Evidence-backed backlog delivery
Turn verified requirements into stories ready for delivery
Reqify turns a BRD, FRD, or raw discovery file into a clean set of user stories — As-a / I-want / So-that, INVEST-checked, with acceptance criteria and Gherkin examples — ready to push to Jira.
- INVEST-checked
- Gherkin acceptance criteria
- Jira push (Team plan)
- Persona-aware
Writing user stories at scale is where backlogs go to die
A good user story is a tiny piece of writing that has to do a lot of work: capture a real user need, fit in a sprint, carry acceptance criteria precise enough for QA, and not duplicate the seven stories sitting nearby in the backlog. Senior BAs and product owners spend a disproportionate chunk of their week writing, re-writing, and chasing stakeholders to clarify them.
Generic AI copywriters produce stories that sound right and break the moment a sprint planning meeting touches them. Persona names get swapped, acceptance criteria collapse to "the feature works", and Gherkin examples invent edge cases that have nothing to do with the actual system. The team ends up rewriting every story anyway.
Story generation only earns its keep when the output is faithful to the source material, structured for the backlog tooling, and honest about its own uncertainty.
How Reqify writes user stories you can actually push to Jira
Reqify reads your discovery context — BRD, FRD, transcripts, personas, existing backlog — and emits user stories grouped by persona and capability. Each story is structured: actor, action, benefit, story points (rough estimate from complexity), priority, acceptance criteria in plain English, Gherkin scenarios for the happy path and primary edge cases, and a link back to the BRD or FRD it derives from.
INVEST checks run automatically. A story flagged "not Negotiable enough" or "missing Estimable signal" is surfaced inline. You see which stories need clarification before the next refinement, not after.
The output is also data, not just prose. Filter by persona, by epic, by acceptance-criteria completeness. Bulk-edit estimates. Then push to Jira with epic mapping intact and links preserved as Atlassian smart-links back to the originating Reqify artefact.
What you get with every generation
As-a / I-want / So-that format
Clean, consistent story statements. Persona names come from your actual persona definitions, not invented.
Acceptance criteria + Gherkin
Both plain-English ACs and Given/When/Then scenarios for the happy path and the main edge cases.
INVEST checks
Each story is rated on Independent, Negotiable, Valuable, Estimable, Small, Testable. Weak stories surface as warnings.
Epic and capability grouping
Stories are clustered by epic and capability, so backlog refinement starts organised instead of starting as a pile.
Traceability back to BRD/FRD
Every story carries the originating BR-ID or FR-ID. Coverage view shows which functional requirements have no story.
One-click Jira push
Team plan. Stories become Jira issues under the correct epic, with descriptions and ACs preserved as ADF.
From discovery to a sprint-ready backlog
- Step 1
Add your context
BRD, FRD, personas, transcripts, existing backlog. Reqify indexes them against the story schema.
- Step 2
Pick the slice
Whole product, an epic, a release, or a single capability. The model focuses where you point it.
- Step 3
Generate
Under a minute for a focused capability slice. Stories stream in grouped by persona / epic.
- Step 4
Review and tune
Edit any story. Flag duplicates. Mark a story as deferred. INVEST warnings stay live as you edit.
- Step 5
Push to Jira
Map to the target board, confirm epic mapping, push. Smart-links from Jira back to the Reqify artefact stay in sync.
Who this is built for
- Senior BAs writing 50+ stories per release
- Product owners running sprint refinement
- Agile coaches standardising story quality
- Consultancy teams handing off backlogs to client engineering
- Bid teams scoping fixed-bid agile delivery
- PMOs requiring traceability from BRD → FR → story → test
User-story generator FAQ
Are the acceptance criteria actually usable?+
Yes. Each story gets plain-English ACs plus Gherkin (Given/When/Then) scenarios for the happy path and a small number of primary edge cases. Edge cases that the model cannot derive from your sources are flagged so QA knows what still needs definition.
Will the stories use my personas?+
If you upload your personas — or if Reqify can extract them from your discovery files — the As-a section will use those persona names. Otherwise Reqify will use the actor labels from your BRD/FRD and flag that personas need to be defined.
Does this push to Jira?+
Yes on the Team plan. Stories push as Jira issues under the right epic, with ADF-formatted descriptions, acceptance criteria, and a smart-link back to the Reqify artefact. Confluence push is also available.
How does Reqify avoid duplicates?+
During generation, Reqify checks the new stories against any imported existing backlog and against each other. Likely duplicates are flagged before you push to Jira — you decide whether to merge or keep both.
Can I generate a single story or a whole epic?+
Both. Generate stories for a single capability, an epic, or the whole product. The structured output is identical either way.
How long does it take?+
A focused capability slice takes under a minute. A full product or release takes a few minutes. Watch the stories appear live as they generate.
Stop hand-writing every story
Generate a backlog-ready set of user stories with acceptance criteria, INVEST checks, and Jira push — in the time it takes to write one by hand.