Audit AI Company Facts Versus Industry Context
Build a claim-attribution ledger that keeps sector trends, account evidence and sales hypotheses from becoming interchangeable.
10/24/20263 min read
An industry report says that food distributors often struggle with seasonal staffing. A company page says that one distributor operates two depots. Neither statement establishes that this particular distributor has a staffing problem. When an AI brief combines them into “the company is struggling to staff its depots,” it has crossed an attribution boundary.
Test that boundary with paired passages: one about an identified account and one about the surrounding industry. The goal is not to suppress useful context. It is to keep the context from acquiring a company name and becoming an unsupported sales claim.
Give every passage an evidence level
Use three labels in the input packet: account-specific observation, industry-level context and analyst hypothesis. Keep the labels attached during extraction, summarization and editing. A hypothesis can be useful, but its evidential status must not improve merely because it appears in a polished paragraph.
The evaluation task should request two sections: “What is established about this account?” and “What remains a question?” Do not require a pain-point section unless the source actually establishes one. A compulsory problem field can encourage the system to invent the commercially convenient answer.
A paired-passage audit
Consider fictional Ash Grove Packaging. Passage A1, its own supplied company description, says it manufactures reusable transport crates and operates plants in Leeds and Cork. Passage A2, a synthetic account announcement, says the Cork plant plans to add a second shift after commissioning a line. Neither mentions vacancies, staffing difficulty or a purchasing project.
Passage I1, a synthetic industry overview, says some packaging manufacturers use temporary workers during seasonal peaks. Passage I2 says manufacturers may assess scheduling tools when shift patterns change. These statements describe possibilities across a sector, not Ash Grove's plans.
Fill the attribution ledger as follows:
C1: “Ash Grove operates in Leeds and Cork.” Evidence A1; account fact; supported within the supplied packet.
C2: “The Cork plant plans a second shift after line commissioning.” Evidence A2; account plan with dependency; supported if the condition remains.
C3: “Ash Grove depends on temporary workers.” Evidence I1 only; account attribution unsupported; remove.
C4: “Shift scheduling may be worth asking about.” Evidence A2 plus clearly labeled analyst interpretation; hypothesis, not a confirmed need.
C5: “Ash Grove is purchasing scheduling software.” Evidence I2 only; unsupported purchase inference; reject.
The finished brief can contain C1, C2 and C4 with their distinct labels. It should not contain C3 or C5 as company facts. This ledger is an original evaluation artifact; no research on a real company or actual model result is being reported.
Test the boundary by changing one passage
Remove A2 while leaving the industry overview unchanged. A proposed question about second-shift scheduling should lose its account-specific justification. If the model still states that Ash Grove is adding a shift, the answer is not grounded in the supplied packet.
Next replace I1 with a different industry's staffing context. The account facts should remain stable, while any contextual interpretation should change or disappear. Finally, add an explicit account statement that temporary labor is not used. The summary must preserve that account evidence rather than allowing general sector language to override it.
These controlled edits are more informative than asking whether the whole brief “sounds relevant.” They reveal which conclusions move with the evidence and which appear to come from an assumed industry narrative. Keep unchanged fields unchanged so the reviewer can isolate the attribution defect.
Choose the disposition of every claim
Mark each claim supported, hypothesis, contradicted or not established. Do not use “likely” as a universal repair. “Likely purchasing software” still communicates a purchasing inference without the required support. A useful alternative is a question that admits the answer could be no.
NIST recommends checking generated sources and citations. Here the reviewer checks not just whether a source exists, but whether its subject matches the claim. If a source is unavailable, leave the claim unverified rather than laundering it through an industry citation. The final account note should explain what is known and what would need confirmation before outreach, without presenting background knowledge as private insight into a buyer.
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