AI VISIBILITY AUDIT

AI Engines Are Already Deciding Which Brands Get Recommended. Is Yours on the List?

American consumers are using ChatGPT, Perplexity, Google Gemini, and Copilot to research products, compare services, and shortlist vendors — and the brands that appear in those AI-generated answers are capturing intent before a single click happens. This audit tells you exactly where your brand stands across 12 AI engines, scored across three measurement dimensions, with a competitive gap analysis and a prioritised 90-day action roadmap — all delivered in 5 working days.

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This is for you if

For American businesses that want evidence, not speculation, about their AI search position.

You have never formally measured your AI visibility. You may have strong Google rankings, a well-maintained website, and an active content program — but you have never run a structured test to determine what ChatGPT, Perplexity, or Gemini says when someone in your market searches your category. That gap is significant. Google rankings do not predict AI citation rates, and organic visibility does not translate automatically into AI brand mention share. The audit measures what Google Analytics cannot.

You are preparing to invest in AEO, GEO, or LLMO work. You have budget earmarked for AI search optimisation — or you are building the business case to secure that budget. Either way, committing resources without a diagnostic baseline means you cannot measure return, cannot prove improvement, and cannot defend the investment when stakeholders ask what it produced. The audit is the before-state that makes every subsequent dollar accountable.

You lead a marketing, growth, or revenue team that needs data for internal alignment. US marketing and revenue leaders frequently face the same internal challenge: the instinct that AI search matters is strong, but the data to justify specific investment levels is absent. The audit produces five scored metrics, a competitive benchmark, and a prioritised action plan — the exact evidence package needed to secure sign-off and align teams around a clear direction.

You have already begun AI visibility work and want to quantify its impact. You have updated schema, produced structured FAQ content, done entity-building work, or started a citation-focused PR program. But if you started without a formal baseline, you have no documented before-state to compare against. The audit scores your current position so that your next round of work is measured against evidence rather than intuition.

What's broken

The problem is not that AI search is complicated. The problem is that US businesses are optimising without a baseline.

There is no existing metric for AI visibility.

Your AEO citation rate — the percentage of relevant AI queries that cite your brand as a source — does not appear in Google Search Console, Google Analytics, or any existing analytics platform. Neither does your GEO brand mention share or your LLMO accuracy score. These metrics require specific, structured testing across the engines that are actually serving AI-generated answers to your customers. Without them, you are managing a growing channel with no performance data.

Optimisation spend is not prioritised by evidence.

US businesses are beginning to invest significantly in AI search — content production, technical schema work, PR for citation building, entity development. These are all potentially high-value investments. But without a diagnostic that shows which gaps are largest and which fixes have the highest impact-to-effort ratio, spend gets distributed based on what seems important rather than what the data shows is most urgent. The result is diluted effectiveness.

Competitors may have already established a meaningful AI citation advantage.

In US markets where AI search adoption is high — SaaS, financial services, healthcare information, legal services, real estate, e-commerce — some businesses have been deliberately optimising for AI citation for over a year. The competitive gap analysis in this audit compares your citation rate and brand representation against three competitors. If a competitor is appearing in AI-generated answers to queries that should surface your brand, that is a direct revenue risk — and you cannot close it without first knowing it exists.

AI engines may be misrepresenting your brand.

The LLMO accuracy score in this audit does not just measure whether you appear in AI answers — it measures the accuracy of what those answers say about you. US businesses with complex product lines, multiple locations, recent acquisitions, or recent rebrands frequently discover significant inaccuracies in AI engine knowledge: wrong product descriptions, outdated pricing references, confused entity relationships, missing service lines. These inaccuracies affect conversion at exactly the moment a prospect is using AI to evaluate their options.

What we engineer

A 47-point diagnostic across 12 AI engines — built to give you the data your competitors do not have.

Full 47-Point Audit Report

all tested engines, all findings, all scores, with evidence documented for every check

Competitive Gap Analysis

a scored comparison of your citation rate and brand representation against three nominated competitors, with gap size quantified for each query type

Entity Health Score

an assessment of how completely and consistently your brand entity is represented across AI knowledge sources, Wikipedia, structured data, and citation networks

Schema Quality Audit

a review of your current structured data implementation against standards for AI readability and citation behaviour

AEO / GEO / LLMO Score Card

your three core AI visibility scores in one document, with US industry benchmarks where available

Prioritised Action Plan

a 90-day roadmap with every recommendation ranked by impact and effort, structured for immediate execution

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What changes

The audit changes your position from reactive to data-led — across every AI visibility decision you make from this point forward.

