Our approach to trust
Make the basis for trust visible.
For Loomiq, building trust means making it possible to understand what an AI system does, where its limits are and who remains accountable.
Trust Infrastructure
Loomiq’s working approach connects the behavior of the system with the responsibilities of the people using it. These are the questions we bring into the design.
Ownership and boundaries
Name the operating owner and define what the system may access, change or send.
Inspect the agreementRole assignments, scoped permissions and approval points.
Evaluation and intervention
Decide what to test, how to recognize a failure and when a person should step in.
Inspect the behaviorEvaluation results, exceptions and stop conditions.
Evidence and review
Record the actions and decisions that matter, then review them as systems and usage change.
Inspect the recordDecision logs, known limitations and review dates.
Agent Receipts
A record someone can use.
For an AI workflow, a receipt should help the next person understand what happened and what still needs attention. The fields and level of detail depend on the workflow and data-handling requirements.
- What did it do?The relevant action and its purpose.
- What changed?The result, with supporting evidence where appropriate.
- What is uncertain?Missing information, limitations and unresolved exceptions.
- What needs verification?The next check and the person responsible.
The Trust Ladder
Choose how much responsibility to delegate.
Loomiq’s Trust Ladder is a discussion framework for deciding the level of human involvement in a workflow. A higher rung is a choice that requires evidence and a suitable operating context.
These are working methods, not a certification or a universal score of system safety.
- Full supervision
A person reviews each action before it happens.
- Spot check
People review selected outputs against an agreed sampling plan.
- Exception review
The system flags defined situations for human attention.
- Auto-approve with undo
Permitted actions proceed with a tested recovery path.
- Full delegation
The defined task runs within agreed boundaries, with ownership and review still in place.
Be specific about the limits.
Hosting, model providers, data flows and retention need to be examined for each engagement. They belong in the system design and written agreements, alongside access and confidentiality requirements.
The approach here is not a promise of error-free AI, a security certification or a substitute for legal advice. Where specialist review is needed, that should be part of the scope.
One useful external reference is the voluntary NIST AI Risk Management Framework, which addresses trustworthiness across the design, development, use and evaluation of AI systems.