We already track a handful of prompts every week. Why add this?

Keep tracking. Weekly tracking shows what came back on those runs. Without many answers per need and a re-asked sample, a real shift and normal variation look the same. The check gives you a baseline built from many answers on a pinned model panel, so you can judge later movement against it.

What do we do with a "Too close to call" result?

Treat it as a finding. It means the data does not support ranking you above or below that competitor. The position is open, and the Monday plan shows which work could move it.

My CEO wants one number. Can we give them one?

Yes. The Verdict gives one headline readiness score from 0 to 100, with a two-sentence verdict and three pillar scores beside it. The Methodology footprint shows what the number rests on when someone asks how sure you are.

Our customer research tells a different story. Which one is right?

Both can be accurate. Your research measures what customers believe. The check measures what AI assistants believe about your market. Where AI's version differs, AI's version is what buyers are told when they ask. That gap is worth knowing about.

Can we run it again to see if our work paid off?

Yes. The check is a one-off audit you can repeat later. Because the panel is pinned to dated model snapshots and a slice of questions is re-asked, a later run measures movement in the models rather than drift in the method. The comparison holds when both runs use the same scan setup (same model panel, question set and scoring version), and the report records that setup in its Methodology footprint.

Who sees our results?

You do. Customer results are never published. Your results belong to your brand.

AI visibility, measured with statistics

Ask an AI the same question twice. You can get two different answers.

AI assistants write every answer fresh, so one answer says little about the next. The AI Readiness Check by Pure Neo asks each buyer need in several phrasings across a panel of AI models, re-asks a sample of questions, and reports where your brand stands, marking differences too small to be real as too close to call.

Configure your check See a sample report

Checks start at $1,890. The configurator shows the exact price for your scope before you pay. Your results are never published.

The screenshot problem

Someone sends you a screenshot. An AI assistant was asked for the best options in your category, and the answer names your brand. Or it names a competitor and leaves you out. The screenshot goes into a deck as evidence.

Then you type the same question yourself. The list changes. A different brand comes first. Yours appears, or it doesn't.

That uneasy feeling is the right instinct. AI assistants generate a new answer each time. The same model, asked the same question on the same day, can name different brands in a different order. A screenshot captures one of those answers. A weekly check of a few prompts captures a few more. Both are anecdotes.

If you can't tell a typical result from a lucky one, you can't answer the questions that matter:

Is this what buyers usually see, or one good run?
Did we gain ground since last quarter, or did the answers land differently this time?
Is that competitor ahead of us, or are we within normal variation?
Does every AI assistant say this, or only one of them?

GEO and AEO plans built on anecdotes are hard to defend in a budget review and impossible to check afterwards.

Why the usual approach can't answer this

Search tools were built for search results, where a position is stable enough to track. SEO suites and rank trackers do that job well. Keep them, and keep the partners who run them.

AI answers vary from run to run, so any single answer is one point in a spread of possible answers. Reading that one answer as a ranking applies search logic to something that moves. AI visibility checkers and prompt trackers are a useful quick look. A sample of a few runs shows what came back. It can't show how much the result shifts from one run to the next.

There is a second source of variation. When an assistant searches the web live, part of its answer depends on what that search returned that day. The check measures what the models themselves hold about your brand and your market, separate from what a live web search returns on a given day. That is the lasting belief that shapes answers across many conversations. A search result may be gone tomorrow.

That lasting belief is what counts. AI's version of your market is what buyers are told. Where it differs from what is true, or from your own research, the AI version wins over time.

Been there, done that.

The check was built by a designer with two decades inside the SEO and content playbook for some of the biggest brands in the world. The playbook worked and still works. It also shaped the web AI learned from, and we now deal with its side effects.

Designed digital products for Allianz, MetLife, JPMorgan Chase, BlackRock, Nestlé and Porsche.

What the AI Readiness Check measures

The check treats AI visibility as a measurement problem. A fixed panel of leading AI models is asked a large set of realistic buyer questions about your market.

Many answers per need. Each buyer need is asked in several phrasings, and every model in the panel answers each one, so your result is built from many answers. One lucky or unlucky answer can't decide it.

