Use Cases

Validate models and datasets without exposing data

By September 4, 2021September 7th, 2021No Comments

The risk of sharing and collaborating over data in a way that may lead to legal action or regulatory fines is an extremely powerful disincentive.  

Imagine going through expensive and time-consuming negotiations, legal checks, and compliance approvals to access other parties’ data without any guarantees on the expected value that could be derived. Assuming the data isn’t outdated by the time it’s received, all parties would have to worry about data breaches, data leaks, maintaining database integrity, 3rd party breaches, and their investigations (that may or may not be related to the data understudy), lawsuits, regulatory fines, etc…  

If it sounds archaic, that’s because it is.  

We understand that innovation is based on experimentation which is why we help you set up collaborative AI initiatives through easy, secure data access. In addition to outsourcing ML/AI efforts to data scientists, researchers, or startups without putting any data at risk, you can also validate datasets and model accuracy without acquiring or being exposed to the data in full. 

Fuel internal and external AI initiatives

  • Solve the main hindrance behind AI initiatives and research – data access 
  • Outsource machine learning and AI model development efforts 
  • Integrate AI solutions through partnerships rather than investing in an in-house team 

Test new data assets and collaboration initiatives 

  • Develop partnerships quicker and with fewer risks
  • Audit and track metrics and computational consumption 
  • Examine overlap between available and prospective datasets 

Promote innovation with startups and entrepreneurs 

  • Remove risks to data access for AI-based startups and research projects 
  • Assess external startups’ capabilities without exposing data or algorithms
  • Run innovation challenges for startups and students  
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