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Knowledge Capture

Capture, codify, and reuse expert knowledge

Capturing expert knowledge and data in the Citrine Platform reduces costs in subsequent projects.

  • It helps new colleagues get up to speed quickly
  • It enables digital assets such as data sets, AI models, and analytical formulas to be shared and reused.

Reduced initial investment

Learning from Previous Projects

Reuse of digital assets

DATA

Our platform contextualizes data so that it can be reused in multiple projects. Instead of having a floating Excel spreadsheet, it enables users to link the raw measurements to final processed data so that future users can understand the context and pedigree of the data.

EXPERT KNOWLEDGE

Analytical formulas, rules of thumb, known correlations, important constraints, and key features can all be captured and recorded in the platform. This expert knowledge can improve the AI model accuracy and serve as a reusable asset in future projects.

Our graphical models enable users to seamlessly combine analytical formulas with data-driven models. Each node in the graph represents an AI model or an expert-provided formula. Each link represents a known relationship or correlation. This hybrid approach means that you can build accurate models with less data, efficiently using all the information available to you.

domain knowledge integration

HELP NEW EMPLOYEES GET UP TO SPEED

A SHRM report suggests that almost 27% of workers in the manufacturing sector will retire within the next decade. For materials and chemical companies this means the loss of some of their most senior experts. Our platform captures and records domain knowledge to prevent knowledge loss and enable smooth and efficient knowledge transfer.

Capture Knowledge

CAPTURE AND DISSEMINATE KNOWLEDGE

DESIGNED TO BE INTERPRETABLE

The Citrine Platform is designed to make sure that data and models are interpretable.

  • New employees can quickly find out what data are available
  • Links between data are clear
  • AI models are represented graphically and known relationships between parameters are clear to see
  • The importance of each feature to an AI model is explicit

Feature importance of AI