How to Check Whether an AI's Competitor Claims Are True

Not every claim needs checking. In a typical AI-produced competitive summary, the risky content is concentrated in a handful of sentences: the ones containing numbers, dates, named sources or quotes. Everything else is interpretation, where being slightly off costs little.

That concentration is what makes a routine practical. Marking the specifics and checking those, rather than re-researching the whole document, turns verification into a few minutes rather than a second research pass.

It also improves the output beyond accuracy. Forcing every specific to carry a source and a date produces a document someone can defend in a meeting, which is usually the actual requirement.

Prices and plan limits first. They change without notice, they are the most likely to be quoted from stale training data, and they are the most damaging to get wrong because a prospect will know. Open the pricing page, confirm, note the date.

Dates and recency second. Anything phrased as recently, currently or this year should be checked against the source, since a model has no reliable sense of when now is. Launch dates and version claims belong here.

Cited statistics and named studies third. If a figure is attributed to a research firm or a report, confirm that the source exists and says that. Misattributed or invented citations are a documented failure mode and they are the most embarrassing kind in a leadership document.

Quotes fourth. If a sentence is presented as a customer or reviewer statement, find the sentence. Paraphrases presented as quotes are both inaccurate and a reputational risk when the subject is a named company.

For prices, the primary source is the competitor pricing page, not a comparison article. For product claims, the competitor own documentation or product page. For ad activity, the ad library. For reviews and quotes, the review platform itself. In every case the rule is the same: primary source or nothing.

Record the verification inline rather than in a separate log. A short parenthetical with the date is enough, and it survives copy-paste into other documents, which a separate log does not.

When you cannot verify something, do not delete it silently. Mark it as unverified and keep it as a hypothesis, because the underlying observation may still be worth investigating and a deleted claim tends to reappear later without the caveat.

Most of these errors come from asking a model for facts it never had. If the assistant is reasoning over data you supplied, or querying a system that holds real records, fabrication mostly disappears and the remaining risk is ordinary misinterpretation.

So the structural fix is to separate collection from interpretation. Automated collection keeps the facts current, the model summarises and compares that record, and verification collapses to spot-checking rather than auditing every sentence.

Oppira attaches sources to the insights and answers it generates, which is what makes this practical: the check is opening the linked source rather than searching for one. Connected over MCP, an assistant queries that stored record instead of recalling from training data.

Nothing with a number leaves the team without a source and a date. It sounds bureaucratic and it takes about fifteen seconds per number, which is far less than the cost of one wrong price quoted to a prospect.

Apply it symmetrically to your own claims too. Internal decks accumulate figures about your own performance that nobody can trace either, and the same discipline fixes both.

Then review the failures. If a particular category keeps needing correction, that is a signal about how you are using the tool rather than about the tool, and usually the fix is supplying the data rather than asking for it.