Management Summary
The solution makes internal research data, patents, external literature, and other knowledge sources accessible via natural-language queries. The RAG chatbot identifies relevant content from texts, tables, and images. Subject matter experts receive generated responses with references to the underlying sources.
Project summary
Global chemical company
Industry: Chemical Industry
Project Start: 04/2023
Focus: Generative & Agentic AI
Use Case: Enterprise Search & Knowledge Management
Project Goal
Address complex research questions via natural-language queries on large research datasets
Key Core Functions
- Natural-language search across internal research data, patents, external literature, and other sources
- RAG-based identification of relevant content from text, tables, and images
- Generation of answers with source references to provide a subject-matter context for the results
Tech Stack
The Starting Point
The global chemical company had extensive research databases that had grown over the years. Subject matter experts had to find relevant information from internal research data, patents, external literature, and other sources, and contextualize it within their field.
Traditional search approaches were insufficient for many complex research questions. The queries were specific, context-dependent, and often could not be addressed using individual search terms. What was needed was an AI-powered enterprise search solution capable of processing natural language and making large datasets accessible for research.
Our Strengths, Your Benefits
RAG for Research Data
HMS combines AI engineering, data architecture, and implementation experience in complex enterprise environments. In this project, the focus was on making large research datasets accessible via natural language and verifiable by subject matter experts.
What You Can Expect from HMS
HMS helps you assess AI-powered search solutions from a business perspective, implement them technically, and integrate them into existing data and search environments.
- Implementation of a RAG-based chatbot system for large research datasets
- Integration of internal and external knowledge sources
- Expansion of prototypes into cross-source solutions
