AI-driven Modernization for Pharmaceutical Software Provider

As a result, we executed seven core features that delivered substantial improvements to the client’s software:

1. AI summarization tool: Automates the summarization of complex metadata, such as descriptions, product names, codes, dates, and PDF and DOCX files, enabling faster understanding and analysis for support teams.

2. Multiple acceptance criteria field: Streamlines complaint validation by automatically assessing input against predefined criteria. This provides a “valid” flag, clear reasoning for the assessment, and actionable recommendations for improvement, reducing manual review time and ensuring data quality.

3. Automated email template generation: Improves response times and consistency in customer communication. AI extracts key information (e.g., name, symptoms) from incoming complaints and seamlessly generates pre-defined email templates, providing ready-to-send responses in a two-step process.

4. Severity-based issue classification: Conducts a robust risk assessment by providing AI-driven recommendations for complaint severity (e.g., critical, major, minor) based on historical data and event descriptions. This allows for quicker prioritization and allocation of resources, leading to more efficient complaint management.

5. Audit report generation: Generates tailored audit reports by comparing user input against static regulatory guidance. Users can specify key topics and desired report structure, giving them control and flexibility while ensuring compliance.

6. Document change summarization: Semantically analyzes documents, identifies and tags all changes, and generates a short, concise summary of the main modifications. This significantly reduces the time and effort required to review lengthy documents (e.g., 200–300 pages) from hours to mere seconds, improving efficiency and accuracy in critical document management.

7. Manufacturer and user facility device experience (MAUDE) code classification model: Automates the critical task of categorizing medical device event descriptions into specific FDA codes, which significantly streamlines compliance and reduces manual classification errors. Leveraging GPT-4o for robust inference and an advanced text-embedding-3-large model, the feature employs custom algorithms to effectively manage data imbalances and achieve high accuracy.