Vision-first recording
Docket records tests using screen coordinates instead of selectors, which lets it automate flows by interacting with the UI the way a user would.
Docket
ReclamarDocket is an AI-driven QA testing platform for mobile, web, desktop, iOS, and Android. Validate critical flows, reduce flaky tests, and catch regressions with vision-first automation.
DocketWhat Docket is
Docket is an AI-driven QA testing platform for mobile, web, desktop, iOS, and Android. It is built around vision-first end-to-end testing, using on-screen coordinates and AI-assisted steps to cover critical flows without depending on brittle selectors.
The product is positioned for teams that want to catch regressions earlier, reduce test upkeep, and keep coverage moving as applications change. Case studies on the site show Docket being used to validate acceptance criteria from Jira, run pre-release regression suites, and support plain-English test authoring for both technical and non-technical team members.
Core capabilities
Self-healing behavior
When UI elements move or layouts change, Docket updates click locations so tests keep running without manual selector maintenance.
AI steps for dynamic flows
AI steps handle changing screens and unpredictable flow branches in real time, which is useful for dynamic forms and other variable UI paths.
Cross-platform coverage
The platform supports running tests across mobile, web, desktop, iOS, and Android from one system.
Operational testing features
The homepage lists CI/CD integration, dedicated mailbox support, notifications, scheduled runs, and 2FA authentication as product capabilities.
Non-standard element coverage
The site says Docket can validate canvases, iframes, popups, and other non-standard elements that selector-based tools often struggle to reach.
Common use cases
Acceptance criteria review
Teams can validate Jira acceptance criteria against the live UI, using plain-English tests to check whether a ticket behaves as expected before it closes.
Pre-release regression testing
Engineering and QA teams can run a full regression pass before each release to catch breakage across multiple modules without maintaining brittle selector-based scripts.
UI-change resilience
Teams with changing interfaces can keep tests running when buttons, banners, or layouts shift, because Docket adjusts click locations instead of relying on fixed selectors.
Dynamic flow coverage
Organizations with complex forms or changing data can use AI steps to navigate branching logic and unpredictable states that are hard to script ahead of time.
Non-standard element testing
Teams testing canvases, iframes, popups, or other non-standard UI elements can use coordinate-based automation where traditional tools may struggle.
Pros and Cons
Pros
- Uses visual, coordinate-based automation rather than selectors.
- Includes self-healing to reduce breakage when the UI shifts.
- Supports plain-English test authoring in the case studies.
- Covers several environments from one platform, including mobile, web, desktop, iOS, and Android.
- Shows real-world usage for acceptance criteria checks and regression suites in the case studies.
Cons
- The collected sources do not include pricing, so buyers cannot assess cost or plan structure from this evidence alone.
- The sources mention integrations and operational features, but they do not provide a full published integrations list or detailed setup requirements.
FAQ
What does Docket do?
Docket is presented as AI-driven QA testing for mobile, web, desktop, iOS, and Android. It is designed to help teams cover critical flows, catch regressions before users see them, and reduce flaky tests.
What kinds of testing workflows does it support?
The source shows Docket using vision-first, coordinate-based automation, recorded steps, AI steps for dynamic flows, self-healing when UI elements move, and support for running tests at scale. It also mentions CI/CD integration, dedicated mailboxes, notifications, scheduled runs, and 2FA authentication as product capabilities on the homepage.
Who uses Docket in practice?
The case studies show Docket being used by QA and engineering teams writing tests in plain English, including acceptance-criteria checks from Jira and pre-release regression suites across multiple product modules.
Is pricing published on the site?
The pricing page at the provided URL returned a not-found page, so the available source does not confirm public pricing, plan tiers, or trial details.
Which integrations are confirmed?
The source does not provide a complete integrations list. It does mention CI/CD integration on the homepage and references Jira-driven acceptance criteria in case studies, but broader platform integrations are not fully documented in the collected pages.
Quick Facts
- Category
- AI-driven QA testing
- Platforms
- Mobile, web, desktop, iOS, Android
- Primary workflow
- Vision-first end-to-end testing and regression coverage
- Notable use case
- Jira acceptance-criteria validation
- Source domain
- docketqa.com
- Pricing
- Not published in the collected sources