Claim Extraction

An AI answer about your product is typically a blend of true, stale, and invented statements delivered in one confident paragraph. Reading answers manually does not scale past a handful of prompts, and human reviewers miss subtle errors — a price that was right last year, an integration that was deprecated, a plan limit that changed.

Extraction decomposes each answer into atomic claims ('Product X starts at $49/month', 'X integrates with Salesforce', 'X is SOC 2 certified') that can be checked against ground truth. The classification that follows — accurate, outdated, false, missing — is what makes the output actionable: false claims need correction at the source, missing claims need content that states the fact extractably.

Over time, extracted claims become a dataset: which facts engines get wrong most often, which pages fix them, and whether a correction actually propagated into the answers. That feedback loop is the difference between publishing content and running GEO.