Our client, a global pharmaceutical manufacturer needed a streamlined way to extract and analyze insights from healthcare professionals while ensuring regulatory compliance. Their existing processes were manual and resource-intensive, requiring significant effort to categorize insights and generate reports.

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Sales, research, and marketing teams operated in silos, each relying on different data sources. The lack of a centralized system made it difficult to automate data ingestion, classify insights, and provide real-time access to critical information. To overcome these challenges, the company partnered with Compass Analytics to implement a AI-driven solution including automation, predictive modeling, and real-time knowledge retrieval.

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Data Overload

With thousands of HCP reports and continuous feedback, the company struggled to efficiently process and analyze vast amounts of medical data.

Manual Processes & Inefficiencies

Manual data handling required extensive effort to consolidate, categorize, and summarize prescribing trends, slowing insight generation.

Lack of Real-Time Insights

Without automation, critical trends in prescribing behavior and patient perceptions were often identified too late, limiting proactive decision-making.

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Compass Analytics developed an AI-powered system that automated insight extraction, categorization, and knowledge retrieval. By integrating Snowflake for secure storage and Dataiku for advanced analytics, the company established a robust pipeline that streamlined data ingestion and transformation.

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A custom-built LLM chatbot within the Dataiku Answers web app provided real-time access to a knowledge base, ensuring teams could retrieve relevant insights instantly. Insights and topic modeling results from LDA/LLM-driven analysis were automatically labeled using coherence scores to determine optimal topic segmentation. These findings were deployed via a custom Dataiku Plugin developed by Compass and visualized in Power BI.

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By implementing robust data pipelines, real-time knowledge retrieval, and advanced topic modeling, the company transformed its ability to extract, summarize, and analyze medical insights. The integration of Snowflake, Dataiku, and Power BI enabled streamlined operations, improved insight accuracy, and faster decision-making. With a scalable AI solution in place, the company gained enhanced visibility into prescribing behaviors, proactive strategy development, and more efficient cross-team collaboration.

AI-Driven Insight Summarization

Automated summarization of medical insights enabled faster reporting and improved visibility into HCP prescribing behaviors. LLM recipes in Dataiku categorized and summarized insights, reducing manual processing time.

Cognitive Automation for Streamlined Data Processing

Eliminated manual effort in data ingestion, transformation, and topic modeling. Snowflake stored structured and unstructured HCP data, while a custom Dataiku Plugin classified insights using LDA/LLM-driven topic modeling. Analysts and business teams could focus on strategy rather than data processing.

Real-Time Knowledge Access with LLM Chatbots

Sales, marketing, and research teams gained instant access to a medical insights knowledge base. An LLM-based RAG chatbot retrieved relevant insights within the Dataiku Answers web app, enabling seamless integration with Snowflake data. Decision-making accelerated, and cross-team collaboration improved by reducing reliance on manual research and reporting.

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