The system does not know which company you are
Similar names, thin third-party references, and inconsistent identity data leave the model unsure it is describing you at all.
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AI Visibility Measurement · Jason T Wade · Central Florida
Does AI recommend your competitors?
Measure how AI understands your brand. Fix the gaps. Track what changes.
No provider can guarantee what an independent AI platform will cite or recommend.
Entity
Is it the right company?
Retrieval
Can your evidence be used?
Citations
Where do you appear?
Accuracy
Is the answer correct?
New · Authority Canon
One approved source. Everything else is built from it.
AI systems describe you the way your sources agree. We write your Book or Story in your own voice, then build every page, profile, and episode from it, in text, web, and audio editions.
The Book
Experts, professional firms, consultants, and founders with deep subject matter. 30–70 pages · 10,000–25,000 words.
The Story
Local, service, retail, and product businesses. 8–20 pages · 3,000–8,000 words.
The problem
Why does ranking in Google not mean AI recommends you?
Ranking in Google does not mean AI describes you correctly. Search rankings and AI answers are produced by different processes. A page can rank well and still be absent from the answer a buyer actually reads. There are usually four reasons for that, and they call for different fixes.
Your pages are not the evidence it uses
An answer is assembled from whatever the system can retrieve. If your own pages are hard to fetch, parse, or quote, something else fills the gap.
It is repeating something out of date
Old bios, retired services, and stale directory entries persist in the sources these systems reach for.
Competitors have stronger corroboration
When two brands are plausible answers, the one with more independent, consistent supporting evidence tends to be named.
What we measure
How do you measure AI visibility?
AI visibility is measured on four dimensions, tested against a documented prompt set. Every engagement starts from the same four readings, taken across ChatGPT, Google AI Overviews, Gemini, Claude, and Perplexity. These systems are non-deterministic, so we report ranges and trends across repeated runs rather than treating a single observation as a result.
Entity accuracy
Does the system identify the correct organization, with the correct people, location, category, and services attached to it?
Retrieval coverage
Can the pages and evidence that answer your buyers' questions actually be fetched, parsed, and used as a source?
Citation presence
Across a frozen set of questions, where does your brand appear, where does it not, and which sources appear instead?
Answer accuracy
When you are described, is the description correct, current, and complete enough to be useful to a buyer?
How it works
What does an AI visibility engagement involve?
An engagement is a four-stage cycle: measure, diagnose, implement, re-measure. Individual capabilities — entity architecture, schema, retrievable content, citation tracking — sit underneath this cycle rather than being sold as separate inventions.
- 01
Baseline
Freeze a prompt set for your category and record how each platform answers it today: entity accuracy, retrieval coverage, citation presence, answer accuracy, and which competitors appear.
- 02
Diagnose
Establish which constraint is actually binding — identity confusion, insufficient evidence, inaccessible content, weak third-party corroboration, technical structure, or category positioning.
- 03
Implement
Fix the priority issues: entity architecture, structured data, answer-ready content, internal linking, source development, and legitimate external authority building.
- 04
Re-measure
Re-run the original tests on a documented schedule and report what improved, what regressed, what stayed the same, and what cannot be attributed with confidence.
Methodology
How does BackTier run an AI visibility measurement?
BackTier is an AI visibility measurement and infrastructure firm founded by Jason T Wade, based in Central Florida and operating remotely. It measures how AI systems — ChatGPT, Google AI Overviews, Gemini, Claude, and Perplexity — identify, retrieve, cite, and describe brands, and repairs the evidence layer that determines those outcomes.
The figures below are the fixed parameters of the method. Definitions of the terms used are on the glossary.
| What is measured | Entity accuracy, retrieval coverage, citation presence, and answer accuracy — the four dimensions of the BackTier AI visibility framework. |
|---|---|
| Platforms tested | ChatGPT, Google AI Overviews, Gemini, Claude, Perplexity. |
| Prompt set | A frozen set of 40 to 60 prompts per engagement, built from category, service, location, buyer-role, and named-competitor questions, and held constant across cycles so results stay comparable. |
| Observation method | Each prompt is run 3 times per platform per cycle, in a clean session with no personalisation, and reported as a range and a trend rather than a single answer. |
| Baseline turnaround | 5 business days from the agreed start date to the written deliverable. |
| Re-measurement cadence | The same prompt set is re-run every 30 days during an engagement, and each cycle is dated. |
| What is reported | What improved, what regressed, what stayed the same, and what cannot be attributed to the work with confidence. |
| What is not claimed | No guarantee of a citation, a ranking, or a recommendation by any independent AI system. |
Evidence
What results does BackTier publish?
This is what we will publish, and what we will not. We are preparing case studies that meet our publication standard. Until they are ready, this site does not display anonymised percentage gains, client counts, or citation multiples, because those numbers cannot be checked by the person reading them.
A published result will carry the client or property identity, the starting baseline, the frozen prompt set, the platforms tested, the measurement dates, the work implemented, the later observations, what did not improve, and the limits on attributing any of it to our work. Anything run on BackTier-owned properties will be labelled as an internal experiment, not a client outcome.
