The deck has three chats and no list of prompts
We stop the deck. We lock the set first. A vibe is not a score.
AI Search Visibility
AI Search Visibility from SEOConsultants.ai helps your brand get found, cited and recommended in ChatGPT, Gemini, Perplexity and Google AI Overviews. We start with a free audit and a 90-day plan built around your goals.
Our process
We fix the prompts, platforms and test date up front, so results are a fair sample rather than a cherry-picked one.
We run every prompt on that date and label each result by location, account and product mode.
We hand the results to your team, so decisions are based on data instead of anecdotes.
Position
Someone presents three screenshots and calls it visibility. There is no prompt list. There is no freeze date. There is no rule for named versus skipped versus wrong. This page owns a sample that can survive that meeting. It does not own ongoing alerts. That is AI search monitoring. It does not own ChatGPT as the only surface. It does not own Overview modules as a sold slot.
Work starts by locking the set. Then we sample. Then we mark. Then we share the artifact. LLM SEO stays the parent. An AI search audit can diagnose why rows look bad. Platform children go deep when one UI is the whole job. This file is the cross-surface sample.
We will not promise Google AI Overviews (AIO) placement. We will not sell LLM visibility as a commercial rank. If you need a weekly ping when a prompt breaks, use monitoring. This room is one dated board. A sample can include ChatGPT and Overview rows without becoming those children. Depth still lives on the platform URLs.
Compare packages if a published retainer already fits. Use AI search monitoring when a related service is the real job. If this page is still a guess, start with a free AI + SEO audit.
When this fits
This page fits when leadership is arguing from screenshots with no prompt set and no freeze date. If you needed an alert feed, a single assistant log, or a sold Overview, start on those doors.
We stop the deck. We lock the set first. A vibe is not a score.
ChatGPT went well. Perplexity did not. The sample has to show both or name the exclusion.
We will refuse the fake index. A dated score is the artifact. Monitoring is the other job if they need ongoing watch.
We will refuse the SLA. Observation of a module can be one sample row. It is not this page’s product. Depth lives on Google AI Search Optimization.
Process
Scoreboard work in this order. We do not start an alert rule in the last step. The point is a dated sample leadership can read, not a vibe and not a pager.
Outcome A named prompt set and surface list, with a freeze date and exclusions.
Outcome A dated grid: prompt by surface.
Outcome A score table with definitions of named, skipped, and wrong.
Outcome A shareable sample pack with the freeze date on the cover.
Deliverables
You leave with a locked set, a dated grid, a score table, and a share pack. You do not leave with an alert feed, a ChatGPT-only product, or a fake share-of-answer subscription.
Prompts and surfaces. Freeze date on the cover. Last quarter’s anecdote slide is not this set.
Prompt by surface on the run date. Extra chats from Slack are out unless added to the set.
Named, skipped, wrong. Definitions written. A purchased rank is not a table.
Which assistants you refused to treat as the market.
Cover date, definitions, grid. If the next job is ongoing watch, that note is a handoff to monitoring.
Benefits
The meeting stops moving the goalposts mid-slide.
It is wrong.
ChatGPT rows sit next to other surfaces you included. Exclusions are named.
Someone can rerun the same set. Nobody pretends an Overview SLA was included.
Methodology
This works when someone can freeze the set and someone can score named versus wrong against the legal name. If both are missing, we are collecting chats.
We start with the prompt list, the surface list, and the legal name. If any of those is missing, we name it first.
We run the grid. We do not trust a vendor index with no prompts. Does this cell name you, skip you, or describe you wrongly?
You may rename the roles below. You may not leave the set implied. An implied prompt list is how two decks disagree after the meeting.
First working session
The first hour is the prompt list and the freeze date, not a tour of every assistant. It is not a monitoring onboarding.
We take the three screenshots leadership loves. We ask which prompts they were. If nobody knows, the set is the job. If the real question is a pager, that is AI search monitoring. If the real question is why rows fail, that is an AI search audit. If the real question is ChatGPT only, that is ChatGPT visibility. If the real question is Overview modules, that is Google AI Search Optimization.
Then we freeze surfaces and exclusions. We run a tiny grid, not a fake index. The extra empty cell is usually the job. A vendor share-of-answer demo is not evidence.
We leave with a first sample pack or a stop. A pack names the set, the freeze date, and the score rule. A stop names what is missing: a frozen list, a name definition, or a job that is actually monitoring. Both are outcomes. An Overview SLA workshop is not.
AI search
Measurement here is a dated platform sample. This page cannot sell AI Overviews, a chat rank, or LLM visibility as a commercial score.
People ask whether a better sample will place them in Google AI Overviews (AIO). It will not. Rows may observe a module. Observation is not a sold slot. If you need platform depth, open the child. If you need ongoing watch, open monitoring. LLM visibility measurement belongs here for samples, not as a sold score.
Anecdotes are not a sample. A single assistant URL is not this page. Nobody can guarantee a spot in AI Overviews, but clear pages improve your chances.
We score against the name you will stand behind. That is not Entity SEO as a full program.
Monitoring owns the watch. This page owns the sample you share once, or on a planned rerun, without pretending it is a feed.
Schema
JSON-LD can help extractors on URLs you own. This engagement scores assistant answers on a freeze date. Schema supports the work; it is not the whole job.
If assistants skip you because the site is empty, we still score skip. We name the site handoff. Markup from this page will not fill the grid.
We refuse stuffing FAQ only to game a cell. A real prompt stays in the locked set.
Markup will not turn this sample into a market product. Eligibility is not a purchased score.
Who we work with
Someone has to lock the set. We work with that person. We do not replace them with a second visibility retainer that is actually monitoring.
Freezes prompts. Holds the line when a VP wants to add a lucky chat after scoring.
Fills the grid on the sample date. Labels modes.
Gets handoffs to platform children and to monitoring. If they try to run this as an Overview SLA or a ChatGPT rank, two jobs collide.
FAQ
Monitoring is an ongoing watch with alerts over time. This page is a dated sample: a prompt set, a run, a score of how often assistants named you, and a share-out of that sample. It is not an alert feed. Open AI search monitoring when the job is continuous watch.
No. Those are platform children. This page is the scoreboard across surfaces for one sample window. A ChatGPT-only log belongs on ChatGPT visibility. An Overview module log belongs on Google AI Search Optimization. Do not treat this file as a single assistant URL.
No. We will not sell a fake share-of-answer dashboard or an Overview SLA. We score a named prompt set on a freeze date. The number is a sample, not a market index you can buy.
No. We will not promise Google AI Overviews (AIO) placement. If a sample row observes a module, that row is an observation. It is not a sold slot. Platform depth stays on the Google AI Search child.
It counts, on that freeze date, whether each assistant in the set named you, skipped you, or described you wrongly, for each prompt. It is not LLM visibility as a commercial rank you purchase. It is a sample you can share with leadership without turning anecdotes into a strategy.
One freeze date, many assistants
Name the freeze date, the prompt list leadership already argues about, and the surfaces you want in the grid. We will say if a scoreboard sample fits, or whether monitoring, a ChatGPT log, or an Overview observation should go first.