Finder: A Multimodal AI-Powered Search Framework for Pharmaceutical Data Retrieval
New multimodal AI system processes 31,000+ videos and 1,100+ audio files in 98 languages for drug discovery.
A team of researchers including Suyash Mishra and Satyanarayan Pati has introduced Finder, a novel AI-powered search framework specifically designed for the complex, multimodal world of pharmaceutical data. Published on arXiv, the system addresses a critical bottleneck: traditional search tools struggle with the diverse formats—scientific papers, clinical trial videos, lab audio notes, and molecular images—that define modern drug discovery. Finder's core innovation is a scalable, modular pipeline that ingests these disparate data types, enriches them with metadata, and stores everything in a vector-native backend for unified retrieval.
Finder employs a sophisticated hybrid search approach, combining sparse lexical models (good for keyword matching) with dense semantic models (good for understanding context and meaning). This hybrid fusion, along with techniques like intelligent chunking and metadata-aware routing, allows the system to support "reasoning-aware" natural language queries. A researcher could ask a complex, multi-part question about drug interactions and get precise answers synthesized from text documents, video lectures, and audio recordings. The framework's scale is demonstrated by its processing of over 291,400 documents, 31,070 videos, and 1,192 audio files across 98 languages.
The practical impact is a significant leap in information accessibility for pharmaceutical professionals. By breaking down data silos, Finder can accelerate literature reviews, enhance regulatory compliance checks, and uncover hidden connections in research data. Its architecture is designed for domains where precision and contextual relevance are paramount, moving beyond simple keyword lookup to true semantic understanding of highly technical content. This represents a major step toward AI-augmented intelligence in one of the world's most research-intensive industries.
- Processes 291,400+ documents, 31,070+ videos, and 1,192+ audio files in 98 languages for comprehensive search
- Uses hybrid vector search combining sparse lexical and dense semantic AI models for high-precision retrieval
- Enables reasoning-aware natural language queries across regulatory, research, and commercial pharma domains
Why It Matters
Accelerates drug discovery and regulatory review by allowing scientists to search all research formats—text, video, audio—with a single natural language query.