No provider can guarantee what an independent AI platform will cite or recommend. Results vary by model, category, competition, and retrieval environment.
How material on this site is sourced, reviewed, and attributed is set out in our editorial policy, and anything we get wrong is logged publicly on our corrections page. Real test records, with prompts, platforms, and dates, are published in the AI answer audit.
Read the measurement standardWho it is for
Who needs an AI visibility baseline?
Professional and regulated services
Firms whose buyers ask AI systems for a recommendation, and where an inaccurate answer carries compliance or reputational cost.
B2B companies with real expertise
Organizations that already publish substantive material but are not being retrieved or cited when their category comes up.
Brands with identity confusion
Founders and companies being merged with a similarly named entity, described with outdated facts, or attributed to the wrong people.
Founder
Who is Jason T Wade?
Jason T Wade is the founder of BackTier and an AI visibility architect focused on entity resolution, retrieval, structured evidence, and the measurement of brand representation across AI systems. He developed the BackTier methodology and hosts the AI Visibility Podcast.
He is the author of AI Visibility: How to Win in the Age of Search, Chat & Smart Customers (August 2025) and hosts the AI Visibility Podcast. Based in Central Florida. Works remotely with clients across markets.
Podcast
What is the AI Visibility Podcast?
The AI Visibility Podcast with Jason T Wade publishes practical tests, conversations with practitioners, and case-based analysis of how AI systems select and describe sources. Episode count is not evidence of anything; the useful part is the findings and the sources behind them.
Browse episodesFAQ
Questions worth asking.
Everything you need to know about AI visibility, our methodology, and what working with BackTier looks like. Terms used here are defined in the glossary.
AI visibility refers to whether your brand is cited, recommended, or referenced when systems such as ChatGPT, Perplexity, Gemini, or Claude answer questions in your industry. Buyers increasingly begin their research inside AI assistants rather than on a traditional results page. A brand absent from those generated answers may therefore be absent from an important part of the buying journey — regardless of how well it ranks in traditional search.
Generative Engine Optimization (GEO) is the practice of improving the signals AI systems use to discover, understand, and cite a brand when they generate an answer. It combines entity clarity, so the system knows which organization it is describing; structured data, so facts are machine-readable; authoritative content that answers the questions buyers actually ask; retrieval accessibility, so those pages can be fetched, parsed, and quoted; and third-party corroboration, so independent sources agree with what you publish about yourself. GEO does not manipulate a model. It changes the evidence a model has available to work from.
Traditional SEO focuses primarily on ranking pages in search results, and it measures success in positions, impressions, and clicks. GEO focuses on helping AI systems retrieve trustworthy information, resolve it to the correct entity, and use it when generating an answer, and it measures success in entity accuracy, retrieval coverage, citation presence, and answer accuracy. The two disciplines overlap heavily — retrievable, well-structured, credible pages help both — but they measure different outcomes, and a page can rank on the first results page while being absent from every AI answer in its category.
It varies by entity, category, competitive environment, retrieval pathway, and platform behaviour, and no provider can guarantee when or where an independent AI system will cite a brand. In practice, entity corrections and structured data are the changes most likely to be reflected first, because they are the easiest for a system to re-read. Content that answers new questions follows. Independent third-party corroboration takes longest, because it depends on other people publishing. BackTier measures changes against an established baseline and reports what moved, what did not, and what cannot be attributed with confidence, rather than promising a fixed timeline.
We build platform-agnostic visibility infrastructure and measure across the systems buyers actually use: ChatGPT, Google AI Overviews, Gemini, Claude, Perplexity, Grok, and Microsoft Copilot. Because these systems are non-deterministic, the same prompt can produce different answers on different runs, so each prompt in the frozen set is run repeatedly per platform per cycle and reported as a range and a trend rather than a single observation.
A documented prompt set frozen for your category, built from category, service, location, buyer-role, and named-competitor questions. Testing of that prompt set across ChatGPT, Google AI Overviews, Gemini, Claude, and Perplexity. Findings on how your brand is identified and described, including inaccuracies and out-of-date facts. A competitor citation comparison showing which sources appear when you do not. A retrieval and source analysis of what the systems could and could not fetch from your properties. A prioritized remediation roadmap. And a clear recommendation about whether implementation work is warranted at all. It is a paid written deliverable, not a sales call.
If your buyers do not research your category in AI assistants, if you have no substantive published material for a system to retrieve, or if your immediate constraint is demand generation rather than how you are described, a baseline is not the right purchase yet, and we will tell you so. The same is true if you want a guaranteed citation, a promised ranking, or a fixed date by which an independent AI platform will recommend you — nobody can sell that honestly. A baseline is worth running when you already have expertise and content, and the problem is that AI systems are misidentifying you, describing you with stale facts, or naming competitors instead.
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A baseline is a written deliverable: a frozen prompt set of 40 to 60 questions, run 3 times each across ChatGPT, Google AI Overviews, Gemini, Claude, and Perplexity, delivered in 5 business days, with a prioritized roadmap and a clear recommendation on whether implementation work is warranted at all. Scope, format, and pricing are on the baseline page before you contact us.