Before
After
Before You have a scored, documented baseline.
After Your AEO citation rate, GEO brand mention share, and LLMO accuracy score exist as evidence-backed numbers for the first time. Every future optimisation effort — schema improvements, structured content, entity corrections, citation-building PR — can be measured against these numbers. This is how you demonstrate ROI on AI search investment, which is the metric that matters most to US marketing and revenue leadership.
Before You have a competitive intelligence advantage at the query level.
After The competitive gap analysis does not tell you in general terms that a competitor is "ahead in AI search." It tells you which competitors are being cited on which specific query types, at what citation rate, across which engines. That query-level specificity is what makes the gap analysis immediately actionable — you know exactly where to direct effort first.
Before You have a prioritised action plan built for execution.
After Every finding in the 47-point audit is mapped to a specific recommended action. Every action is ranked by impact and effort. The 90-day roadmap is structured so your team can begin on day one — the first 30 days address the highest-leverage, fastest-to-implement improvements; the following 60 days cover structural changes that build compounding AI visibility. This is an operational document, not a strategic overview.
Before You have the evidence to align leadership, secure budget, and direct your team.
After US marketing and growth leaders consistently use the audit output to do three things: justify AI search investment to a CFO or board, align internal teams around a specific set of priorities, and brief external partners on exactly what needs to be built. The score card and competitive benchmark provide the quantified evidence layer that makes those conversations productive and specific.
Common questions

FAQ

What exactly does the AI Visibility Audit include?

The audit includes 47 distinct diagnostic checks across 12 AI engines, covering your AEO citation rate, GEO brand mention share, LLMO accuracy score, entity health, schema quality, and a scored competitive gap analysis against three competitors. Every check is documented with raw evidence — the actual AI-generated responses that support each finding — and the complete output includes a prioritised 90-day action roadmap. This is a full diagnostic of your current AI search position, not a checklist or a general content review.

How long does the audit take from start to delivery?

The audit is delivered in 5 working days from the point your brief is confirmed. Day 1 is brief confirmation and scope setup. Days 2 and 3 are structured testing across 12 engines and competitive analysis. Day 4 is scoring and diagnosis. Day 5 is action plan completion and delivery, followed by a 60-minute walkthrough call. There is no discovery phase that extends the timeline — we begin the day your brief is complete.

What does the audit cost in USD?

The AI Visibility Audit is priced at USD 2,200 for the standard scope, covering one brand, three competitors, and all 12 AI engines. This includes all six deliverables: the full 47-point audit report, competitive gap analysis, entity health score, schema quality audit, AEO/GEO/LLMO score card, and the 90-day prioritised action roadmap. Custom configurations — additional competitors, expanded query sets, or vertical-specific engine weighting — are quoted on request.

What happens after the audit is delivered?

You receive the full report and action plan, and we walk through every major finding in a 60-minute delivery call. From there, you implement the action plan using your own team or agency partners, or you engage Ignited Nepal to execute the priority items through an ongoing AI visibility optimisation engagement. The audit is a complete standalone product — there is no automatic continuation. Most US clients move the first 30-day action tier to their development or content team immediately and make a decision about ongoing support based on early implementation results.

Which AI engines are tested?

The audit tests 12 engines including ChatGPT (GPT-4o), Google Gemini, Perplexity AI, Microsoft Copilot, Claude, Meta AI, You.com, Brave Leo, and additional platforms selected based on US market usage data and your audience profile. We do not limit testing to the two or three most prominent platforms. Citation behaviour and brand representation vary materially across engines — a brand well-cited on Perplexity may be absent or inaccurate on Copilot, and capturing that full picture is the reason the 12-engine scope exists.

Does the audit include a strategy, or is it only a diagnostic?

The audit includes both a complete diagnostic and a structured action plan — these are delivered together as a single product. Every finding in the 47-point framework is mapped to a specific recommended action. Every action is ranked by its impact-to-effort ratio and placed into a 90-day roadmap. The audit tells you what is wrong, why it matters, and what to do about it in what order. What it does not include is implementation — executing the schema fixes, content updates, entity corrections, and citation-building activities is available as a separate engagement. The strategic direction is fully contained in the deliverable you receive at the end of 5 working days.

Our team

The people behind the work

Not a black box. Real specialists you can call, with their names on the work.

Niraj Raut

Niraj Raut

Founder — Ecommerce SEO
Keshab Joshi

Keshab Joshi

PPC Expert
Hawrry Bhattarai

Hawrry Bhattarai

Google Ads Expert
Arogya Rijal

Arogya Rijal

SaaS SEO Expert
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Five working days from now, you could know exactly where your brand stands in AI search.

Most US businesses that complete the AI Visibility Audit are surprised by two things: how specific the gaps are, and how actionable the roadmap is. There is no vague "improve your content" recommendation here — there is a scored diagnostic, a competitive benchmark, and a ranked 90-day plan that your team can begin executing immediately.

Run an AI Visibility Audit →

Run an AI Visibility Audit