Case Study: InPharmD achieves 70% better query accuracy with Pinecone

InpharmD Redefines Evidence-Based Healthcare with Pinecone

InpharmD, a digital health platform, needed to overcome the challenge of providing healthcare professionals with fast and accurate answers to complex clinical questions. Navigating a vast repository of 30 million medical documents was time-consuming and imprecise. To build its AI assistant, Sherlock, the company required a production-ready vector database capable of understanding the nuanced context of medical information with minimal latency.

The solution was implemented using Pinecone, which serves as the core vector database for Sherlock. Pinecone stores and processes vector embeddings, enabling efficient similarity searches that retrieve the most relevant medical information for each query. This integration resulted in a 70% improvement in query accuracy, a 75% reduction in response time, and 80% savings in data storage costs, allowing InpharmD to deliver evidence-based care more effectively.

InPharmD

Tulasee Rao Chintha

CTO, and Co-founder