Case File: ai-pharma-forecasting#Agentic AI#RAG

AI-Powered Pharma Forecasting Tool

Client

SAIKOU

Pharmaceutical Analytics Firm

Primary Objective

Client had vast amounts of historical trial data but no way to leverage it effectively. Manual forecasting took 2 people over 3 days per cycle.

Impact Protocol

Reduced manual effort by 90% (saving 48+ man-hours per cycle). Significantly improved forecast accuracy by identifying hidden patterns in historical data.

The Challenge

SAIKOU, a leader in pharmaceutical analytics, was sitting on a goldmine of historical clinical trial data. However, this data was unstructured and scattered across various legacy systems. Their analysts were spending days manually searching for comparable trials to forecast outcomes for new drugs. This manual process was slow, prone to human error, and unable to scale with their growing pipeline.

Our Approach

We architected a bespoke RAG (Retrieval-Augmented Generation) solution. By vectorizing their entire historical dataset and storing it in a high-dimensional vector database, we enabled semantic search capabilities. We then integrated a fine-tuned Large Language Model (LLM) to act as an intelligent analyst. The system can now ingest a new trial protocol, instantly retrieve the vast set of analogous historical trials, and generate a probabilistic forecast report with citations.

The Results

Reduced forecasting cycle from 3 days to 4 hours.

Identified key risk factors missed by human analysts in 15% of back-tested cases.

Freed up senior analysts to focus on strategy rather than data drudgery.

Seamlessly integrated into their existing secure intranet.

Tech Stack

Python

LangChain

OpenAI GPT-4

Pinecone Vector DB

React

FastAPI

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