Accurately categorize unstructured healthcare asset descriptions.
Case Study — Financial Services
Driving Efficiency in Pharma Product Management with an AI-Powered Category Classification Solution
- ML Classification
- NLP & OCR
- Healthcare & Pharma
- Regulatory Compliance
Overview of the Client
Who we built this for
A leading global organization in the healthcare and pharmaceutical sector needed a scalable solution to classify thousands of unstructured product descriptions—spanning medical devices, pharmaceuticals, consumables, and supplements—into accurate, predefined categories. This classification was critical to support valuation processes, regulatory compliance, and streamlined operational workflows.
What Client Needed
What the client needed to achieve
To automatically classify unstructured healthcare product descriptions to reduce manual sorting, improve downstream analytics and compliance.
Reduce manual effort and increase throughput
Provide consistent tagging for improved reporting, valuation, and informed decision-making
Business Challenge
Where this build got difficult
The client faced a major bottleneck in managing and categorizing large volumes of product data stored in spreadsheets. The descriptions were highly varied, unstandardized, and included a mix of technical, commercial, and medical terminology. Manually processing such a vast dataset was not only time-consuming but also prone to errors and inconsistencies, significantly impacting data reliability and operational efficiency. Key issues included:
Unstructured and Ambiguous Data
Product descriptions lacked consistent language and formatting, complicating manual classification.
High Dependency on Manual Sorting
Human classification was slow, error-prone, and resource-intensive.
Need for Scalable Automation
The client sought to automate classification across multiple asset types with high accuracy to support downstream analytics.
What We Built
What we actually built
Model Training & Evaluation
- Applied and benchmarked Support Vector Machine (SVM) and Random Forest classifiers using labeled historical data.
Natural Language Processing (NLP)
- Used NLP models to process, clean, tokenize, and vectorize medical and commercial terms for model training.
OCR Integration
- Incorporated EasyOCR and Pytesseract for processing image-based product records and extending coverage beyond text-based data.
Web Interface for Review & Export
- Delivered a simple, intuitive, and highly interactive web application enabling data upload, classification visualization, manual review, and export.
Tech Stack
Core tech stacks we implemented
ML Algorithms for Classification
Text Preprocessing
OCR Integration for Image-Based Data
Web Application for Review and Export
Business Impact
What the solution changed
Classification Accuracy
Achieved 89% model accuracy across pharmaceutical data
Operational Efficiency
Reduced manual sorting effort by over 70%
Faster Data Processing
Enabled large-scale classification in minutes
Improved Decision-Making
Delivered clean, categorized data for reporting and analytics
Turning Tech Challenges into Solutions!
The solution enabled rapid scaling of product data management across departments and set the foundation for broader AI adoption. By automating the categorization of complex pharmaceutical product descriptions, DRC Systems significantly streamlined operational workflows, minimized manual effort, and enhanced the accuracy and accessibility of enterprise data across departments. The AI-powered accurate classification also helped the client support regulatory and internal compliance efforts.
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