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In many cases, AI projects in enterprises are approached in the same way as other IT implementations, where focusing on selecting an experienced vendor and creating a precisely defined proof of concept are the key milestones. Projects carried out in this way usually end in the PoC phase and never reach deployment. The search for efficient solutions using data science or machine learning requires a different approach.
At deepsense.ai we work on the basis of CRISP methodology, a process model including six stages of running AI projects that lead to the right solution when repeated over and over again. This concept involves an agile approach, where priorities and pathways change as project development progresses.
Startup Natural language processing supporting communication Learn more
Beauty Recognition and classification of skin condition 91% of accuracy Learn more
Nielsen Holdings 93% reduction in data gathering time by reducing the amount of manual work Learn more
Volkswagen Generated over 100 years of simulated driving experience for vehicle training Learn more
The United Nations 91% of Twitter accounts disseminating terrorist propaganda identified Learn more
NOAA 98% time reduction in image analysis Learn more
United States of America