Replacing a homegrown ML engine without disrupting a single user.
An employee engagement platform set out to replace its homegrown AI models with a third party large language model - without any interruption for users.
The transformation involved creating a large amount of backlog items, so the team looked for a way to automate the work. Initially, they integrated their Jira with an agentic AI solution through the Atlassian MCP server to automate the new backlog creation. A large part of it was automated, but at a certain point they noticed some of the key context wasn't being taken into account: most of the items came out similar and too general, and many of the important artefacts still had to be added manually.
With BA Star, the transformation project became a fully structured backlog. Fed with the platform's architecture docs, GitHub codebase, Confluence, Jira history, and migration notes, BA Star generated over 150 context-rich items. Dependencies between the legacy and new engine were flagged automatically. The team reviewed and published the whole backlog in three days.
How they use it
- Architecture docs, GitHub codebase, Confluence, Jira history, and migration notes fed in as context
- Over 150 context-rich backlog items generated and prioritised
- Dependencies between the legacy and new engine flagged automatically
- Phased migration roadmap created