The challenges of traditional mobile testing at scale (3,000+ simultaneous experiments) Architecture and implementation of DragonCrawl using MPNet and embedding techniques Real-world examples of DragonCrawl's adaptive behavior and problem-solving capabilities Practical strategies for handling LLM challenges like hallucinations and adversarial cases Results and metrics from production deployment Live demonstration of DragonCrawl in action

We'll explore the technical details of model selection, embedding evaluation, and the specific guardrails implemented to ensure reliable testing. Attendees will see how we achieved 99%+ stability in production while eliminating maintenance overhead.

Anam Hira is a Machine Learning researcher and co-founder of Revyl.ai, pioneering automated mobile testing powered by LLMs. As a key contributor to Uber's DragonCrawl project, he helped develop an innovative testing framework that caught 11 P0 bugs and delivered $25M in savings within just four months. Building on DragonCrawl's success, Anam founded Revyl.ai to bring AI-powered testing to enterprises worldwide. His work spans the intersection of machine learning, mobile development, and quality assurance, with a focus on making sophisticated testing technology accessible to development teams of all sizes