Virtual Voice Assistant Platform
A major technology company was building a virtual voice assistant that needed to work across both cloud-based and on-device AI services. The on-device requirement was driven by privacy and security constraints — some users' data couldn't leave their devices. The challenge was building quality systems that could validate behavior across two fundamentally different execution environments.
Testing and validating AI behavior is genuinely hard. You can't just write a unit test for 'does this sound natural?' Ensuring quality across cloud and on-device ML models, under strict security constraints, with executive stakeholders demanding visibility into risk — required building new kinds of testing infrastructure and quality thinking.
We built automated QA systems that could evaluate AI responses across cloud and on-device services simultaneously, surface quality deltas, and generate executive-legible dashboards that translated technical quality signals into business risk. The on-device NLP work required close collaboration with ML engineers to build test harnesses that didn't compromise the privacy model.
- — Automated QA infrastructure spanning cloud and on-device AI service integration
- — On-device NLP/ML capability delivered within strict privacy and security constraints
- — Executive visibility dashboards translating AI quality metrics into operational risk signals
- — Cross-functional integration framework adopted across multiple product teams