Trust at Marsen
Useful AI. People in control.
Our approach to building AI workflows with approved information, human review, and a clear way to correct mistakes.
Start with a task people understand. Define what AI may do, where it must stop, and who takes over when the answer or action is uncertain.
Choose a bounded job
A useful starting point is preparing a customer brief, drafting a reply, or organising a weekly update. Define the input, expected result, and owner before expanding the workflow. Avoid treating a fluent answer as proof that the work is correct.
Use information you are allowed to use
Agree knowledge sources, provider access, and retention before connecting data. Keep sensitive information out of demonstrations and public tools. Voice, identity, and customer information need appropriate permission for the intended use.
Make review part of the workflow
Let people inspect sources and edit drafts. Require explicit review where an action affects a customer, money, access, or a consequential decision. If a workflow cannot resolve a request reliably, hand it to a named person with the context they need.
Test ordinary cases and difficult ones
Evaluate representative tasks as well as missing information, conflicting sources, malicious instructions, and requests outside scope. Check whether outcomes differ unfairly between people or groups. Record failures and decide which prevent release.
Monitor, correct, and pause
Agree who reviews feedback and how the team can stop an automation. Keep a record appropriate to the work, correct source information, and repeat evaluations when models, prompts, permissions, or integrations change.
Talk about your requirements
Tell us which decisions matter, what information is sensitive, and who must approve an action. We will use those constraints to shape the proposed workflow, rather than assuming the same setup fits every business.