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Technical controlling and
quality assurance in AI projects


Sometimes, companies or administrations are not necessarily able to monitor and evaluate the technical implementation of an AI application themselves. Are the right algorithms being used? Are they reusable? Is it ensured that lessons learned from development are documented? 


  • Controlling to achieve goals in the project 
  • Checking the usefulness and sustainability of the AI algorithms used 
  • Lessons learned from data handling 
  • Quality control in the method 
  • Risk analysis 
  • Audits of the “Reliable AI” testing method 


Ensure sure that your AI project is successfully implemented. Learn from past mistakes and use algorithms and procedures for future projects. 

Your contact

Christian Meyer  msg

Christian Meyer
Principal Business Consultant
msg artificial intelligence