Revin
Developed AI agents at a $10M early-stage startup, automating front-office workflows for home service businesses. This was the experience where I truly learned how to work with AI models and context—seeing firsthand how context engineering affects model behavior with real-world users. The core lesson: make the agent's life as easy as possible so it can make the right decisions.
AI Agent Performance Optimization
Boosted booking rates from 16% to 46% across 18,667 conversations, generating an additional 4,257 bookings by re-engineering our AI-powered SMS scheduling agent for Sears. This performance improvement outperformed the conversion rate of live human agents, demonstrating the effectiveness of well-designed AI systems.
Security & Authentication
Identified a severe login bypass vulnerability in local storage and integrated Supabase Auth to secure authentication. This security improvement protected user data and ensured proper access control across the application.
Analytics & Monitoring
Implemented fail-reason and intervention-needed tagging for our agent dashboards, improving analytics and timely escalation. This enhancement provided better visibility into agent performance and enabled proactive support when needed.
Infrastructure & Scalability
Led efforts to consolidate client-specific repos and built an extensive logging system, enhancing scalability and sustainability. This infrastructure work improved code maintainability and provided better observability for the engineering team.
Reflection
Revin shaped how I think about building AI systems at scale. From optimizing AI agent performance to securing authentication systems, I gained valuable insights into what it takes to build and maintain software in a fast-paced startup environment.
Seeing the direct impact of my work through improved booking rates and enhanced security was rewarding—and it showed me firsthand the power of AI to automate tedious manual work, freeing people to focus on what actually matters.