Is there a formula or approach to estimate how much you should spend on your quality engineering (QE) efforts? Our CEO, Steve Semelsberger, is frequently asked that question in discussions…
AI systems change faster than traditional QA models can react, which means quality risks now emerge in real time rather than at release.
AI is evolving faster than the guardrails meant to validate it, leaving organizations exposed to compliance risk, model drift, opaque decision paths, and breakdowns in trust.
Two weeks ago, our team gathered in Mexico City for our annual company offsite, LionFest, where we celebrated all the wins and milestones we achieved this year.Â
AI testing careers are shifting in ways that most people in QA are not fully prepared for, and the changes are creating opportunities that did not exist even a few years ago.
AI doesn’t just learn from data, it learns from us, and we are far from perfect. When it scrapes the internet for knowledge, it also absorbs our biases, blind spots, and noise, shaping how it interprets the world.
For years, QA practices were designed for predictable, rules-based software. AI has upended that reality by introducing risks that traditional methods cannot fully address.
The goal of any mobile product is to create an app experience that’s innovative and new. But you must accomplish specific, necessary steps between crafting a clear vision for your…
Did you know the global digital health market is on track to surpass $660 billion by the end of 2025? That’s no surprise, considering how healthcare apps have become indispensable in our daily lives.Â
Mobile application testing ensures the functional and non-functional quality of mobile application workflows. As more users rely on smartphones for daily tasks, expectations for performance and reliability continue to rise.
Across industries, AI systems are being scrutinized under new laws that demand proof of fairness, transparency, and human oversight.
If you have ever searched for a crowdsourced testing partner, you have probably seen the same promise repeated: “thousands of devices, hundreds of geographies.” While impressive at first glance, these vanity metrics rarely reflect the true quality of a QA partnership.
The promise of AI breaks down when testing focuses only on idealized inputs. Real users ask incomplete questions, switch languages mid-thread, or provide contradictory details that models must still handle.