Every new support agent meant months of engineering handoffs. Now the people closest to customers build and ship their own.
A rideshare platform managing millions of customer support interactions across riders and drivers, spanning categories from account access and earnings disputes to damage claims and autonomous vehicle support.
Building each AI support agent demanded months of dedicated engineering work, with domain experts who understood customer issues best unable to...
“By leveraging LangGraph to orchestrate a sophisticated multi-agent system, Lyft has transformed its customer support operations, managing millions of interactions for riders and drivers. Our 'self-serve' platform integrates LangGraph's subgraph architecture with LangSmith's robust tracing and monitoring tools, empowering non-technical domain experts to develop and refine AI agents independently.”
On-demand ridesharing, bicycle-sharing, and motorized scooter platform.
Framework and developer platform for building LLM-powered applications.
Lyft's Self-serve agent platform is part of this use case:
Related implementations across industries and use cases
Riders faced 40-minute waits as agents juggled four chats. AI now resolves issues in seconds, freeing humans for sensitive cases.
A legacy bot passed 50% of chats to staff. Now, AI agents connect 62 databases to resolve 87% of inquiries autonomously.
Routine cloud setups tied up engineers for days. Now, developers ask AI agents in Jira and Webex to provision resources instantly.
Fleet repair estimates took up to 48 hours. Autonomous agents now process them in 30 minutes, absorbing a 10x volume surge.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
A new SUV launch generated massive lead surges. AI voice agents handled initial outbound calls, routing high-intent leads to human dealers.
Thousands of software modules slowed vehicle development. Now, engineers collaborate with 64 AI agents to write and debug code.
Manual call evaluations delayed insights by weeks. Now, bots score calls at scale, empowering analysts to synthesize data in hours.
Fragmented support caused frustrating handoffs. Live AI translation now lets a small English-speaking team handle 14 languages instantly.
Every new support agent meant months of engineering handoffs. Now the people closest to customers build and ship their own.
A rideshare platform managing millions of customer support interactions across riders and drivers, spanning categories from account access and earnings disputes to damage claims and autonomous vehicle support.
Building each AI support agent demanded months of dedicated engineering work, with domain experts who understood customer issues best unable to...
“By leveraging LangGraph to orchestrate a sophisticated multi-agent system, Lyft has transformed its customer support operations, managing millions of interactions for riders and drivers. Our 'self-serve' platform integrates LangGraph's subgraph architecture with LangSmith's robust tracing and monitoring tools, empowering non-technical domain experts to develop and refine AI agents independently.”
On-demand ridesharing, bicycle-sharing, and motorized scooter platform.
Framework and developer platform for building LLM-powered applications.
Lyft's Self-serve agent platform is part of this use case:
Related implementations across industries and use cases
Riders faced 40-minute waits as agents juggled four chats. AI now resolves issues in seconds, freeing humans for sensitive cases.
A legacy bot passed 50% of chats to staff. Now, AI agents connect 62 databases to resolve 87% of inquiries autonomously.
Routine cloud setups tied up engineers for days. Now, developers ask AI agents in Jira and Webex to provision resources instantly.
Fleet repair estimates took up to 48 hours. Autonomous agents now process them in 30 minutes, absorbing a 10x volume surge.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
A new SUV launch generated massive lead surges. AI voice agents handled initial outbound calls, routing high-intent leads to human dealers.
Thousands of software modules slowed vehicle development. Now, engineers collaborate with 64 AI agents to write and debug code.
Manual call evaluations delayed insights by weeks. Now, bots score calls at scale, empowering analysts to synthesize data in hours.
Fragmented support caused frustrating handoffs. Live AI translation now lets a small English-speaking team handle 14 languages instantly.