Perspectives

Delivering analytics quality at a global scale is never easy. One broken event or missed signal can derail product launches, fuel bad decisions, and shatter customer trust overnight.

If you are building or scaling digital products, chances are your QA process already includes gig testers. You post a task, someone across the globe picks it up, files a bug, and moves on. 

AI failures rarely look like crashes. More often, they appear as confident but incorrect answers, subtle bias, or culturally inappropriate responses.

AI doesn’t just fail with bugs. It fails in silence, in bias, and in behavior. That’s why traditional QA won’t cut it anymore.

As software systems get increasingly complex every day, the challenges of effective testing also escalate. Software testing involves handling large datasets, complex workflows, and shorter release cycles. 

Smarter AI starts long before the model is trained. It begins with the quality of the data you feed it. Data that reflects real-world nuance, cultural context, and human behavior is what sets strong systems apart.

Large language models are under threat from a tactic called LLM grooming, where bad actors flood public data sources with biased or misleading content to influence AI training behind the scenes.

Smartphone users worldwide are expected to reach 7.7 billion by 2028. With such an enormous and growing user base, non-functional requirements like performance, security, reliability, and usability can make or break businesses.

In quality engineering, axioms are foundational truths drawn from years of practice that underpin effective, scalable automation systems.