Screen grant applications against your eligibility rules in seconds — with the field cited behind each pass or fail, and gaps flagged instead of silently rejected.
In short: To screen grant applications for eligibility, run each one against your eligibility rules and have the AI return pass/fail per rule, a completeness check, and any missing attachments — always citing the field behind each judgment. The discipline that matters is flagging gaps, not silently rejecting an applicant over a missing document. Sopact Sense grades each application green, amber, or red so you act on evidence and give applicants a fair chance to fix what's missing.
Begin with the grant round loaded as clean data with persistent contact IDs, so every pass/fail traces to a real application field. Point the assistant at the dataset and have it read your Decision Brief first — the decision, audience, outcomes, indicators, and evidence standard.
You are the Sopact Sense Assistant working over the DEMO-05 · Grant Applications dataset (clean data + persistent contact IDs). Load my Decision Brief (decision, audience, outcomes, indicators, evidence standard) first, then wait for my task.
The prompt names your rules and forces a citation per decision. Paste this verbatim:
Check application [ID] against [ELIGIBILITY_RULES]: pass/fail per rule, completeness, missing attachments. Cite the field; flag (don't reject). Grade green/amber/red.
The prompt works because of five elements: the dataset it checks over, the rules it tests against, the instruction to cite the field behind each pass/fail, the rule to flag don't reject when something is missing, and the demand to grade green/amber/red so gaps are visible.
Run it against the Foundation grant round demo:
Run on the Grant Applications dataset (DEMO-05) already loaded in Sopact Sense.
GRADE: green | budget | present; amber | match | funds unclear; red | 501(c)(3) | missing
The green rule is satisfied — a budget is present and verifiable. The amber rule is the matching-funds requirement, where the figure is ambiguous and needs confirmation. The red rule is the missing 501(c)(3) determination letter — flagged for upload, not used to reject.
Take the lowest-graded rule and resolve it with the single input that moves it. Sense shows the before → after grade.
Take the lowest-graded element above and fix it using only what the program could realistically measure. Show the before → after grade and the single indicator/edit that moves it to green.
Turn the screen into a report, then a link that opens with no login.
Create a 'missing & incomplete' report from this analysis in Sopact branding [or paste your website URL / brand guideline to apply your own]. List every element graded amber or red, what is missing, and the one input that fixes each. Lead with the decision this report informs.
Create a shareable link for this report and open it in a new tab.
Flag, never silently reject. A missing determination letter usually means the applicant forgot to attach it, not that they're ineligible. Mark it red and request the upload so a fixable gap doesn't end a worthy application.
State every rule explicitly. Eligibility lives in the rules you pass in. Spell out tax status, geography, budget floor, and match requirements so nothing is judged on assumption.
Separate completeness from eligibility. An incomplete application isn't an ineligible one. Track the two grades separately so reviewers know whether to request a document or to decline.
Re-check after the applicant responds. Once the missing attachment arrives, re-run only that rule to confirm the application now clears.
Re-check only the 501(c)(3) rule for application [ID] now that the determination letter is attached, and update the eligibility grade.
Grantmakers compare each application against a fixed set of rules — tax status, geography, budget thresholds, required attachments — and mark pass or fail per rule. The reliable way to do it with AI is to require a cited field behind each judgment and to flag missing items for follow-up rather than rejecting outright, so completeness gaps don't disqualify eligible applicants.
No — incompleteness is usually a missing document, not ineligibility. Best practice is to flag the gap, tell the applicant exactly what to upload, and re-check that single rule when it arrives, keeping eligibility and completeness as separate grades.
A screen is trustworthy when every pass/fail cites the field it rests on and when gaps are flagged for resolution instead of silently failing the application. Grading green/amber/red makes the ambiguous cases — like unclear match funds — visible so a reviewer can confirm rather than guess.
ChatGPT, Claude, and Gemini are fine for a quick test — but not for an answer you'll put in front of a funder or board. When it has to hold up, run it in Sopact Sense.
Try it in Sopact →
