Day Research
A Division of Charles F. Day & Associates

Day Research A Division of Charles F. Day & AssociatesDay Research A Division of Charles F. Day & AssociatesDay Research A Division of Charles F. Day & Associates

Day Research
A Division of Charles F. Day & Associates

Day Research A Division of Charles F. Day & AssociatesDay Research A Division of Charles F. Day & AssociatesDay Research A Division of Charles F. Day & Associates
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    • Home
    • QWISE™
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    • Our Mission
    • Establishing Credibility
    • cfday.net
    • NTSA Award: Dr. Alok

  • Home
  • QWISE™
  • ESISST
  • RWISE
  • Day Research News
  • Our Mission
  • Establishing Credibility
  • cfday.net
  • NTSA Award: Dr. Alok

RWISE Reference World Information and Simulation Environment

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Changing the Game

 

The Reference World Information Synthetic Environment (RWISE) platform is a decision-making tool providing reliable, actionable information and data-driven insights that allow leaders to test policies, identify shortfalls, examine multiple parallel simulations, and compare key metrics of proposed courses of action before committing resources.

Reference World is more than a data analytics or Modeling & Simulation software. It is an AI/ML/DS enriched workflow that moves data first through curation, extract, transform, load, then through AI/ML capture and creation of a relationship network model.  All relationships within and between data sets are integrated into a synthetic model.  We provide an AI/ML enriched Simulation engine where you can test the future impacts of changing variable based on the synthetic models.

Why Choose RWISE?

Just to list a few reasons:

Visit   https://rwise.ai/  for more information


The Most Advanced A I Platform Available Today


Our synthetic models mimic the human brain to forecast Changes in Human Behavior


RWISE Uses Multiple Machine Learning Technologies


Greater Intelligence Applied to Complex Machine Learning


The Power of Both Inductive and Deductive Modeling


Competitive platforms focus on Deductive modeling. (Generally working off of legacy assumptions)


Both "Human in the Loop" as well as "Human on the Loop"


This provides greater flexibility in manipulating the future environment. Automated process with human intervention as needed.

            Click for RWISE website 

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