Grant guide
OpenRouter architecture for low-cost grant matching
FundingLens uses an OpenRouter-compatible AI layer so grant matching can route tasks to suitable low-cost models while keeping source citations and audit records.
Teams deciding where to spend application time
Technical buyers, operators and consultants assessing how FundingLens can automate grant matching safely.
Make the first review more concrete
Understand OpenRouter-compatible architecture for AI grant matching.
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.
Route tasks by purpose: source summarisation, fit reasoning, caveat extraction, draft notes and review-pack generation can use different model settings.
Keep cost caps, usage logs, retry status, model choices and fallback output in the AI audit table.
Do not run uncapped production analysis; source facts must remain separate from AI inference and human review status.
Readiness checks
- Task-specific model route configured.
- Cost cap and retry policy recorded.
- Source citations included in output.
- Deterministic fallback available.
- Human review state required before alerts.
Eligibility caveats
- Provider routing improves flexibility but does not guarantee availability or perfect output.
- Cheap models still need task-specific prompts and regression checks.
- FundingLens should not run uncapped AI jobs in production.
Source references
Use OpenRouter routing docs to explain provider selection, fallbacks and model routing without promising perfect availability.
Official sourceOpenRouter limitsUse OpenRouter limits documentation to frame rate-limit, key and budget caveats for production grant matching automation.
Official sourceGovernment Data Quality FrameworkUse the framework to ground metadata, data quality, provenance, caveats and review practices for public-source funding data.
Related FundingLens pages
AI-assisted grant matching for organisations that need source-cited fit reasoning, confidence, caveats and human review status.
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 guideConfidence score: how much evidence supports a funding matchUnderstand confidence scores in AI grant matching as evidence-quality signals for source completeness, ambiguity, freshness and review need.