Building in public means being honest about what didn't work. These tools were real experiments. Some had the right concept but the wrong implementation. We document them here because the reasoning matters.
A six-persona AI review panel that evaluated nonprofit fundraising appeals and grant narratives. Each persona represented a distinct donor or funder perspective — returning a score, qualitative feedback, and a revision roadmap.
Security. The tool required users to paste real donor data, appeal copy, and grant narratives into a shared AI interface. That creates real risk — data handling, confidentiality, and organizational liability — that we weren't comfortable with once we understood it more deeply.
The concept is sound. If this comes back, it comes back with proper data architecture, no shared inputs, clear security documentation, and explicit guidance on what should and shouldn't go into an AI tool.
A free tool that generated a six-section draft AI policy in minutes — acceptable use, data privacy, disclosure. Built when generative AI was new to the sector and most orgs needed exactly that: something basic to get started with.
AI capability has grown enormously in the last 6-12 months, and it isn't slowing down. What counted as a complete policy a year ago doesn't anymore. This tool never addressed what happens once AI stops waiting to be asked and starts taking action on its own — sending things, scheduling things, making small decisions without a person in the loop each time. That gap didn't exist when this tool was built. It exists now, and it's growing.
A framework built specifically for that — including the part this tool was never designed to cover. See the Agentic Governance Framework on the Nonprofits page.