What is an executive digital reputation audit?

An executive digital reputation audit produces a defensible read of the current state across every layer stakeholders actually encounter, then translates that read into a prioritized set of interventions. Rather than a one-off check of name search, it maps the connected system, search, AI engines, Wikipedia, owned properties, social profiles, and the entity signals that tie them together, that determines what people learn about an executive before they ever click a link.

What the audit covers, layer by layer

• SERP composition, We run IMPACT™ against the executive’s name and priority queries to map the full Google results page, including AI Overviews, the Knowledge Panel, news boxes, and image and video results. These are the recognized building blocks of a modern name SERP.
• AI engine narratives, AIQ captures how each of the eight major AI engines: ChatGPT, Gemini, Copilot, Perplexity, Claude, Grok, Google AI Overviews, and Google AI Mode, describes the executive and which sources each is citing. Because the engines answer the same question differently and draw on different sources, each is reviewed on its own.
• Wikipedia and Knowledge Panel status, The Wikipedia article and Wikidata entry are reviewed for accuracy, sourcing, and structural quality. This layer matters disproportionately: Wikipedia feeds the Google Knowledge Panel and is paraphrased by AI engines as a canonical reference, and Knowledge Panel data is drawn from Wikidata, Wikipedia, and the Knowledge Graph.
• Owned-property authority: The corporate bio, the personal site if one exists, schema markup quality, and internal linking are assessed. Person schema with sameAs links connects an executive bio to their Wikipedia article and Wikidata ID, helping search and AI engines attach the page to the correct entity.
• Social presence: Social profiles are audited for completeness, consistency, and vulnerability. LinkedIn in particular ranks consistently well on name SERPs and is cited by AI engines for executive perspectives, while inconsistent bios across platforms introduce conflicting signals that weaken entity recognition.
• Entity signals, sameAs links, structured data, and third-party profile alignment are checked end to end. These are how Google and AI engines resolve scattered references, website, LinkedIn, Wikipedia, press mentions, to a single identity.

The deliverable

The output is a written report with prioritized recommendations and the underlying data, typically covering four to six structural interventions and a longer list of cleanup items.