How InpharmD Uses Pinecone & RAG to Boost Clinical Query Accuracy by 70%
InpharmD's AI assistant, Sherlock, leverages Pinecone's vector database to deliver fast, accurate drug information to healthcare professionals. By embedding 30 million medical documents into a RAG pipeline, InpharmD achieved 70% better query accuracy, 95x faster first response times, and 80% cost savings on data storage.
Impact
80%
Data Storage Cost Savings
4x faster
Query Response Time Improvement
95x faster
First Response Time (FRT) Improvement
75%
Overall Response Time Reduction
70%
Query Accuracy Improvement
30 million
Medical Documents in Knowledge Base
2 billion+
Vectors Indexed Simultaneously
~40 billion
Planned Vector Scale
Challenge
Healthcare professionals face slow, imprecise access to medical literature due to the enormous volume of documents, unreliable sources, and the need for real-time updates — making timely clinical decision-making difficult. InpharmD needed a scalable vector database to power fast, accurate retrieval from a 30-million-document knowledge base.
Solution
InpharmD built Sherlock, an AI assistant powered by Pinecone's vector database and the Canopy RAG framework, embedding 30 million medical documents as 1,536-dimensional vectors to enable semantic similarity search and context-aware drug information retrieval for healthcare professionals.
Tools & Technologies
AWSWhat Leaders Say
“In 2021, the landscape was different. We envisioned a platform that could not only understand the nuances of clinical inquiries but also respond with tailored, evidence-based information. Pinecone was a game-changer for us as it allowed us to process vast amounts of medical literature with unprecedented speed and accuracy.”
“Pinecone is integral to our data-driven operations. Its seamless scalability, rapid query results, and impressive low latency make it an indispensable asset in enhancing efficiency and productivity.”
“In healthcare, time is often a critical factor. By leveraging Pinecone's capabilities, we've not only accelerated the information retrieval process but also reduced the time clinicians spend on navigating complex literature. This not only translates to time savings but also contributes to more efficient patient care.”
“Our vision is to empower clinicians with unparalleled access to actionable drug information. Pinecone has been instrumental in realizing this vision, and we're committed to pushing the boundaries of what technology can achieve in healthcare.”
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Full Story
InpharmD is a digital health platform built around a simple but ambitious premise: healthcare professionals deserve instant, evidence-based answers to complex clinical questions. In a landscape where clinicians are pressed for time and the volume of medical literature is overwhelming, InpharmD set out to bridge the gap between raw research and actionable drug information. In 2021, CTO and Co-founder Tulasee Rao Chintha made a pivotal strategic decision to build InpharmD's AI capabilities on top of vector database technology — a move that would define the company's competitive trajectory in digital healthcare.
The core challenge InpharmD faced was the sheer complexity and scale of medical data. Accurate, real-time clinical information is essential for patient safety, treatment efficacy, and regulatory compliance — yet searching the medical literature is notoriously slow and imprecise. With millions of documents to sift through, unreliable sources, and the constant need for up-to-date information, healthcare professionals were losing valuable time. InpharmD needed a way to make its 30-million-document knowledge base not just searchable, but intelligently queryable at scale and with minimal latency.
To solve this, InpharmD developed Sherlock, an AI assistant that combines large language models, human pharmacy expertise, and retrieval-augmented generation (RAG) to answer clinical drug inquiries. After evaluating multiple vector database options, the team selected Pinecone as its core infrastructure partner. Using Pinecone's open-source RAG framework, Canopy, InpharmD processed its entire library of medical PDFs — extracting text, chunking documents, and embedding them as 1,536-dimensional vectors stored in Pinecone alongside rich metadata. This gave Sherlock a long-term semantic memory capable of understanding the nuanced context of clinical questions, not just keyword matches.
The Sherlock workflow operates in four stages: a clinician submits a question, Sherlock translates it into vector embeddings and runs a similarity search in Pinecone via Canopy, the system refines its response through reinforcement learning and human feedback, and finally the InpharmD pharmacy team reviews the output before delivering it to the clinician. This human-in-the-loop approach, powered by Pinecone's low-latency retrieval across over 2 billion vectors, ensures both speed and clinical reliability.
The results have been transformative. InpharmD realized approximately 80% savings in data storage costs, a 75% reduction in overall response time, and a staggering 95x improvement in first response time. Most critically, query accuracy improved by 70%, giving clinicians far greater confidence in the information they receive. With plans to scale their vector index to approximately 40 billion vectors, InpharmD is positioned to continue expanding its medical knowledge base while maintaining the speed and precision that evidence-based patient care demands.