AI with boundaries, evidence and a human owner.

Purpose and risk

We define the intended decision, affected people, unacceptable outcomes and a safe fallback before selecting a model.

Data flow

Sources, personal data, retention, access, provider region and training use are documented. Customer data is excluded from training unless explicitly agreed.

Evaluation

Representative examples, failure cases and regression thresholds turn quality into evidence rather than a demo impression.

Human review

High-impact or uncertain outcomes remain reviewable and reversible. Interfaces show sources and uncertainty where useful.

Operations

Latency, cost, model changes, errors and outcome quality are monitored after release. Provider substitution remains an architectural option.

Compliance

DPA, DPIA, logging and AI Act responsibilities are addressed in proportion to the actual role and risk. This is engineering context, not legal advice.

Last materially updated: 24 August 2026.