Rovo and Atlassian Intelligence, automation across Jira and JSM, custom services on your own infrastructure. We enable it where the work already happens, group by group, with the access boundaries written down first.
Most clients start with one of these. It widens once the first pilot holds.
Rovo and Atlassian Intelligence switched on the way a security review expects. Group-based rollout, a written scope of what the AI may read, permissions cleaned up before launch, and procedures for running the features once they are live.
Agents and LLM workflows wired into the systems you already run: ticket triage and enrichment in Jira and JSM, document handling, internal assistants, integrations over the REST API. We start with the workflow where the payback is easiest to measure.
An independent read on what is worth adopting: a shortlist of use cases, what the licences really cost, the data protection questions, a usage policy people can follow, and training for the teams who will use it.
Retrieval over your own content, chat interfaces, agent services, and the plumbing between them and your data. It runs on infrastructure you own, in your cloud or on Cloudflare.
One workflow taken end to end in weeks, with a number attached: hours saved, tickets deflected, time to resolution. Enough to settle the licence question either way.
The same four steps as our other work, with the AI-specific parts folded into them.
We map where AI can realistically help: which workflows, what data sits behind them, which licences you already pay for, and what your own rules allow.
One workflow, taken to production with a measurement attached. It runs on real work and it finishes in weeks.
Access boundaries, data scope, logging and a usage policy. All of it before the rollout.
Group by group enablement, documentation, training for the people who will run it, and a retainer if you want us reachable afterwards.
We are Atlassian consultants first, and that shapes how we handle AI. In an enterprise the hard parts are permissions, data scope, licence cost, and who answers for the output when it is wrong. The model is usually the least of it.
So we work inside the governance you already have: your identity provider, your permission schemes, your data boundaries. Nothing we switch on gives the AI reach beyond the person using it.
And if a pilot fails to produce a number worth the licence spend, we will tell you to stop. That happens, and it is still a useful result. English-speaking, remote-first, working worldwide.
Six that come up in almost every first call.
Into the model your licence already covers, in the tenant and region you already use. For Rovo and Atlassian Intelligence that is Atlassian's own infrastructure under your existing agreement. For anything we build, you pick the provider and we document every call that leaves your network.
Whichever fits the task and your constraints. On Atlassian work the model is whatever the product ships. On custom work we choose per use case and keep the provider swappable, so a price change or a policy change on their side does not strand you.
Then the pilot has done its job. A measurement that says stop is still worth having, and the assessment is built to reach it early, while you can still act on it.
Three to six weeks from kickoff to a measured result, in most cases. What moves that number is how quickly we get access and how clean the data behind the chosen workflow turns out to be.
No. Atlassian is where our AI work usually starts, because that is where the processes already live. Automation, retrieval, and custom services run just as well against your other systems.
Yes, and that is usually the better shape. We build it with your admins and engineers so it stays maintainable once we are gone, and we document it for whoever takes it over.
Something else on your mind? Ask us directly.
Tell us the workflow that costs your team the most time. We will say whether AI is the right answer for it, and what it would take to find out.