Methodology

A structured system for AI visibility, not guesswork.

Be Cited applies 20 years of strategic communications experience to the AI search era, through a four-part methodology built on authority, clarity, structure and evidence.

Unlike many AI visibility or SEO approaches, we don't begin with recommendations. We begin by measuring what AI actually says about your brand today. One-off activity is unlikely to build durable AI visibility on its own; it relies on consistent signals: the same facts, the same narrative and the same proof of expertise, repeated across multiple trusted sources over time. That's why every engagement follows the same structured process rather than a list of ad hoc recommendations.

The four-stage methodology

Audit

Before recommending anything, we establish exactly how AI engines currently describe, represent and cite the brand. This runs on Be Cited's proprietary AI visibility tool, which puts real, structured prompts to the major AI engines and captures the actual answers, not assumptions about them.

  • Brand mention and citation rate across a structured set of real buyer questions
  • Comparison against named competitors on the same questions
  • Review of which sources and domains AI engines currently cite in the category
  • Classifying each citation source (owned, earned media, review site, directory, competitor, Wikipedia, Reddit, social or other) to show where authority is being built or lost
  • Visibility check on relevant community and discussion platforms such as Reddit, which can appear as sources in AI-generated answers alongside formal editorial coverage
  • Distinguishing between platforms that ground responses in live web sources and those that do not consistently expose live citation data, so each result is interpreted in the correct context
  • A prioritised list of the gaps most likely to be limiting visibility

Clarify

Once we know where a brand stands, we sharpen the narrative and authority signals that determine whether AI engines treat it as a credible source worth citing.

  • Reviewing how consistently positioning, facts and claims are presented across owned channels
  • Identifying gaps or contradictions that could create unclear or inconsistent signals about the brand
  • Sharpening core messaging so it is unambiguous enough for AI engines to summarise accurately
  • Aligning spokesperson positioning where a named expert voice strengthens authority

Structure

AI engines can only cite what they can understand. We organise content and information architecture so key facts and messages are easy for AI engines to read, understand and reference.

  • Auditing existing content for clarity, structure and machine readability
  • Recommending structural fixes such as clearer headings, defined entities and consistent terminology
  • Flagging where structured data and schema markup could improve machine readability, for a specialist to implement
  • Identifying where specialist SEO support may be needed to improve technical foundations, page structure or search visibility
  • Prioritising the pages and assets most likely to influence AI citation

Amplify

Structure alone isn't enough. Third-party validation can play an important role in how AI engines assess and represent authority, so we build the coverage and citation signals that reinforce authority beyond the brand's own website.

  • Strategic PR and earned media to build credible third-party mentions
  • Thought leadership and byline placement in outlets buyers trust
  • Genuine community and platform engagement where it builds authority, not link-building
  • Ongoing monitoring so citation signals compound over time rather than spiking once

Typical finding

Which sources is AI relying on?

Every Audit classifies each citation AI engines rely on, by source type. This reflects how AI engines source their answers: more often from specialist resources, vendors and review sites than from a brand's own content. The breakdown below is a composite pattern based on that research and on early Audit findings, not a single client's dataset. Your own Audit is built from your category's citation data specifically.

  • Specialist resources 32%
  • Vendors & competitors 24%
  • Review sites 18%
  • Government & official 14%
  • Reddit & forums 8%
  • Brand's own content 4%

Composite pattern based on source-type findings across multiple audits, not a single client's result.

This isn't a visibility problem. It's an authority problem.

How this differs from a typical AI visibility or SEO engagement

Traditional approach

  • Recommendations first
  • Generic best practice
  • Technical fixes

Be Cited

  • Measure first
  • Evidence from your own market
  • Communications, authority and structure together

Why this works

Built for how AI answers now, and how search is changing.

When people use AI to research a question, they increasingly receive a synthesised answer rather than relying on a traditional list of search results. Being one of the sources an AI system selects and cites is becoming increasingly important alongside traditional search visibility.

That's why this methodology doesn't stop at technical fixes. Structure can help content be found and understood, but it may not be enough on its own to establish the authority signals associated with citation. Authority is reinforced beyond the brand's own website, through consistent narrative, credible third-party coverage and genuine presence in the places buyers and AI engines both look for consensus. Each stage of this method builds toward that, in order, rather than treating AI visibility as a one-off technical audit.

See how this applies to your brand

The AI Visibility Review is the fastest way to see where you stand today, and which stage of this methodology would move the needle first.

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