Grant guide
Confidence score: how much evidence supports a funding match
A confidence score explains how well the available evidence supports a match or summary. It is about data quality and uncertainty, not whether a funder will award money.
Teams deciding where to spend application time
Funding teams reviewing AI-assisted match summaries, source facts and alert previews.
Make the first review more concrete
Understand confidence scores in AI grant matching and funding alerts.
Review workflow
What FundingLens helps you do
Keep source facts, caveats and next actions together so your team can decide what deserves attention before application work starts.
Confidence should rise when source fields are complete, current, specific and easy to map to the organisation profile.
Confidence should fall when source data is missing, stale, ambiguous, contradictory or heavily dependent on AI inference.
Use low-confidence records as review prompts rather than hiding them or turning them into polished claims.
Readiness checks
- Source completeness and freshness checked.
- Ambiguous eligibility wording flagged.
- AI inference separated from verified facts.
- Confidence shown beside caveats.
- Low-confidence matches routed to human review.
Eligibility caveats
- Confidence is not certainty.
- A high-confidence source summary can still describe an opportunity the organisation should not pursue.
- FundingLens does not use confidence to promise funding success.
Source references
Use the framework to ground metadata, data quality, provenance, caveats and review practices for public-source funding data.
Official sourceGovernment Data Quality Framework guidanceUse the guidance to explain metadata, audit information, quality rules and data-quality dimensions without treating scraped data as perfect.
Official sourceDSIT AI assurance portfolioUse UK government AI assurance guidance to frame review records, evidence, limitations and human oversight for AI-assisted funding analysis.
Related FundingLens pages
Track grant data provenance with source URLs, fetched dates, raw fields, normalised fields, confidence notes and known caveats.
Grant guideAI grant audit trails for source-cited funding reviewsCreate AI grant audit trails with source inputs, model routing, budget records, citations, deterministic fallbacks and human review status.
Grant guideMatch score: a triage signal, not a funding predictionUnderstand FundingLens match scores as source-cited triage signals covering eligibility, fit, budget, readiness and evidence strength.
Draft notes guideSource-cited draft notes for grant applicationsUse AI draft notes for grant preparation while keeping official source citations, caveats and human review clearly visible.