The RAG system significantly accelerates product development by automating repetitive tasks, ensuring consistent compliance with regulatory guidelines, and enabling Medochemie to scale its operations efficiently as document volume increases. More importantly, it allows the company’s resources to focus on strategic and innovative work, shifting attention from tedious documentation to high-impact research and development.
The proposed implementation of this system follows a structured approach. It begins with the collection of external data related to active pharmaceutical ingredients (APIs), ensuring all information was machine-readable. This is followed by a systematic process of data splitting and indexing to facilitate efficient retrieval. The final steps involve integrating the system with a Large Language Model (LLM) using frameworks like LangChain, and thorough testing and validation to ensure accuracy and limit hallucinations.
Medochemie – Consultation services
Medochemie Ltd., is a global leader in the production and distribution of generic pharmaceutical products. When it comes to product development, it faces a common but significant challenge: the time-consuming literature review process involved in product development.
To overcome this hurdle, Medochemie turned to AI-driven solutions, and our research institute proposed a cutting-edge Retrieval-Augmented Generation (RAG) system. This advanced technology allows Medochemie to automate the otherwise laborious task of processing vast volumes of documents and extracting critical information. By importing all relevant literature into a RAG system, Medochemie can now automatically generate accurate answers to the standardised questions required for each phase of product development.