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AI-Powered Regulatory Compliance Platform Enhancement
  1. case
  2. AI-Powered Regulatory Compliance Platform Enhancement

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AI-Powered Regulatory Compliance Platform Enhancement

codedistrict.com
Information technology

Challenges in Pharmaceutical Regulatory Compliance

Pharmasift, a pharmaceutical regulatory compliance company, faced significant challenges related to manual review of promotional materials and regulatory research. These challenges resulted in lengthy review times, inefficient research processes, scalability issues with increasing regulatory data volumes, and inconsistent compliance feedback, increasing operational overhead and potential compliance risks.

About the Client

Pharmasift is a leading provider of pharmaceutical regulatory compliance solutions, specializing in automating and streamlining processes related to drug promotion and regulatory guidelines.

Project Goals

  • Reduce the time required for regulatory document review by 80%.
  • Accelerate regulatory research and information retrieval by 90%.
  • Improve the accuracy of compliance assessments by leveraging AI-driven analysis.
  • Enhance analyst productivity by automating repetitive tasks.
  • Ensure scalability to accommodate future growth in regulatory data and evolving guidelines.
  • Maintain consistent compliance decision-making across all reviewed materials.

System Functionality

  • Automated review of pharmaceutical promotional materials against regulatory guidelines.
  • Instantaneous search and retrieval of regulatory documents (CFRs, enforcement letters, etc.).
  • AI-driven feedback and references to past enforcement actions.
  • Hybrid search model combining SQL and semantic search.
  • Retrieval-Augmented Generation (RAG) for accurate and contextually relevant responses.
  • Scalable architecture to handle large volumes of data.
  • User-friendly interface for analysts to validate AI-generated feedback.

Technology Stack

Generative AI
Retrieval Augmented Generation (RAG)
Hybrid Search Models (SQL & Semantic Search)
Large Language Models (LLMs)
Vector Databases

External Integrations

  • Existing Pharmasift document repositories
  • Regulatory data providers (e.g., FDA databases)

Non-Functional Requirements

  • High Scalability
  • Fast Response Times (under 3 seconds for search)
  • High Accuracy (minimize compliance errors)
  • Data Security and Privacy
  • Consistent Performance

Expected Business Impact

This project is expected to significantly improve Pharmasift's operational efficiency, reduce compliance risks, enhance customer satisfaction, and enable faster time-to-market for its clients. The projected 80% reduction in document review time, 90% faster regulatory search, and 70% improvement in accuracy will translate into substantial cost savings and increased revenue potential. The improved scalability will enable Pharmasift to serve a larger customer base and adapt to evolving regulatory landscapes.

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