A re-asked sample. A slice of questions is deliberately re-asked, so the result rests on more than one pass.

Too close to call. The check uses statistics, so when the difference between your brand and a competitor is too small to be real, the report labels it Too close to call instead of showing it as a lead. It ranks what the data supports, and nothing more.

Model split. Results are broken down model by model, so you can see whether a pattern holds across the panel or comes from one AI assistant.

Methodology footprint. Every report describes how the scan was run. The panel is pinned to dated model snapshots and a slice of questions is deliberately re-asked, so a re-scan measures movement in the models rather than drift in the method. Anyone who reads your report, from your CFO to your agency partner, can see what the numbers rest on.

Alongside the measurement, a technical audit checks whether AI models can reach, read, and extract your website and product information: structured data, machine-readable specs, crawlability, and llms.txt (reported as informational). Each failing check comes with fix text.

How it works

Configure your scope. Choose your market and add the competitors you want named in the comparison.
See your price. Checks start at $1,890. The configurator shows the exact price for your scope before you pay.
The scan runs. Every model in the panel answers each buyer need in several phrasings, and a slice of questions is re-asked. Your result is built on 7,000+ analyzed data points. The technical audit checks your site at the same time.
Get the report and the Monday plan. Nine sections, each answering one buyer question, including a ranked to-do list.

What you get

A report that separates what the data supports from what is too close to call. The sections that matter most when you need numbers you can defend:

Verdict. Where do we stand? One headline readiness score from 0 to 100, a two-sentence verdict in plain English, and three pillar scores.
Visibility map. Where do we show up in AI answers, and where don't we? Buyer topic by buyer topic.
Model split. Does this differ model by model?
Competitive picture. Who owns the answers we're missing? A head-to-head difference too small to be real is marked Too close to call.
Methodology footprint. How the scan was run, on pinned model snapshots with a re-asked slice of questions, so a re-scan measures movement in the models rather than drift in the method.
Monday plan. One ranked to-do list. Each task has an owner, whoever runs your website, owns your content, or owns your positioning, and a link to the evidence behind it.

The report also covers Machine access (can AI models read our website, and how do we fix what they can't), Whitespace (buyer topics no brand owns yet), and Perception (what AI says about us, laid out so you can hold each finding against reality and fix what is wrong or thin).

Pure Neo measures. We don't sell content packages or backlinks. The Monday plan goes to whichever team or agency does the work.

Questions

We already track a handful of prompts every week. Why add this?: Keep tracking. Weekly tracking shows what came back on those runs. Without many answers per need and a re-asked sample, a real shift and normal variation look the same. The check gives you a baseline built from many answers on a pinned model panel, so you can judge later movement against it.
What do we do with a "Too close to call" result?: Treat it as a finding. It means the data does not support ranking you above or below that competitor. The position is open, and the Monday plan shows which work could move it.
My CEO wants one number. Can we give them one?: Yes. The Verdict gives one headline readiness score from 0 to 100, with a two-sentence verdict and three pillar scores beside it. The Methodology footprint shows what the number rests on when someone asks how sure you are.
Our customer research tells a different story. Which one is right?: Both can be accurate. Your research measures what customers believe. The check measures what AI assistants believe about your market. Where AI's version differs, AI's version is what buyers are told when they ask. That gap is worth knowing about.
Can we run it again to see if our work paid off?: Yes. The check is a one-off audit you can repeat later. Because the panel is pinned to dated model snapshots and a slice of questions is re-asked, a later run measures movement in the models rather than drift in the method. The comparison holds when both runs use the same scan setup (same model panel, question set and scoring version), and the report records that setup in its Methodology footprint.
Who sees our results?: You do. Customer results are never published. Your results belong to your brand.

Bring a measurement to the next meeting

The next time someone shares a screenshot of an AI answer, you can set it next to a result built from many answers per buyer need, with a re-asked sample, differences too small to be real marked too close to call, and a record of how the scan was run. Checks start at $1,890. Configure your scope, and the configurator shows the exact price before you pay. The report includes a plan your team can start on Monday.

Configure your check

Questions before you buy? Email us