Transparency
People know when they are interacting with AI, why it is used and when a team takes over.
Responsible AI · operational safety
Trust is built into the system.
An AI system is ready for operations only when people understand its purpose, boundaries and accountability. We plan control, evaluation and failure handling from the start.
Five principles
Responsibility in system design.
Implementation depends on the data, users and risk of the case. These principles are our starting point.
Data minimisation
Use only the data, retention and access the defined workflow requires.
Human oversight
Route sensitive, unclear or out-of-scope situations to accountable people.
Evaluation
Test behaviour against representative tasks, edge cases and explicit criteria before and after launch.
Incident handling
Record failures, limit impact, investigate causes and re-evaluate changes.
Make purpose and boundaries concrete
We describe what a system is expected to do and what it must not do. Permitted actions, prohibited content, user groups and handoff cases are defined before implementation. Interactive AI also needs clear identification in the user experience.
Data and permissions by need
Data sources are not connected by default. We examine which content is required, who may access it and how long it is needed. Tool permissions are limited so an agent can only perform intended actions.
Human control as its own workflow
A generic “hand over to a person” button is not enough. We define the trigger, target role, context and response path so a team can continue without unnecessary repetition.
Evaluation before and after launch
Quality is made measurable for each workflow. Criteria can include correct routing, completeness, safe refusal, appropriate handoff and successful tool actions.
Detect and handle failures
Production systems need routes for reporting errors and incidents. Critical actions must be stoppable. Lessons are incorporated into rules, data or system logic in a controlled way.
Legal contextThis page describes development principles and is not legal advice. Data protection, the EU AI Act and other requirements must be assessed for each specific use case.
Review questions
What must be answered before launch?
These questions make responsibility visible across the business, technology and operating teams.
Who decides?
Who owns the process, approves data and is accountable for changes?
When is the handoff?
Which uncertainty, sensitivity or exception requires a person?
How is quality tested?
Which test cases and operating signals show the system stays within agreed quality?
Make responsibility concrete
Surface risks early.
In the strategy call, we examine not only value but also the data, decisions and handoffs in one workflow.