In this episode of Tech. Entrepreneurs. Nebraska., Shohina Sultonova visits with Archana Raghu, Quality Lead at Quantum Workplace, where she leads both traditional and AI quality, partnering with data science teams on AI evaluation and testing.

Her work focuses on building the systems and frameworks that make software reliable, fair, and trustworthy in production.

Everyone is focused on speed. Fewer are focused on responsibility.

Archana breaks down what it actually means to test AI in the real world and why failures don’t always show up loudly. Sometimes they show up quietly through bias, flawed data, and unreliable outputs that impact real people and real decisions.

We also talk about her passion beyond the workplace.

She is deeply committed to developing the next generation of tech talent through her work at the University of Nebraska Omaha, Girls Who Code, CodeCrush iSTEM, and mentoring students in the NSF-funded PRIME Scholars program and UNO tech seminar course.

In this episode:
• The difference between traditional and AI testing
• Why AI failures are harder to detect
• The gap between building fast and building responsibly
• What it takes to build trust in AI
• How students and professionals can stay relevant

If you care about where AI is going, this conversation will challenge how you think about quality, speed, and accountability.