RAIN / Resources
Understand the thinking.Inspect the boundaries.
Explore how local learning works, what a useful pilot needs, and the principles guiding RAIN’s development.
Explore RAIN
01 / EXPLORE
A closer look at resources.
Explore the current foundation, the product direction, and the questions still to be validated.
Notes from a system in development
RAIN Journal
Practical reading on local AI, useful model evaluation and the work of preparing an operational pilot.
ExploreDesign principles · simulation stage
AI Safety
How RAIN approaches separation from plant controls, evaluated model changes and evidence for operator review.
ExploreProduct principles
Responsible AI
A practical approach to operator authority, purpose-limited data and an honest account of what a model can establish.
ExploreWorking product framework
Ethics Framework
A working set of questions for deciding what an on-site AI system should observe, explain and leave to people.
ExploreAudit framework · no independent report published
LLM Audit & Model Evidence
Understand RAIN’s recorded model history, the evidence an evaluation needs and the limits of the current prototype.
ExploreArchitecture explainer
Why Local-First AI
Why RAIN brings capture, inference and retraining together at the facility, and what local operation still needs to prove.
ExplorePlanning for a first field pilot
The First-Pilot Guide
A practical starting point for defining a RAIN design-partner pilot: data access, operating boundaries and evidence of usefulness.
ExploreModel evaluation explainer
Evaluating Model Updates
How candidate evaluation, held-out data and retained model history support a more inspectable local learning loop.
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A useful next step.
RAIN is preparing for its first field pilot. Start by defining the environment, the data boundary, and what success should look like.