Donor Intelligence & Grantee Insight for a Community Foundation
Illustrative reference architecture. This is a composite engagement template built to show how Mopshy structures enterprise work. It is not a real Mopshy client, and every figure, KPI, and quotation on this page is illustrative rather than measured client data.
AI-assisted prospect research, grant application triage, and impact reporting — deployed with a small development team and a tight governance stance.
+27%
Major-gift pipeline value
−58%
Grant review cycle time
3×
Donor touches per officer
The bottleneck
Development team of 9 managed 4,200 donor relationships. Grant reviews took 6 weeks per cycle. Impact reporting was manual PDFs no one read.
The system
- Prospect-research agent surfacing capacity + affinity signals per donor
- Grant-application triage summarizing alignment with focus areas and prior awards
- Impact-report generator producing donor-specific narratives from grantee data
- Governance: no automated decisioning on grant awards; humans retain authority
Outcomes measured, not promised
4,200
Donor profiles enriched
94%
Officer trust score on prospect briefs
9
FTE development team unchanged
0
Awards made autonomously by AI
What it cost, what it returned
Investment
$680K program (year one)
Payback
10 months
Annualized value
$4.9M incremental major-gift commitments
How it is built
- Prospect-research agent with source-cited briefs
- Grant-application triage with rubric-aligned scoring
- Impact-narrative generator with grantee opt-in data
Representative engagement. This case study describes a composite of Mopshy AI's enterprise engagement patterns, delivery methodology, and observed outcome ranges. Company names, individual quotes, and specific figures are illustrative and used to communicate the enterprise pattern rather than to describe a single identified client.
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