5 papers
From Prompt Rules to Golden Data Model: Production-Scale Generation of Personalized, Compliant Pharmaceutical Claims
Suyash Mishra, Qiang Li, Subhabrata Adak +3
Large language models (LLMs) are increasingly used to generate domain-specific commercial & medical content (e.g., claims, statements, and promotional phrases) in regulated industr…
Parameterized Dense-Sparse Fusion for Hybrid Retrieval: Tuning a Rank-Score Mix on BEIR SciFact with Qdrant
Satyanarayan Pati, Srikanth Patil
We study a parameterized hybrid ranker that fuses a dense embedding list and a sparse lexical list. The method has a small, explicit parameter vector: a dense prior , a…
Finder: A Multimodal AI-Powered Search Framework for Pharmaceutical Data Retrieval
Suyash Mishra, Srikanth Patil, Satyanarayan Pati +2
AI is transforming pharmaceutical search, where traditional systems struggle with multimodal content and manual curation. Finder is a scalable AI-powered framework that unifies ret…
Scaling Vision Language Models for Pharmaceutical Long Form Video Reasoning on Industrial GenAI Platform
Suyash Mishra, Qiang Li, Srikanth Patil +2
Vision Language Models (VLMs) have shown strong performance on multimodal reasoning tasks, yet most evaluations focus on short videos and assume unconstrained computational resourc…
Dimension vs. Precision: A Comparative Analysis of Autoencoders and Quantization for Efficient Vector Retrieval on BEIR SciFact
Satyanarayan Pati
Dense retrieval models have become a standard for state-of-the-art information retrieval. However, their high-dimensional, high-precision (float32) vector embeddings create signifi…