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Useful AI needs boundaries, ownership and review.

We design AI features as part of a controlled software system, with explicit inputs, permissions, expected outputs, review points and fallback behaviour.

Principles

  • Use AI for a defined business task, not because a model is available.
  • Minimise data exposure and align providers and deployment with project requirements.
  • Keep high-impact actions reviewable or approval-controlled.
  • Measure quality using representative tasks, not demos alone.
  • Log and monitor AI-assisted workflows where appropriate.
  • Design fallback behaviour for unavailable or low-confidence outputs.
  • Never present model output as guaranteed fact.

Questions

Do you train models on customer data?

This is defined per project and provider. We make no blanket statement: the data-use terms of the chosen provider and the engagement agreement apply and are documented in the project.

Can AI actions require approval?

Yes. Approval gates can be mandatory for selected workflows.

Can AI be disabled?

Features can be architected with controlled fallback paths where the process needs continuity without AI.