What should a firm do when AI gets something wrong?
Treat it as a work error with a record: contain, correct, tell, learn.

• Contain. Stop using the output. If it has been sent, ask for it to be held or recalled.
• Trace. Where did it go: a client, a court, a regulator, a decision, a file, another document? Who else used the same prompt or source?
• Correct. Replace the output with a verified version and correct the record.
• Tell. Inform those affected and those who need to know, quickly and in plain words. Professional and regulatory rules differ, so check yours.
• Find the cause, using the list below.
• Fix the process. Add the check that would have caught it, restrict the task if needed, retrain.
• Record. What happened, the impact, the cause, the fix, who was told and when.

• The source was wrong or out of date.
• The source was missing, so the model guessed. Models can produce content that sounds right but is fabricated.
• The system retrieved the wrong passage, or an old version.
• A scan was misread.
• The task was beyond what the model can do reliably.
• Nobody checked. OWASP calls excessive trust in model output “overreliance” and advises human oversight by reviewers trained to avoid it.
• Access was wrong: the answer used data the person should not have seen. OWASP gives a user receiving another user’s personal data as one scenario of sensitive information disclosure.

NIST’s AI Risk Management Framework includes, under its manage function, post-deployment monitoring that captures input from users and covers appeal, override, incident response and recovery, and asks for incidents and errors to be tracked, communicated and documented. NIST’s generative AI profile adds that logging, recording and analysing incidents helps information be shared with the right people.

A one-page form is enough: date, who found it, tool, what was asked, what was wrong, impact, who was told, cause, fix, owner and closing date. Review the forms every quarter and look for patterns.

In Mata v. Avianca, a US federal case, lawyers filed a brief containing cases that a chatbot had invented. The court’s sanctions order of 22 June 2023 imposed a $5,000 penalty and required letters to the client and to each judge falsely named as the author of a fake opinion. It was a US court, but it shows what an unchecked citation can cost anywhere.

ANVE is built to make errors easier to catch, not to make them impossible. Every answer is cited, so a reviewer can open the source passage. Each action item in meeting minutes must quote the transcript line it came from, and its owner must be confirmed by a person before it is approved; each confirmation is logged. An agent can take up to seven tool steps per request, and a claim made without a successful tool call is challenged. Every model call is logged by user, which helps trace who used what. The office still checks the work and owns the result.

No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.

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