Systems | Development | Analytics | API | Testing

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.

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.

Android test automation is the process of using software tools and scripts to execute tests on Android applications automatically.

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.