Finder: An AI-Driven Framework for Enhanced Pharmaceutical Data Search

The pharmaceutical industry is experiencing a significant shift in data retrieval methods, largely due to advancements in artificial intelligence. Traditional search systems often face challenges when dealing with various content types, including text, images, audio, and video. Finder emerges as a robust AI-driven framework designed to streamline this process. By employing a hybrid vector search approach, Finder integrates both sparse lexical and dense semantic models to enhance retrieval efficiency. Its adaptable architecture allows for the ingestion of multiple data formats while enriching metadata and utilizing a vector-native storage system. This innovative framework facilitates reasoning-aware natural language searches, resulting in improved accuracy and contextual relevance in search results. To date, Finder has successfully processed a vast array of content, including over 291,400 documents, 31,070 videos, and 1,192 audio files across 98 languages. Key techniques such as hybrid fusion, chunking, and metadata-aware routing contribute to its ability to provide intelligent access to information across various sectors, including regulatory, research, and commercial domains.