Responsible AI
Responsible AI as an operating process
Responsibility is not a statement added to a finished product. It is a set of checks and decisions from problem definition through operation.
Discuss your project →Before development
We begin with the task, the user's authority and the consequences of error. We identify which data are necessary, who owns them, why access is permitted and which actions cannot be delegated. If value cannot be validated or risk cannot be bounded, the hypothesis does not proceed to a pilot.
- A specific user and the decision being supported
- Minimum necessary sources and fields
- Information classes and access roles
- Critical consequences and prohibited actions
- Quality criteria and stop conditions
During the pilot
A pilot uses an approved dataset and control examples. Answers, findings and actions have a defined validation source. Errors are not hidden behind an average score: the team examines their types, recurrence and consequences. An accountable person confirms critical outputs.
- Source references where feasible
- A separate set of control examples
- Logs of requests, outputs, errors and approvals
- Permission checks in the context of each user
- Ability to disable the feature or return to the existing process
Before and after launch
A successful demonstration is not operational readiness. Before expanding access, we define solution ownership, change control, quality monitoring, incident response and periodic permission reviews. A model, source or business-rule change triggers retesting of the relevant control examples. Documentation and knowledge remain with the client team.
- Product and data owners
- Versions of models, prompts, sources and rules
- Quality and operational-failure monitoring
- Incident, rollback and shutdown procedure
- Regular access and source-freshness reviews
Practical resources
Apply the methodology with your team
Download pilot readiness, provider evaluation and acceptance checklists without registration.
View the documents →Frequently asked questions
Frequently asked questions
Does human oversight require reviewing every answer?
Not always. Oversight follows the consequences of error. Informational retrieval may use sampling, while actions with financial, legal, employment or safety consequences require explicit approval.
How should access work in an AI assistant?
An assistant must not expose a document merely because it exists in the knowledge base. Retrieval and answers should respect the specific user's role and source-system rules.
What triggers shutdown?
Conditions are defined before a pilot, such as a critical error type, loss of logging, an access violation or quality falling below an agreed criterion.
Is this a legal or certification guarantee?
No. It is our delivery methodology. Specific legal, sector and security requirements are determined for each client and environment.
Ideas on this topic