Challenge

A leading materials manufacturer faced significant inefficiencies in how scientists accessed and analyzed research. Valuable data was scattered across independent internal repositories and external databases. Searching and synthesizing this complex, multi-format scientific data took up to a week per task and required specialized query expertise, significantly slowing down R&D cycles.

Solution

Quantori designed an AI-powered assistant using a Retrieval-Augmented Generation (RAG) architecture on Azure Databricks. The team integrated internal research reports with external databases to process heterogeneous data into vector embeddings for semantic search. AI agents were deployed to retrieve relevant content and generate contextual, structured outputs through a highly secure, natural language chatbot interface.

Outcome

The unified platform reduced patent and scientific data analysis time from days to just minutes. By eliminating manual, multi-system queries, the solution accelerated R&D decision-making, reduced duplicated efforts, and allowed scientists to focus entirely on innovation and high-value research.