Advice

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.

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.

Chatbots have quickly moved from novelty to necessity. With over 987 million users and platforms like ChatGPT receiving more than 4.6 billion monthly visits, chatbots are now core to how people interact with digital products.

Remote work has unlocked a world of opportunity for career-motivated individuals. At Testlio, we meet talent from every corner of the globe, enabling us to hire the strongest candidates in the world.

You’ve translated the app and maybe even hired native speakers. It passes all your internal checks, but users in new markets are still dropping. The problem often isn’t obvious.

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.