Challenge / Need

A global leader in pharmaceuticals, sought to revolutionize their chromatogram processing by leveraging machine learning (ML). The challenge was to collect raw chromatogram data from various sources and apply ML models to process and annotate this data. Netica’s solution provided a unified and automated data flow, transforming the way partner handled chromatograms. 

Netica’s Solution: Automated ML Pipeline

Netica’s NetILab offered a comprehensive platform tailored to partners specific needs: 

  • Automated Data Collection: Seamless integration with various chromatogram systems, eliminating the need for manual extraction and data collection.
  • ML Prediction Pipeline: Implementation of an automated machine learning prediction pipeline, transforming raw chromatograms into actionable insights.
  • Review and Sign-Off Capability: Providing the ability to review and sign results and predictions made by ML models, either automatically or manually, ensuring accuracy and compliance.
  • PDF Report Generation: Creating detailed PDF reports that include predictions and reviews, offering a complete overview of the chromatogram processing.

Transform Your Chromatogram Processing with NetILab. With ML applied chromatogram processing solution offers a cutting-edge approach to handling chromatograms. Whether you’re in the pharmaceutical industry or exploring other scientific applications, NetILab provides a tailored solution to meet your unique challenges. 


By implementing NetILab, partner achieved: 

  • Streamlined Data Collection: No more manual extraction, saving time and reducing the risk of errors.
  • Automated ML Predictions: Enhancing chromatogram processing with automated machine learning predictions.
  • Control and Compliance: Ability to review and sign off on results, ensuring alignment with industry standards and internal requirements.
  • Comprehensive Reporting: Generation of PDF reports with included predictions and reviews, providing a clear and concise record of the chromatogram processing.

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