
Shows how Lyft, Vodafone, and LATAM Airlines structure customer-facing systems in production, including self-serve agent platforms, frontline copilots, and using to align engineers with domain experts on agent behavior.
“LATAM Airlines saw this with Concierge. Initially, 13% of messages were classified as out of scope. After reviewing the conversations, the team found that 95% were legitimate passenger needs the agent had not yet been designed to handle, including check-in and baggage questions. Adding a customer-care specialist reduced the out-of-scope rate from 13% to 1%.”
LangChain
“Lyft facilitates 79 million trips each month, while AI Assist handles roughly 270,000 monthly interactions across seven or more production agents. The system has achieved a 65% deflection rate and a 35% AI resolution rate.”
LangChain
“This approach reduced the time required to develop an agent from roughly six months for Lyft’s first driver agent to about two weeks for a new configurable agent.”
LangChain
“Lyft’s first LLM-generated customers were too articulate, patient, and cooperative, producing offline pass rates above 90% that did not reflect production behavior. Real users often write in fragments, omit context, repeat themselves, or arrive with a specific goal such as securing a refund or bypassing the agent.”
LangChain
“Super TOBi is the agentic evolution of Fastweb + Vodafone’s existing chatbot. It now serves nearly 9.5 million customers across the Customer Companion App and voice channels, handling use cases such as cost control, active offers, roaming, sales, and billing.”
LangChain
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