Connecting work with Atlassian Intelligence
An energy company had its code, documentation, and tickets in Atlassian tools that didn’t talk to each other, and no easy way to audit what changed. We set up Jira, Confluence, and Bitbucket for 220 people with proper access control, layered Atlassian Intelligence across the stack to correlate the three, and added commit-level audit controls.
Most teams already have their work in Atlassian tools. What gets left on the table is the connections between them: code in one place, documentation in another, tickets in a third, and no thread tying a change back to the reasoning and the work behind it.
An energy company of 220 people was in that position. Jira, Confluence, and Bitbucket were all in use, but siloed. Access control needed tightening. There was no straightforward way to trace a ticket to the documentation that explained it and the code change that resolved it, and the audit trail on that code was thin.
We set the foundation first, then added the intelligence on top.
We installed, configured, and administered Jira Server with Lightweight Directory Access Protocol (LDAP) for access control. We set up Bitbucket for code control and management, across all 220 employees, so the platform was governed rather than improvised. Then we implemented Atlassian Intelligence to correlate Confluence documentation, Jira issues, and Bitbucket repositories, so the three finally connected and a question that used to mean digging through three systems could be answered in one. To close the audit gap, we added commit-level controls using pre-commit git webhooks in Bitbucket Cloud, giving the kind of traceability that matters when you need to know exactly what changed and when.
What changed
| Dimension | Before | After |
|---|---|---|
| Access control | Improvised | LDAP-governed across 220 users |
| Code, docs, and tickets | Siloed | Correlated via Atlassian Intelligence |
| Code audit trail | Thin | Commit-level controls via pre-commit webhooks |
The AI earned its place here the way we think it usually should: by making the tools a team already owns more useful, rather than adding one more system to maintain. It is the same idea behind our Atlassian and AI work generally, and a small, concrete example of what “AI inside the platform” looks like when it is aimed at a real gap instead of switched on for its own sake.