Storymate
AI-powered story slicing, test cases, and sprint planning fo
Every sprint, the same expensive ritual: product owners rewriting vague epics, engineers sitting in story-pointing meetings, QA scrambling to write test cases after the fact, and a sprint plan that's wrong by Wednesday. Smart people, doing work they hate, on repeat.
I built Storymate to fix that. You drop in a rough feature, and it slices it into user stories shaped by your domain prompts, documents, personas, and Definition of Ready rules. It generates test cases from approved stories. It suggests sprint candidates from refined work. It keeps traceability from feature through story through test - automatically.
The team stops drafting from a blank page and starts reviewing, deciding, and improving instead.
It connects to Jira, Azure DevOps, GitHub, GitLab, Confluence, TestRail, Xray, and Zephyr Scale - so it fits into your existing toolchain rather than replacing it. You can also bring your own LLM if your organization has existing AI contracts or data residency requirements.
I launched it a few months ago and I'm looking for feedback from teams that run regular refinement cycles. Happy to answer questions or give anyone a walkthrough.
1 Comment
One thing I found interesting reading this is that most of the post is framed around generating stories faster.
But a lot of the value you're describing seems to come from what happens after generation—consistency, traceability, and reducing interpretation gaps across the team.
Those feel related, but not necessarily the same reason someone would adopt the product.