SoKat has successfully built and deployed Intelligent Automation to federal grants management. In sifting through a massive amount of data using Machine Learning tools, our team built a risk profile for over 750,000 prospective grant applicants and predicted a correct negative audit finding for an applicant in  96% of the cases. Most impressively, our model reduced the grant application processing time from weeks to days. Examples of the types of use cases in the federal government to that we can apply our Automated Intelligence tool are: 1) Application Eligibility and 2) Application Review. 

Use Case 1: Application Eligibility

Applications received by an awarding agency must first be screened to ensure that they meet the eligibility criteria for the award itself. These steps must be manually taken by a reviewer in determining grant eligibility. Our AI tool reduced the eligibility review time from 2 weeks to 1 day. 

Use Case 2: Application Review

A complete application evaluation involves manually accessing multiple systems and data sources to provide an an accurate understanding of the applicant’s historical performance and financial health. Our AI tool reduced the  application review time from 4 weeks to 3-5 days. 

Now, for a closer look at how we applied our IA tool.

Past Project: Intelligent Automation in the Federal Grants World

Problem:

  • $800B in Federal grants need to be invested by means of slow, manual processing of grant applications
  • Siloed data
  • Legacy systems

Our Solution:

  • Intuitive search with automated intelligence
  • Clear transparency of potential risks; audit predictor model was accurate in 96% of the cases.
  • Easily and rapidly scalable due to cost efficient build-out

Outcome/Lessons Learned:

  • Saved an estimated $30M/annually in costs to the federal agency
  • Reduced administrative process time of applications from days/weeks to mere seconds
  • Our AI tool we built won GCN Government Innovation Awards 2018 and 2019