Systems | Development | Analytics | API | Testing

Across industries, AI systems are being scrutinized under new laws that demand proof of fairness, transparency, and human oversight. Hemraj Bedassee , Delivery Excellence Practitioner,

2025 will be remembered as the year AI wrote poetry, passed medical exams, and stumbled with spectacular blunders. For every impressive breakthrough, there was an equally impressive facepalm.

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..

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.

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 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.

When it comes to accessibility, there are a lot of vendors and each is unique and credible in its own way.

We put two of the most talked-about models head-to-head in a real-world RAG scenario, and the results might surprise you. Hemraj Bedassee , Delivery Excellence Practitioner,

For many engineering leads and executives, reviewing quality assurance dashboards and automation reports may feel like trying to solve a complex puzzle, one where the pieces keep changing, and the full picture only comes into focus after it’s too late.

AI is no longer just a technical feature, it is a business-critical system that shapes conversations, decisions, and customer experiences.