End-to-End Testing for the AI-Powered Meeting Platform
Challenge
Apollo.ai is an AI-powered all-in-one board and decision cloud that captures, manages, and shares knowledge before, during, and after meetings. The platform integrates AI directly into online meeting workflows to automate decision tracking and surface actionable insights for leadership teams.
The client needed manual end-to-end testing for a SaaS platform built from scratch, with no existing QA process in place — the project manager had been handling testing independently. Key challenges included: setting up the testing process from zero; ensuring sufficient test coverage without overloading sprints; validating functionality across multiple user roles and access levels on different devices and browsers; testing non-deterministic AI features; and preventing logic gaps at the planning stage before defects reached production.
Apollo.ai is an AI-powered all-in-one board and decision cloud that captures, manages, and shares knowledge before, during, and after meetings. The platform integrates AI directly into online meeting workflows to automate decision tracking and surface actionable insights for leadership teams.
The client needed manual end-to-end testing for a SaaS platform built from scratch, with no existing QA process in place — the project manager had been handling testing independently. Key challenges included: setting up the testing process from zero; ensuring sufficient test coverage without overloading sprints; validating functionality across multiple user roles and access levels on different devices and browsers; testing non-deterministic AI features; and preventing logic gaps at the planning stage before defects reached production.
Solution
After joining the project, the QA engineer conducted exploratory testing to learn the product while reporting defects in parallel — uncovering bugs missed by prior non-QA checks. An effective communication framework was established between the QA engineer, developers, and Product Manager to ensure efficient reporting and feedback.
The QA engineer prepared a comprehensive checklist covering functional, UI, compatibility, regression, and basic usability testing, updated continuously as new features were added. Detailed bug reports were written to help developers locate defects and root causes quickly. Change-related testing was run after each fix set — including retesting, smoke testing, and regression testing — to confirm that code changes did not affect existing functionality.
End-to-end testing focused on functionality and UI across various devices and browsers, with BrowserStack used for extended cross-browser coverage on Windows, macOS, and iOS.
After joining the project, the QA engineer conducted exploratory testing to learn the product while reporting defects in parallel — uncovering bugs missed by prior non-QA checks. An effective communication framework was established between the QA engineer, developers, and Product Manager to ensure efficient reporting and feedback.
The QA engineer prepared a comprehensive checklist covering functional, UI, compatibility, regression, and basic usability testing, updated continuously as new features were added. Detailed bug reports were written to help developers locate defects and root causes quickly. Change-related testing was run after each fix set — including retesting, smoke testing, and regression testing — to confirm that code changes did not affect existing functionality.
End-to-end testing focused on functionality and UI across various devices and browsers, with BrowserStack used for extended cross-browser coverage on Windows, macOS, and iOS.
Results
The QA engineer joins Apollo.ai's team for one week per sprint, running extended testing after each code iteration to validate software quality across all usage scenarios.
The platform is tested on Windows, macOS, and iOS across multiple browsers and OS versions. 100% of blockers and Critical/Major defects are caught before each production release, ensuring no critical issues reach end users. The majority of UX and interface improvement suggestions made by the QA engineer have been approved and implemented by the Apollo.ai team, reflecting the level of trust built through ongoing collaboration.
Key outcomes:
- Full QA process established from scratch on a platform with no prior QA coverage.
- 100% of blockers and Critical/Major defects caught pre-production release.
- Cross-platform coverage across Windows, macOS, and iOS via BrowserStack.
- UX improvement suggestions consistently accepted and implemented by the product team.
The QA engineer joins Apollo.ai's team for one week per sprint, running extended testing after each code iteration to validate software quality across all usage scenarios.
The platform is tested on Windows, macOS, and iOS across multiple browsers and OS versions. 100% of blockers and Critical/Major defects are caught before each production release, ensuring no critical issues reach end users. The majority of UX and interface improvement suggestions made by the QA engineer have been approved and implemented by the Apollo.ai team, reflecting the level of trust built through ongoing collaboration.
Key outcomes:
- Full QA process established from scratch on a platform with no prior QA coverage.
- 100% of blockers and Critical/Major defects caught pre-production release.
- Cross-platform coverage across Windows, macOS, and iOS via BrowserStack.
- UX improvement suggestions consistently accepted and implemented by the product team.