CASE STUDY

AI-driven Chatbot Optimization: Achieving Human-Level Accuracy and Speed in Data Retrieval

Key Highlights

A leading pharmaceutical company needed a no-code solution for querying large-scale multi-modal data. This company faced challenges with fragmented data and limited coding expertise, making data retrieval and analysis time-consuming and complex for non-bioinformaticians.

Elucidata developed an LLM-powered chatbot with natural language query processing, allowing users to query harmonized, multi-modal data without coding. The chatbot used a harmonized, AI-ready knowledge base and RAG system, supported by a user-friendly GUI, to streamline data access and enhance biological context in responses.

This solution led to 5x broader adoption by researchers, real-time query responses, and a $100K annual reduction in infrastructure costs.

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