Use the least access needed
Projects should only use the systems, fields, and permissions required to make the workflow work. Broad access is not the default.
Data Handling and AI Guardrails
Campbell builds AI-forward automation systems for real businesses, which means data handling and workflow safety need to be treated seriously from the start. The approach is practical: use the least access needed, keep the workflow understandable, and avoid automating sensitive judgment without a human checkpoint.
This page is meant to help clients understand the operating principles. It is not a substitute for your own internal policies, legal review, or formal security requirements when those apply.
Principles
Projects should only use the systems, fields, and permissions required to make the workflow work. Broad access is not the default.
Whenever practical, the implementation should live around the client's existing tools, records, and accounts so the business keeps ownership and continuity.
AI can classify, draft, summarize, or extract. It should not quietly make final high-stakes decisions where expert review is still required.
The client should understand what the automation does, what triggers it, where it writes data, and what happens when something goes wrong.
AI Usage
What Campbell Will Not Promise
Campbell does not present itself as holding certifications or legal guarantees it does not have. If your environment has formal requirements, those should be reviewed explicitly.
If the workflow needs a human sign-off, escalation path, or exception review, the system should respect that rather than hide it.
The preferred model is to move only the information the workflow truly needs and avoid creating extra copies or sprawl when it is not useful.
Clients should be able to ask basic questions about how the automation works and get straight answers about the logic, dependencies, and risks.
Questions Worth Asking
Good questions improve the project.
Ask what systems need access, where data will move, what actions are fully automated, where human approval stays in place, how exceptions are handled, and what the rollback path is if something does not behave as expected.