By automating the data ingestion process and using a data model that was explicitly designed for generative AI, CDM gives clients a way to use all the data they have – not just the data that is convenient to record.
Structuring data for AI to be effective
CDM structures your data so AI algorithms can succeed, using machine learning (ML) and simulation techniques to reduce the number of experiments needed to generate desired outcomes.
Customer Responsiveness
When you’re faced with new customer requirements, you need to quickly find out which materials could be most easily and cheaply adapted to meet the demand. CDM enables you to do this by preparing your data for AI.
Cost Reduction
CDM arranges data for algorithmic use so you can systematically consider far more parameters than before. This can drive down operating expenses by rationalizing ingredients for bulk purchases, using cheaper alternatives to achieve the same result, or changing process settings to use less energy or increase consistency.
Meeting Changing Regulations, Becoming More Sustainable
Whether you face evolving regulations around conflict minerals, restricted substances, or emissions and waste, CDM’s good data management helps you spot problematic inputs. It also powers AI models that can optimize performance criteria while also reducing dependence on specified materials, improving recyclability, or reducing the carbon footprint of production.