Doctolib
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Doctolib has 2 source-linked AI deployments documented in AIUseCaseHub, across 1 industry and 1 country.
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Hyperscaler mix
See whether Doctolib's cases are powered by Microsoft, AWS, GCP, or multiple providers.
How Doctolib builds AI
Build / Buy / Compose across this company's documented cases
2 of 2 cases classified (100%) · Compare all use-case types
Use case portfolio
Use case types at Doctolib
Clinical documentation leads with 1 of 2 documented cases; 2 distinct types appear across the visible portfolio.
Reported outcomes
2 cases report measurable results
−50%
Time & speed
median · 1 metric
−20%
Automation & deflection
median · 1 metric
Medians of results published in Doctolib cases, normalized for comparability. See all benchmarks →
Evidence persistence
2 of 2 judgeable cases are still publicly referenced · 1 show the organization expanding AI use.
Durability of public evidence, not whether systems remain in production. How this is measured →
Technology snapshot
What Doctolib uses across visible cases
AI Agents appears in 2 of 2 indexed cases; 6 named technologies are mentioned, led by Azure OpenAI.
Capability mix
Technologies mentioned
All Use Cases (2)
Doctolib transforms consultations with Azure OpenAI Service
Doctolib utilized Azure OpenAI Service to create an AI-powered medical assistant, enabling real-time transcription of consultations. This supports healthcare practitioners by reduc...
Doctolib revolutionizes healthcare customer care with AI-powered RAG system
Doctolib, a leading European e-health company, implemented an advanced AI-powered customer care solution to enhance its support services. Their journey began with deploying Retrieval Augmented Generation (RAG) to power customer FAQs, using GPT-4o through Azure OpenAI Service and OpenSearch vector databases for dynamic knowledge retrieval. The team built robust data pipelines for continuous FAQ embedding and leveraged machine learning classifiers to increase answer precision. An evaluation tool measured key metrics such as context precision, recall, faithfulness, and answer relevancy to optimize the system. With iterative improvements, including prompt engineering and reranking, Doctolib reduced the volume of deflected cases and improved user satisfaction. Key challenges like system latency were addressed through architectural adjustments and model optimization. The article outlines a path toward more sophisticated agentic AI frameworks capable of handling even more complex queries and actions.Limitations of conventional scripted bots were overcome as LLMs (Large Language Models) enhanced response adaptability. The integration of RAG enabled context-aware responses using up-to-date internal documentation. However, the system exposed bottlenecks in handling complex, non-FAQ scenarios, motivating explorations into multi-agent agentic architectures for future expansion.The solution underscores Doctolib’s ongoing development, aiming to further streamline healthcare customer care while providing a scalable and secure support framework that protects user data privacy.
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