The Quality Beat
The Quality Beat
Podcast Description
Where software quality lives, breathes—and keeps evolving.
The Quality Beat is a podcast for tech leaders, engineers, and quality minds who care about building better software—from strategy to delivery. In each episode, we tune into real conversations around what drives quality at scale: test efficiency, AI in QA, accessibility, SAP testing, end-to-end engineering, and everything in between.
Hosted by Nagarro and joined by voices across the industry, we go deep on what drives modern software quality without losing the heartbeat of what makes teams and tech work together. Because quality isn’t static. It lives, breathes—and keeps evolving.
Podcast Insights
Content Themes
The podcast focuses on themes such as test efficiency, AI in QA, accessibility, SAP testing, and end-to-end engineering. For example, episodes cover the integration of AI in improving quality processes, sustainable engineering practices, and ways to enhance team collaboration and engagement in quality assurance activities.
Where software quality lives, breathes—and keeps evolving.
The Quality Beat is a podcast for tech leaders, engineers, and quality minds who care about building better software—from strategy to delivery. In each episode, we tune into real conversations around what drives quality at scale: test efficiency, AI in QA, accessibility, SAP testing, end-to-end engineering, and everything in between.
Hosted by Nagarro and joined by voices across the industry, we go deep on what drives modern software quality without losing the heartbeat of what makes teams and tech work together. Because quality isn’t static. It lives, breathes—and keeps evolving.
The “best” AI model is not always the right one. For QA teams, the better question is: how much intelligence does this testing task actually require?
In this episode, Neeraj Jain and Nikita Mittal unpack a practical 3-tier framework for AI model selection across the Software Testing Life Cycle (STLC), and discuss the factors teams should weigh before choosing: complexity, quality, latency, cost, and security/compliance.
Because smarter AI adoption starts with choosing the right model for the right job.
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