Our beliefs
Organizations serving their communities should not be the last to benefit from AI.
The implementation gap
AI adoption is increasingly constrained not by access to models, but by access to implementation capacity.
Companies closest to AI have teams dedicated to experimenting, evaluating tools, and redesigning workflows. Community organizations rarely have the same time, technical support, or room to experiment.
The hard part is understanding a real workflow and translating it into something useful. A donated license helps, but it does not map workflows, train staff, establish governance, or maintain a system after setup. Even modest software or usage costs can become a barrier for smaller organizations.
Good Model Project brings technical experience to this work. We work alongside community organizations to identify where AI and automation can help, implement useful systems, and support their continued use.
The nonprofit is the beneficiary
Volunteers will learn and contributors may gain experience along the way. These are secondary effects. If a project is not useful to the nonprofit, it should not continue simply because it is interesting to contributors.
Some organizations may not need AI. Some may not want it. Some operate in areas where the risks outweigh the benefits. Sometimes the right answer does not involve AI.
The purpose is to responsibly increase capacity. That requires clear limits: protecting frontline human care, retaining oversight of sensitive decisions, and building systems staff can understand and maintain.
Implementation and impact
A workflow can function correctly and still be useless. It can produce polished outputs and save no time. It can automate a task that should remain manual, create false confidence, or introduce new maintenance work.
Good Model Project requires attention to both implementation and impact. Can we turn a difficult operational process into a functioning system? Does that system actually help the organization?
Workshops run, tools introduced, and prompts written do not establish that an organization became more capable. The question is whether staff saved time, fewer tasks fell through the cracks, or the workflow remained usable after the original team left.
Continuity matters. If a volunteer builds something brittle and disappears, the nonprofit inherits the maintenance burden. Documentation, shared workflows, and clear ownership are part of the work.
Shared infrastructure
Many nonprofits face similar operational problems. If one team develops something useful—an intake workflow, a training resource, or an integration—other organizations should be able to benefit from it.
Good Model Labs is the software and research arm of the project. Its work should grow from repetition: seeing the same problem across several organizations, understanding it well, and reducing the cost of solving it again.
AI will create institutional advantages. The question is who gets to use them, who shapes them, and who benefits. The communities that will depend on this technology should have a hand in shaping it.