Systems | Development | Analytics | API | Testing

As organizations increasingly rely on AI to power their products and services, addressing bias is now a critical responsibility for quality and engineering leaders.

With constant releases, testing on multiple devices, and users scattered across the globe, internal teams alone can fall short. To address these issues, companies are increasingly resorting to crowdsourced testing.

With the EU AI Act now in force, compliance is no longer about aspirational ethics or last-minute checklists, it’s about operationalizing quality assurance at every stage of your AI lifecycle.

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.

Outsourcing quality assurance (QA) used to be about saving money. In 2025, you will have to build resilience into the release pipeline.

Test automation is no longer a nice-to-have—it’s a baseline expectation for any team serious about delivering high-quality software at scale. Paulo Feitosa , Sr. Manager, Automation Solutions,

Engineering teams now move faster than ever. In 2025, they build smarter, ship quicker, and rely heavily on AI tools and continuous delivery pipelines.

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

We are thrilled to share that Testlio has been awarded the ISO/IEC 27001 certification for our information security management system.

Testing AI-powered applications is one of the top priorities for teams deploying machine learning systems at scale. As AI becomes embedded in everyday tools, its behavior becomes harder to control.