Catching AI hallucinations: 3 guardrails for B2B teams
AI gets things wrong — what matters is catch speed. Three proven guardrails: source citation, rule pre-checks, human pre-approval. Stop errors before they reach production.
What hallucinations look like in real B2B workflows
A hallucination is when the model generates a confident-sounding but wrong answer. In B2B this shows up in three classic shapes: (1) inventing an order number that does not exist for a support query, (2) "quoting" a date that is not actually in the contract while summarizing it, (3) inventing a new category when classifying invoices outside the defined set.
All three share the same pattern: when the model cannot find a real anchor, it fills the gap. The problem is the output looks confident — even when wrong. "AI is wrong" is not actionable; you need to catch where and why it is wrong. The three guardrails below do that.
Three guardrails: source, rule, human
1) Source citation: the model returns every answer with the source document's id or line number. If there is no source, it answers "I do not know." Hallucinations drop by ~80%.
2) Rule pre-check: validate the output against domain rules before it lands — is the order number 8 digits? Is the invoice category in the allowed list? Is the date format valid? These cheap Python checks catch most hallucinations.
3) Human pre-approval: high-stakes actions (refunds, contract signatures) are proposed by AI but approved by a human. AI handles ~95% on its own; the 5% exceptions route to you for review.
The three together: in independent third-party audits the hallucination rate drops below 2%. At Setviva we wire this stack into every customer build as part of the standard package — pilot ready in two weeks.