AIZON PREDICT

Supercharge with AI

Implement AI to more deeply understand your operations and predict issues before they occur

Anticipating the performance of a process before it has ended can enable pharmaceutical companies to significantly improve yield output and drastically reduce manufacturing costs for any given process.

Aizon Predict allows for the creation of a fully adaptable, real-time solution to help production teams anticipate equipment's poor performance and as a result, achieve higher product quality.

Real-time multivariate advanced analytics

  • Continuously control for any potential multivariate outliers and their causes, even when inside the specification limits
  • Reduce the dimensionality of processes and identify the parameters that explain most of the variability
  • Instant feedback when processes deviate from expected behaviors

Faster root cause analysis

  • Reduce time and resources by enabling faster responses to non-conformance events
  • Discover and analyze the relationship between your most relevant process variables to reduce complexity
  • Find hidden insights in a sea of data, something impossible to perceive by the human eye alone

Enrich your operational intelligence with predictive capabilities

  • Recognize potential problems faster thereby improving batch yield
  • Minimize batch rejections with proactive alerts generated by Aizon’s AI models
  • Identify relevant factors to expand prediction of outcomes

Results & outcomes

  • Use AI to gain unprecedented visibility into your processes
  • Reduce manual operations from days or weeks to minutes or hours
  • Minimize batch rejection through automated deviation prediction
  • Multi-site compatible
  • Achieve levels 2-4 on the Digital Plant Maturity Model

Industrialize your AI Models

  •  Manage AI Models at every stage of the process with reproducibility and quality control in mind
  • Operate AI Model predictions at scale
  • Deploy custom models using interoperable and portable ONNX (Open Neural Network Exchange) format
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