β Tackled the problem of slow, manual vehicle diagnosis and repair workflows, reorienting the platform toward accelerated AI-driven diagnosis and reduced repair time to improve service-center efficiency and scalability.
β Owned the end-to-end redesign of the diagnosis workflow, enabling ML predictions from minimal inputs, eliminating vehicle log file dependency, and driving 20% DAU growth via faster AI-powered diagnosis.
β Designed and developed both high and low-level designs for a real-time analytics dashboard, aggregating metrics from four systems into service-center leaderboards, resulting in a 30% increase in user engagement.
β Led the transition from a single-tenant to a multi-tenant architecture, enabling multi-brand expansion, new enterprise deployments, and $XXXK+ in ARR leading up to the Series A funding round within a year.
β Built these platforms using a full-stack architecture including Node.js, React/Next.js, GraphQL, REST APIs, PostgreSQL, and AWS Lambda, selecting technologies based on scalability, latency, and AI-integration requirements.