Matching and scoring

AI grant matching that keeps source facts visible

FundingLens is designed so matching logic can move from manual validation to OpenRouter-compatible automation without losing auditability.

Who this helps

Operators who want explainable grant matching rather than keyword-only funding alerts.

Search intent

Evaluate AI tools for matching an organisation to grants and funding.

What makes this page publishable

Shows how FundingLens separates verified source facts from AI inference, logs model usage and avoids uncapped production runs.

Verified fields include funder, source URL, deadline, geography, legal-form rules and eligible activity where available.

AI fields include plain-English summary, fit reasoning, eligibility caveats, confidence and recommended review status.

Budget caps, task-specific model routing and deterministic fallbacks are part of the production architecture.

Readiness checks

  • Ask why the opportunity matched the organisation profile.
  • Check which facts came from the source and which came from AI interpretation.
  • Require human review before using AI notes in an application.

Eligibility caveats

  • FundingLens explains fit signals, eligibility caveats and next steps; it does not promise funding success or application approval.
  • Always check the official funder page before acting because deadlines, match-funding rules and eligible costs can change.
  • AI output must stay separate from verified source facts and should be reviewed before being used in an application.

Source references

Related FundingLens pages