Turn rough tickets into release-ready QA plans.

QAtalyst helps QA teams turn Jira tickets, feature notes, bug reports, and product context into test cases, risk reviews, bug reports, follow-up questions, Jira-ready work, TestRail-ready coverage, and automation starter direction.

Launch QAtalyst See how it works

Before / After

QA shouldn't start from a blank prompt.

Most AI test case generators stop at output. QAtalyst sits between messy tickets and release-ready execution — surfacing gaps, asking better questions, and keeping project memory close.

Without QAtalyst

Tickets land with missing context and unclear acceptance criteria.
QA chases scope, owners, and edge cases days before release.
Generated test cases sound generic and miss the real risk.
Risks are discovered after code is already merged.
Bug reports lack repro steps, environment, and severity context.

With QAtalyst

Detect missing information before it becomes rework.
Ask focused, triage-style follow-up questions like a senior QA.
Reuse saved project context across every workflow.
Generate structured, reviewable QA artifacts — not prompt soup.
Keep human QA judgment in control of every output.

Workflow

Bring context → choose workflow → triage gaps → create/export.

Bring context

Paste scratch notes, fetch a Jira ticket, or pull from saved project context.

Choose workflow

Generate test cases, review risk, improve a bug, shape a feature, or prep Jira/TestRail output.

Triage gaps

Review follow-up questions, missing info, unclear assumptions, and edge cases.

Create or export

Copy markdown, save context, create Jira-ready work, or prepare TestRail-ready coverage.

Workflows

Six focused workflows. One cleaner QA surface.

Each workflow is shaped around real QA work — not generic prompt boxes.

Test Cases

Generate release-ready QA coverage from Jira tickets, stories, and acceptance criteria.

Bug Writer

Turn rough bug notes into structured, Jira-ready defect reports with repro and severity.

Risk Review

Expose unclear requirements, bottlenecks, fragile areas, and release risks early.

Test Improver

Upgrade weak test cases and checklists into clearer executable coverage.

Feature Builder

Shape rough ideas into QA-ready feature briefs with follow-up questions and gap checks.

QAt Companion

Get project-aware QA guidance across risks, bugs, coverage, Jira handoff, and TestRail readiness.

Project Brain / Source Vault

Keep reusable QA context close to every workflow.

QA work gets better when the product history follows the ticket. Store source notes, decisions, risks, and examples once, then reuse them while generating tests, reviewing risks, shaping features, or preparing Jira and TestRail output.

Product notesKnown risksJira ticketsQA decisionsEdge casesRelease context

Integrations

Designed to meet the tools QA teams already use.

Keep the planning layer polished and reviewable, then move useful output toward Jira, TestRail, and automation setup without pretending AI should blindly own the release.

Jira

Fetch tickets and preview Jira-ready parent, child, and QA work before creating anything.

TestRail

Shape generated coverage into a cleaner TestRail-ready structure for review and handoff.

Playwright

Use coverage as a starting point for automation setup and starter skeleton direction.

More to come

The workflow is designed to grow without turning QA planning into scattered AI output.

Feature Builder + QA triage

Shape rough ideas before they turn into vague tickets.

Feature Builder treats early product work like a QA teammate would: brainstorm useful directions, identify missing information, then ask direct follow-up questions that make the scope testable.

Follow-up triageNeeds clarification

What acceptance criteria are missing?
Which user roles or permissions change?
What data or migration risk exists?
What needs product clarification before QA signs off?

Guardrails and trust

Built for reviewable AI-assisted QA, not AI chaos.

QAtalyst should make QA judgment faster to apply, not replace it. The landing page should reflect that same product philosophy: clear, inspectable, grounded, and team-safe.

Preview Jira work before anything is created.

Review and edit outputs before handing them to a team.

Use saved context deliberately instead of hidden magic.

Expose missing information instead of pretending every ticket is complete.

Treat automation as starter skeletons and setup direction, not guaranteed finished tests.

Keep AI output structured, inspectable, and grounded in the source material you provide.

Project memory is authorized server-side before it is used in AI workflows.

Jira and TestRail tokens are encrypted, masked, and never shown back in plaintext.