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AI-Powered Data Curation

Introduction

A leading pharmaceutical research firm needed to track, analyze, and manage vast amounts of drug-related press releases published daily across multiple sources. The existing manual approach — where analysts reviewed and categorized data — was slow, inconsistent, and error-prone, often resulting in critical insights being missed.  

These delays affected competitive intelligence, market trend analysis, and timely decision-making for ongoing drug development programs. The company sought an AI-driven solution that could automate data extraction, classification, and curation with high accuracy and minimal human intervention. 

Challenges

The client’s team faced a series of pressing operational and data management challenges:

 

  • Information Overload: Analysts had to manually scan thousands of press releases every week, leading to missed updates on drugs, diseases, and clinical trials. 
  • Low Accuracy: Manual tagging resulted in up to 30% inconsistency across reports, weakening research reliability. 
  • Time-Intensive Processes: Each press release required manual review, extraction, and categorization — taking 10–15 minutes per record. 
  • Lack of Scalability: As data volume grew, human teams couldn’t keep up with the increasing inflow of new information. 
  • Data Fragmentation: Insights were scattered across formats and systems, making it difficult to generate unified intelligence or actionable reports. 

The company required a smart, automated system to classify drug-related data with precision, eliminate human bottlenecks, and enable faster, data-backed decision-making.

Our Solution

Our team developed a custom AI-Powered Data Curation Platform designed to automate the entire drug intelligence pipeline. 

Key components included:

 

  • Automated Data Extraction: Leveraged AI, OCR (Tesseract), and Natural Language Processing (NLP) to identify and extract key entities such as drugs, diseases, and organizations from unstructured press releases. 
  • LLM-Based Classification: Integrated OpenAI’s Large Language Model to intelligently classify and validate extracted information, ensuring near-human accuracy. 
  • Interactive Review Platform: Built a Django-React web application for analysts to review, validate, and edit extracted data through an intuitive interface. 
  • Scalable Data Management: Implemented PostgreSQL and SQLite databases for high-speed data storage, retrieval, and audit tracking. 
  • Workflow Automation: Streamlined approval and publishing workflows, reducing dependency on manual oversight. 

This solution created a centralized, automated, and scalable framework for managing drug-related data efficiently and accurately. 

Results
  • 99% data accuracy achieved in information classification and extraction. 
  • 85% reduction in manual processing time, cutting average review time from 15 minutes to under 2 minutes per record. 
  • 3x scalability increase, allowing the platform to process over 10,000 press releases per month without added manpower. 
  • 100% consistency in drug, disease, and organization tagging across all curated datasets. 
  • Significant improvement in data-driven decision-making, enabling faster competitor tracking and research validation.

By combining AI, NLP, and automation, the platform transformed how the client manages pharmaceutical intelligence — improving accuracy, speed, and scalability, while freeing analysts to focus on higher-value insights.

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