GIVVISH is a proposed collaborative public service project that aims to  design, develop, deploy, and maintain a socially-scaled, human relevant  method validation platform and library.  It will feature open-source  data storage and processing for delivering super-computed algorithms for  evaluation and regulatory review of emerging human-based assay methods  and tools used in pre-clinical drug development.  The platform will  incorporate Big Data, user/enterprise level security and sharing  settings, and a peer review process to streamline the validation pathway  for these new technologies.  In collaboration with industry, academia,  and government entities, our goal is to provide the surface and  structure to organize and review large volumes of multi-sourced,  independent data to facilitate the bridge from last century's reliance  on animal models to a 21st century solution- a predictive model based on  human biology for assessing human clinical outcome.   The GIVVISH  platform will function in real-time, with deep machine learning for  constant refreshing of algorithmic codes for predictive modeling,  creating an organically-built validation engine and library of validated  methods.  By facilitating engagement of the scientific community and  regulatory agencies with statistically-significant volumes of computed  data in an organized manner, a validation process can take hold.  The  GIVVISH vision is a human-relevant predictive model that will help  deliver safer, more efficacious therapies, spur innovation, reduce cost,  and align with our highest scientific and ethical potential.