Understanding Our Scoring Systems

How GrantCue Helps You Find the Right Grants

GrantCue uses multiple scoring systems to help you prioritize opportunities. Here's how each one works and what it tells you.

Comparing Our Scoring Systems

FeatureProfile MatchAI Success ScoreAI Recommendations
PurposeBasic eligibility checkPredict win probabilityDiscover new grants
Available OnFree planEssentials+ plansEssentials+ plans
Factors Analyzed3 (category, location, amount)5+ (history, competition, fit)5+ (behavior, similar orgs)
SpeedInstant (client-side)Fast (server-calculated)Fast (server-calculated)
Best ForQuick filteringPrioritizing applicationsFinding opportunities
Free

Profile Match Score

A quick eligibility check that compares grants against your organization's profile settings — calculated instantly in your browser.

How It Works

  • Category Match (30 points)

    Does the grant's funding category match your focus areas?

  • Location Match (20 points)

    Is the grant available in your primary geographic regions?

  • Amount Match (20 points)

    Does the funding amount fall within your target range?

  • Base Score (30 points)

    Starting score for all grants.

Score Breakdown Example
Category Match (Education)+30
Location Match (California)+20
Amount Match ($50K-$500K)+20
Base Score+30
Total Score100/100

80%+ = Excellent | 60-79% = Good | 40-59% = Moderate | <40% = Low

Five-Factor Analysis
Organization Fit
30% weight

How well your org type and focus areas match eligibility.

Agency History
25% weight

Your track record with this agency (win rate).

Competition Level
20% weight

Expected applicant pool based on funding amount.

Funding Amount Fit
15% weight

Grant size vs. your typical awards.

Timeline Feasibility
10% weight

Time available to prepare the application.

Essentials+

AI Success Score

Predicts your likelihood of winning a grant using historical patterns, competition levels, and your organization's track record.

How to Use Success Scores

  • Excellent (80%+)

    Prioritize these grants — strong fit across all factors.

  • Good (65-79%)

    Recommend applying if you have the capacity.

  • Fair (50-64%)

    Consider the challenges before committing resources.

  • Poor (<50%)

    May not be the best use of your limited resources.

Essentials+

AI Recommendations

A hybrid recommendation engine that discovers grants based on what similar organizations save — the same collaborative-filtering approach used by Netflix and Spotify.

How ML Recommendations Work

  • User-Based Collaborative Filtering (45%)

    Finds organizations with similar grant-saving patterns using cosine similarity, then recommends grants they've saved that you haven't seen.

  • Item-Based Collaborative Filtering (25%)

    Finds grants frequently saved together with ones you've liked (co-occurrence analysis).

  • Content-Based Features (30%)

    Matches on category, agency familiarity, and funding range from your profile.

The ML Pipeline
  1. 1Build interaction vectors from your saved/submitted grants
  2. 2Calculate cosine similarity with all other organizations
  3. 3Find the top 20 most similar orgs (min 2 shared grants)
  4. 4Score grants by weighted sum of similar-org interactions
  5. 5Combine with item-based CF and content features

Similarity scores cached for 1 hour • Recommendations refresh on each request

Unlock AI-Powered Scoring

Upgrade to access AI Success Scores and personalized recommendations that help you prioritize the grants most likely to succeed.

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