Modern IT organizations produce vast amounts of data, yet few leaders have real-time visibility into where development gets stuck or which teams are excelling. MetrX bridges this visibility gap—gathering development, testing, project, and code quality data in one place and analyzing them in context. The platform not only provides an accurate snapshot but also forecasts potential risks, allowing leaders to intervene before problems arise. This type of transparency and predictive analytics creates direct business value: faster, more stable development processes and measurable performance improvements.

It is common to encounter critical bugs shortly after a release, even when test coverage appears high. The issue often lies not in insufficient testing but in inadequate coverage of risky code areas. TestNavigator provides real-time coverage tracking, visual analysis tools, and code-level transparency to reveal what was really tested. With its objective Go/No-Go decision support and AI-based test case prioritization, the most critical areas always stay in focus. This enables teams to deliver release-ready versions faster, more reliably, and with greater predictability.

AI has now become one of the most important business tools in the financial sector, offering both opportunities and challenges in efficiency, risk management, and compliance. The joint AI Business Breakfast by FRISK, FEA AI Solutions, and FrontEndART showcased how Hungarian organizations apply AI through real-life projects. The professional talks approached regulation, customer service, and data analysis from a practical perspective. The roundtable discussion brought honest insights on measuring AI’s value, overcoming early hurdles, and the leadership decisions needed for success. The event’s main message: AI is now a strategic factor that provides long-term competitive advantage for those who integrate it deliberately and early.

In modern software development, efficiently allocating testing resources is key, as thousands of test cases often await execution near the end of projects. AI-based test case prioritization offers a breakthrough by helping determine, through data-driven methods, which tests provide the most value to quality assurance. For example, the TestNavigator Test Advisor Score ranks tests based on code changes, complexity, and past defect statistics, allowing teams to focus on critical areas. As a result, test time may be reduced by up to 60–70% while maintaining software quality. AI thus supports rather than replaces testers—enabling faster, more objective, and risk-based decision-making.