Automated Grant Application Review with AI Integration

Automated Grant Application Review and Scoring

1. Application Submission

1.1 Online Portal Access

Applicants submit their grant applications through a secure online portal.

1.2 Data Capture

Utilize Optical Character Recognition (OCR) tools such as Adobe Scan to extract text from scanned documents.

2. Initial Data Validation

2.1 AI-Powered Data Validation

Implement AI algorithms, such as Google Cloud AutoML, to validate the completeness and accuracy of submitted data.

2.2 Error Notification

Automatically notify applicants of any discrepancies or missing information via email using Mailgun.

3. Application Review Process

3.1 AI-Driven Content Analysis

Leverage Natural Language Processing (NLP) tools like IBM Watson to analyze application content for relevance and alignment with grant objectives.

3.2 Scoring Algorithm Implementation

Develop scoring algorithms using machine learning frameworks such as TensorFlow to assess applications based on predefined criteria.

4. Scoring and Ranking

4.1 Automated Scoring System

Utilize AI-driven scoring systems to rank applications based on scores generated from the analysis phase.

4.2 Review Committee Dashboard

Create a dashboard using Tableau for review committee members to visualize scores and rankings for informed decision-making.

5. Final Decision Making

5.1 Committee Review

Facilitate a virtual meeting platform, such as Zoom, for the review committee to discuss and finalize funding decisions.

5.2 Decision Notification

Automatically notify applicants of the final decision using email automation tools like SendGrid.

6. Post-Application Follow-Up

6.1 Feedback Collection

Utilize survey tools such as SurveyMonkey to gather feedback from applicants about the application process.

6.2 Continuous Improvement

Analyze feedback data using AI analytics platforms like Microsoft Power BI to improve the grant application process for future cycles.