Why keep the learning loop local?
Explore how local capture, inference and retraining fit together, and why physical location is only part of a data boundary.
RAIN / Resources
Ideas from thelocal learning loop.
Practical reading on local AI, useful model evaluation and the work of preparing an operational pilot.
Notes from a system in development
INTELLIGENCE BELONGS WHERE THE DATA LIVES.
01 / THE IDEA
Start with the questions that matter.
Where should a model run? How do you know an update is useful? What does a first pilot need to prove? These guides explain the choices behind RAIN, grounded in the current battery-storage simulation and the work still ahead.
Evaluate before you promote.
A model update needs more than a better training score. Separate evaluation data, comparable conditions and a retained incumbent make the decision inspectable.
Scope a useful first pilot.
Choose one operational question, map the available signals and agree how operators will judge the result before collecting more data.
02 / IN PRACTICE
Put the thinking into practice.
Define the scope, preserve the boundary, and make the outcome something a person can inspect.
- 01
Understand the architecture
Follow the path from equipment signals to local storage, model evaluation and operator review.
- 02
Identify the evidence
Separate what a simulator can demonstrate from what needs a real asset, real signals and sustained operation.
- 03
Apply it to your site
Use the pilot guide to turn an interesting idea into a question with a clear boundary and measurable outcome.
03 / A FEW DETAILS
Worth understanding.
Are these customer case studies?
No. These are RAIN product and engineering explainers. RAIN has not claimed a field deployment or measured customer outcome.
Where should an operator begin?
The pilot guide is the best starting point. It helps define an asset, an operational question, available telemetry and the read-only access boundary.
BUILD WITH RAIN
One site. One question.
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.