# Complete AI Visibility Audit & Services

> Know your share of voice inside the machines — then grow it.

**Category:** Audits & Reporting · **Mode:** AI-Enabled, Manual

Rankings tell you about ten blue links. AI visibility tells you whether ChatGPT, Gemini, Perplexity, and Google's AI Overviews mention, cite, and recommend you — and against whom. This audit measures that 'share of model' and builds the plan to grow it, rather than leaving you guessing based on a slow, hard-to-attribute decline in inbound interest.

The scale of this shift is no longer speculative. Depending on the study, somewhere between a quarter and nearly half of all Google searches now trigger an AI Overview, and that share has moved in one direction — up — for eighteen consecutive months. Zero-click search, where the user's question is answered without a single site visit, is now the default outcome for a majority of US Google searches. None of that means people stopped searching; it means the answer moved from your page into the AI's response, and your brand either got pulled into building that answer or it didn't.

This service goes beyond a one-off score into ongoing capability: closing entity and citation gaps, earning mentions on the sources AI trusts, and structuring your content so it's the passage models actually quote — with a benchmark against named competitors so 'share of model' is a number you can track quarter over quarter, not a one-time vanity metric.

## Outcomes
- Your visibility and sentiment across the major AI engines for the prompts your actual buyers use
- A competitor 'share of model' benchmark in your category — who gets cited when you don't
- The specific citation, entity, and content gaps holding you back, ranked by fix effort
- A clear read on whether your gap is an access problem, an entity problem, or a content-structure problem
- An ongoing plan to become the cited answer, not just a periodically-checked score

## Deliverables
- **AI share-of-voice benchmark** — How often you appear vs. named competitors across a defined set of target prompts and engines — sampled consistently so the number is comparable over time.
- **Citation-source gap analysis** — The trusted sources AI pulls from (industry publications, Wikipedia, Reddit, YouTube, forums) where you're currently absent.
- **Entity & answer-structure fixes** — Making your brand and answers legible and quotable to LLMs — the same entity-clarity foundation that underlies Entity SEO, applied specifically to citation eligibility.
- **Sentiment & accuracy check** — Not just whether you're mentioned, but whether the AI's characterisation of your brand is accurate and favourable.
- **Ongoing visibility program** — Optional monthly execution and tracking so the benchmark keeps moving instead of being a one-time snapshot.

## Process
1. **Prompt-set design** — Build the specific buyer prompts that matter in your category — not generic 'best X' queries, but the phrasing real prospects actually use.
2. **Multi-engine measurement** — Sample answers across Google AI Overviews, ChatGPT, Perplexity, and Gemini; log mentions, citations, and sentiment consistently.
3. **Gap plan** — Entity, citation, and content actions ranked by impact, tied to the specific prompts where you're currently losing.
4. **Baseline for tracking** — Set the measurement methodology so future checks are directly comparable, not re-invented each time.

## Who this is for
- Brands watching AI answers replace their top-of-funnel clicks without a clear diagnosis of why
- Categories where buyers now 'ask AI' before they Google — B2B software, professional services, high-consideration purchases
- Agencies productising AI-visibility retainers for clients asking about this directly
- Brands that rank well on Google but suspect (correctly, in most audits I run) that they're invisible in AI answers for the same terms

## FAQ

**What is 'share of model'?**

How often and how favourably an AI engine surfaces your brand for the prompts your buyers actually use — the AI-era equivalent of share of voice, and a metric almost nobody was tracking systematically even a year ago.

**Which engines do you cover?**

Google AI Overviews, ChatGPT, Perplexity, and Gemini as standard; other engines (Grok, Claude, specific vertical AI tools) on request depending on where your buyers actually are.

**Can you run this monthly?**

Yes — it pairs naturally with Monthly AEO Services for ongoing tracking and execution, since AI answers shift often enough that a one-time snapshot decays.

**Is tracking AI citations actually reliable?**

It's directional, not exact — these systems aren't fully deterministic and answers vary by session. But sampled consistently against a fixed prompt set and methodology, the trend is genuinely trackable and comparable month over month, which is what matters for a program, not a single number.

**How is this different from the Agentic AI Readiness Audit?**

The Readiness Audit checks whether AI systems *can* access and understand you — the technical and structural prerequisite. This audit measures whether they actually *do* cite and recommend you for real prompts, and builds the ongoing plan to improve that outcome specifically.


## Related services
- [Detailed Agentic AI Readiness Audit Report](https://iliassami.com/services/agentic-ai-readiness-audit)
- [Monthly AEO Services (Retainer)](https://iliassami.com/services/monthly-aeo)
- [Answer / Ask Engine Optimization (AEO)](https://iliassami.com/services/answer-engine-optimization)


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