Confidently wrong is the real risk.
The dangerous failure is not AI that refuses. It is AI that reads more expert than the truth, and in a regulated setting that is exactly what a reviewer waves through. We build the controls that catch it, before anyone sees the output.
Every model is trained on someone else's law.
A model writing your local content drifts to the patterns it saw most in training, so a system generating South African compliance text reaches for the GDPR clause, not the POPIA one. The wrong answer reads more sophisticated than the right one, which is why a reviewer skimming is reassured by exactly the text that would embarrass them. Courts and regulators are now sanctioning hallucinated citations across jurisdictions. "AI-generated" is no longer a neutral phrase.
One loop. Four stages.
It runs continuously, not once, because models change under you, retrieval goes stale, and the law moves. Each stage maps to how enterprise AI assurance is actually done: test and evaluate, ground, gate, and monitor.
Expose
- Map the AI surface and its failure classes.
- Red-team and test: a golden set plus adversarial cases.
- Score outputs for groundedness and factuality.
- Rank error classes by consequence.
Offer: the Confidence Audit
Ground
- Ground the system in authoritative sources.
- Require a real citation for every claim.
- Design the retrieval boundary: answer from authority, reach nothing it must not.
- Define where the model may and may not point.
Offer: Grounding & Boundary Design
Gate
- Executable controls that fail closed.
- Machine checks: every claim resolves to a real primary source.
- Self-correction loops that revise, not ship.
- Wired into CI with frozen baselines.
Offer: Control Build
Monitor
- Continuous test, evaluation, verification, validation.
- Score live traffic; detect drift.
- A short written position each quarter a committee can minute.
Offer: Standing Assurance
We ship these controls. We don't describe them.
Machine-policed legal content
Checks that catch GDPR concepts wearing POPIA citations, and sections that do not exist in the Act, with frozen baselines.
Sign-off bound to the artefact
Human approval bound to a content hash, so approving one version cannot silently approve another. Spend governed per unit.
Gates between every stage
A staged pipeline with QC gates, and a machine-readable contract between systems instead of one parsing another's prose.
An instruction to verify is not a verification step
From our own codebase. It is why we build gates that fail closed, not prompts that ask nicely.
We name our limits.
A practice that names its own limits is the one a legal gatekeeper trusts. Here is what we will not do, and what we do instead. It is a selling point, not a disclaimer.
Where can your AI be confidently wrong?
Take the Confidence Assessment. Score your exposure across the ways AI output fails in a regulated setting, in minutes. No email required.