How JOIN automated QA with AI agents

Company Overview

• Company: JOIN Solutions AG
• Industry: HRTech / Recruiting software
• Business Type: AI-powered recruiting and job distribution platform
• Size: 50 - 200 employees
• Headquarters: Germany (Berlin)

Key Use Cases

• Automated Staging Regression
• AI-Assisted Test Generation (via natural language and documentation)
• Pull Request (PR) Exploratory Testing
• Critical Core Flow Verification (e.g., job applications and sign-ups)

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The Challenge: A QA bottleneck heavily slowing down development

JOIN Solutions operates a fast-paced development cycle where four engineering teams work concurrently on multiple projects. While their engineers maintain high standards using test-driven design, unit tests, and Cypress/Playwright for core "happy path" checks, all features must undergo thorough manual testing in a staging environment before being released to production.

This process created a severe bottleneck. JOIN’s three manual QA engineers were operating at maximum capacity, with each handling up to three projects at a time. Because the team refuses to release features without proper testing, a single QA engineer calling in sick could completely block project releases.

Standard manual regression took a minimum of one week per project. Mayada Ghanem, representing JOIN's QA leadership, knew that simply hiring more testers was not a sustainable path:
‍"I would like not to scale the QA team because it will never stop... at the end with more teams then you have 10 QA engineers all day clicking on buttons, which I feel that we can optimize."

The Solution: Turning QA Engineers into QA Managers

JOIN did not want to entirely replace human oversight with AI; rather, they wanted to give their QA engineers a tool that allowed them to do more without the heavy manual lifting. By partnering with QA.tech, JOIN transformed their QA engineers into "QA managers" who oversee automated AI agents.

The Results: Shrinking Regression from Weeks to Days

Ultimately, QA.tech allows JOIN Solutions to maintain their strict quality standards and safeguard their critical application flows, all while scaling their output and preventing QA from becoming a roadblock to their CI/CD pipeline.

Highlights

• Before: Manual regression testing took at least a full week per project. A team of three QA engineers was at maximum capacity, causing a severe release bottleneck.
• After: AI agents automate repetitive staging regression tests, shrinking QA cycles to just a few days and allowing manual testers to act as AI managers.