What I’d love feedback on:
- API design for async + HITL workflows
- where donor-specific logic should live vs generic strategy prompts
- ingestion/RAG ergonomics for real proposal teams
- whether this is useful as a standalone API vs embedded library
A few caveats / current limitations (so expectations are clear):
- It’s an MVP and currently optimized for drafting workflow structure, not final donor submission formatting.
- Donor coverage is mixed: some donors have specialized strategy behavior, others use shared generic logic with catalog aliases.
- RAG ingestion is intentionally simple right now (PDF ingest + namespace isolation); deeper citation traceability is on the roadmap.
- Multi-tenant auth/permissions is not implemented yet (API key is service-level).
For years I’ve worked on Results-Based Management (RBM) in nonprofit/development programs, where early-stage logic model design is still very manual and repetitive.
I started with GPT-assisted drafting, and now I’m piloting the next step with OpenClaw autonomous agents: a focused skill for nonprofit RBM logic model development.
What it generates:
-5-level results chain: Inputs -> Activities -> Outputs -> Outcomes -> Impact
-Theory of Change (if/then pathway + assumptions + risks)
-SMART outcome indicators
-SDG alignment
-Monitoring and data collection plan
Who it’s for:
-nonprofit program managers
-MEAL/M&E specialists
-grant writers and NGO consultants
This is early-stage and intentionally human-in-the-loop.
Goal: faster structured drafting, but expert validation remains the hard gate for quality decisions.
Main open issues I’m actively thinking about: confidentiality, governance, accountability, and validation quality.
I’m testing a personal profile site optimized for AI/search parsing (llms.txt, schema, sitemap, case studies, measurable outcomes).
For those experimenting with this: what has actually moved the needle for discoverability and recruiter conversion?
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