeps