AI LabLLM ReportingM&EDonor ReportsHuman Review
Automated Intelligence Reporting (LLM)
Automated Intelligence Reporting turns structured field indicators into short, reviewable reporting drafts. It is built around responsible use: the model helps prepare summaries, but a person still reviews the narrative before it is shared.
70%
Reporting time saved target
4
Indicator groups
3
Risk themes
100%
Demo data
⚠ Problem
Programme and M&E teams often spend days converting raw indicator tables, field notes, and partner updates into donor-ready reports. The process is repetitive, time-consuming, and vulnerable to inconsistent narrative quality.
✓ Solution impact
The project shows how reporting teams can generate first-draft narratives faster while keeping human review, source evidence, and indicator alignment in the workflow.
🔒
Safe demonstration. This project uses demo data and representative workflows only. It avoids exposing client, security, beneficiary, or operationally sensitive information.
Use cases
NGO / Donors
Donor report automation
Draft grant narratives from validated field indicators.
M&E Teams
Indicator commentary
Explain target-vs-actual performance and highlight risks.
Executives
Situation briefs
Convert multi-source updates into concise management summaries.
Architecture diagram
Validated field data
→
Prompt template
→
LLM-style narrative generation
→
Human review
→
Donor brief / executive report