How the work is produced

AI Visibility Report Methodology

AI Presence Report is a structured review of public evidence and observed AI-assisted discovery results. It is designed to identify visibility gaps and practical next steps, not to promise rankings or recommendations.

Scope

The scope is agreed before research begins so the findings stay relevant to the customer’s actual decision.

  • Business identity, category, services and location signals
  • Priority buyer-intent questions and observed AI-assisted responses
  • Website content, crawlable service evidence and technical accessibility
  • Independent sources, reviews, citations and relevant third-party profiles
  • Differences between what the business says and what public sources support

Process

  1. Confirm the business, service area and priority buyer questions.
  2. Review the website and major public profiles using a dated evidence log.
  3. Run a defined set of AI-assisted discovery checks and record the responses and cited sources.
  4. Separate verified facts, missing evidence and inconsistent claims.
  5. Prioritize corrections by customer relevance, evidence strength and owner control.
  6. For Monthly Monitor customers, repeat the defined checks and distinguish durable changes from one-off variation.

Evidence standards

Material observations are tied to a dated, reviewable source. Facts, interpretations and recommendations are kept separate. Missing information is labeled as missing or unclear rather than inferred. Customer-provided facts are distinguished from independently observable evidence.

Limitations

  • Results are point-in-time observations and may vary by platform, model, location, personalization and date.
  • The report uses public information and does not require access to private accounts.
  • No ranking, citation, recommendation, traffic or revenue outcome is guaranteed.
  • Recommendations focus on accurate, useful public evidence and do not include manipulative link or review tactics.

Quality review

Before delivery, names, links, pricing labels, calculations and high-priority claims are checked for internal consistency. Recommendations must be specific enough to assign an owner and review date. Monitoring work uses the same defined comparison set so changes can be evaluated against a stable baseline.