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Grants are Built on Evidence

Back up your claims with real data, not just words

Without Evidence

"Our youth program significantly impacts the community..."

  • • Vague and unmeasurable
  • • No proof of impact
  • • Sounds like every other application
  • • Easy for funders to dismiss

With Evidence

"Our youth program served 847 students last year, with 92% improving their grades by at least one letter and 78% enrolling in post-secondary education..."

  • • Specific and measurable
  • • Demonstrates real impact
  • • Stands out immediately
  • • Impossible to ignore

What Grant Reviewers Actually Look For

Reviewers Want Evidence

Grant reviewers prioritize organizations that can prove they've delivered results before

Specific Metrics Matter

Applications with quantifiable outcomes have significantly higher success rates

Vagueness Causes Rejection

Many rejections cite "insufficient evidence" or "unclear impact" as primary reasons

The Evidence-Based Grant Writing Framework

1. Organizational Evidence

Your track record is your strongest asset. Document and organize:

  • ✓Past program outcomes with specific numbers
  • ✓Client testimonials and success stories
  • ✓Awards, recognitions, and partnerships
  • ✓Financial stability and growth metrics

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Every Achievement Counts

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Precision Wins Funding

2. Program-Specific Evidence

Generic descriptions lose. Specific evidence wins:

  • ✓Exact number of people served
  • ✓Measurable outcomes and improvements
  • ✓Cost per outcome achieved
  • ✓Comparison to baseline or control groups

3. External Validation

Third-party evidence carries extra weight:

  • ✓Independent evaluations and studies
  • ✓Media coverage and press mentions
  • ✓Partner organization endorsements
  • ✓Government or academic citations

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Credibility Through Proof

The Hidden Danger of AI-Generated Grants

Generic AI tools like ChatGPT can write beautiful prose, but they have a fatal flaw: they make things up. In grant writing, a single fabricated statistic can destroy your credibility forever.

Examples of AI Hallucinations:

  • ✗Invented success metrics that never existed
  • ✗Created fictional program names and dates
  • ✗Fabricated partnerships with organizations
  • ✗Generated plausible but false statistics

This is why Northstar's anti-hallucination technology matters: every claim traces back to your actual documents.

How Northstar Ensures Evidence-Based Excellence

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Draw from Verified Sources

AI uses information from your uploaded documents and websites

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Citation Tracking

Every statistic links back to its original source document

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Anti-Hallucination Design

Our AI uses advanced techniques to minimize the potential for AI hallucinations

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Advanced Outlining

Our AI outlines your grant application with evidence sources so you can review and edit every claim

Ready to Win Grants with Evidence?

Stop losing grants to organizations with weaker programs but stronger evidence. Let Northstar help you showcase your true impact.

Start Building Your Evidence LibrarySee How It Works