Data location
We write down what stays on the device, what is synchronized, and which service receives anything that leaves it.
Trust boundaries
Know where the work happens.
Before an AI workflow ships, we answer four practical questions: where the data goes, what the model is allowed to do, what happens when the normal path fails, and who owns the system after launch.
Four questions we answer
Trust comes from decisions you can inspect.
Model permissions
A model may suggest, classify, retrieve, or prepare a handoff. Sending a customer message, changing a record, or making a high-risk decision needs a separate rule.
Offline and fallback behavior
When the device is offline, the model is unavailable, or confidence is low, the workflow must queue, stop, ask, or hand the decision back to a person.
Ownership after launch
Someone owns the approved sources, monitoring, corrections, model changes, incidents, and export or deletion requests after launch.
The runtime loop
From input to accepted action.
- 1
Capture
A phone, Mac, browser, field device, or existing business system provides the input.
- 2
Place
The task is routed to the device, edge worker, private network, or connected model that fits it.
- 3
Review
The user sees the result, source, confidence, proposed change, or next action before a consequential step.
- 4
Record
The accepted result is stored, synchronized, or exported through an explicit product or business boundary.
Every product has different limits
Read the product page before you make a trust assumption.
PlantCare, Safari Go, VoiceAgenda, Farmbit, and WolfeWriter use different device capabilities, data flows, and limits. Each page says what the product does, what it does not do, and where support or human judgment is still required.
For a production system
Write the boundary into the scope.
We document the data flow, permissions, fallback path, and ownership before build work begins.