Abstract
Over the past year, Zoox has invested in integrating Large Language Models (LLMs) into internal developer workflows through a company-wide initiative called Zoox Intelligence (ZI). This talk shares how we approached this transformation — from identifying high-impact use cases (like code authoring, code review, search, onboarding, and context-aware chatbots) to designing infrastructure, choosing the right LLMs, and embedding them into real developer tools (CLI, IDE, Slack, web apps).
Beyond software development tasks, we’re also tailoring our platform to support workflows unique to autonomous vehicle development, such as triaging events observed by our autonomy fleet, and supporting our eventual customers. Our vision is to create a flexible, intelligent platform that supports both technical and non-technical teams — from engineers to program managers, and beyond.
Whether you're just beginning to explore LLMs in your engineering org or scaling your internal AI capabilities, this session will offer tangible ideas, design patterns, and organizational strategies.
Speaker
Amit Navindgi
Staff Software Engineer, Developer Experience @ Zoox, Leading Applied AI Initiatives
Amit Navindgi is a Staff Software Engineer at Zoox, where he leads Zoox Intelligence — an initiative applying Large Language Models (LLMs) across engineering, operations, customer support, and autonomy. He builds products and platforms that combine technical depth with thoughtful design, creating interactions that are both intuitive to use and elegant to build. His expertise spans Applied AI, Observability, Semantic Search, Experimentation Platforms, Data Engineering and incident and on-call management tools.
He also runs the Zoox AI Hackathon and The Assembly, a cross-functional forum for knowledge sharing, collaboration, and innovation.
Earlier in his career, he developed web applications and distributed systems at Veritas Technologies and focused on Natural Language Processing at the Xerox Research Centre Europe
