Responsible AI
Responsible AI
AI systems fail differently from ordinary software. Our approach to responsible AI is practical: keep a human in the loop where it matters, ground answers in real sources, measure quality, and fail safely.
Human oversight and escalation
- Keep a human in the loop for consequential decisions.
- Route to a person, with context, when the system is uncertain.
- Put a clear checkpoint before any irreversible action.
Evaluation and reliability
- Measure quality against real inputs, not cherry-picked demos.
- Monitor for drift once a system is in production.
- Be explicit about what a model can and cannot do.
Grounding and transparency
- Ground answers in retrieved sources and attach citations.
- Build in AI disclosure, so users can be told when they are talking to an AI system.
- Keep an audit trail so decisions can be traced.
Privacy, permissions, and bias
- Enforce permissions so users only see what they may access.
- Handle personal data with care and minimise what is used.
- Watch for bias and evaluate for it where it could cause harm.
FAQ
Common questions
Can a person review or approve what the AI does?
Yes. We build human approval into workflows where the stakes justify it, so a person signs off before consequential actions happen.
How do you reduce hallucinations?
For knowledge systems we ground answers in retrieved sources, attach citations, and evaluate whether responses are actually supported by those sources.
What happens when the system is unsure or fails?
We design it to escalate to a person with context and fall back safely when it reaches the limits of what it was built to handle.
Do you use our data to train models?
We do not use your data to train our own models. It is used to operate the system we build for you, and we prefer AI providers and settings that do not train on customer data.
Questions about Responsible AI?
We are happy to walk through how this applies to your project.