Doctolib improves customer care case deflection by 20% with a RAG system built on GPT-4o and OpenSearch
Doctolib's customer care relied on rules-based bots that could not adapt to users or their context. Standard LLMs lacked access to private and recent data, limiting accuracy. A RAG system was pursued to address this gap but initially suffered from insufficient results and latency exceeding one minute.
The initial vanilla RAG implementation did not produce good enough results on its own, and response latency reached up to one minute, making the system impractical for real users.
The improved RAG system achieved a 20% reduction in customer care cases reaching agents and reduced response latency from one minute to less than five seconds, enabling customer care agents to focus on more complex cases.
Frequently asked questions
What did this team achieve with this AI workflow?
The improved RAG system achieved a 20% reduction in customer care cases reaching agents and reduced response latency from one minute to less than five seconds, enabling customer care agents to focus on more complex ca…
What tools did this team use?
GPT-4o, Azure OpenAI service, OpenSearch, Ragas.
What results were reported?
Customer care cases deflection: 20%; Response latency: from a latency of 1 min to less than 5s (source-reported, not independently verified).
What failed first in this deployment?
The initial vanilla RAG implementation did not produce good enough results on its own, and response latency reached up to one minute, making the system impractical for real users.
How is this customer support AI workflow structured?
User query submitted → ML classifier gates request → FAQ document retrieval → Re-ranking applied → LLM answer generation → Customer care case deflected.