# Check Whether AI Recommends Your Software (5 Prompts, 4 Engines)

> Run five prompts from the AI Shortlist Index library on ChatGPT, Perplexity, Gemini and Google AI Overviews, score sixty rows, and read what they mean.

Canonical: https://webpossible.com/tools/ai-visibility-checker/
Source: https://webpossible.com/tools/ai-visibility-checker/
Format: Markdown version for AI agents. The canonical HTML page is at the source URL above.
Last verified: 2026-09-03

---

Tool

# Check Whether AI Recommends Your Software (5 Prompts, 4 Engines)

This is the manual version of what the AI Shortlist Index does every month. Five prompts, four engines, three runs each, and a table at the end that shows whether a property manager asking AI for software in your category hears your name. It takes about ninety minutes the first time, the consumer apps are enough, and there is nothing to install and no email to hand over.

## What this checks

The Index scores four things per vendor: whether you were named, where in the answer, how consistently across runs, and which pages were cited on the way to the mention. The checker scores the same four with a smaller prompt set, by hand, in your own signed-in accounts. On ChatGPT that is the same surface the Index reads, except that your account carries the memory and history the Index switches off. On the other three the Index reads an API or a licensed SERP capture instead of the app. Treat the result as directional either way. The [measurement framework](/ai-visibility/) explains each metric and why those are the ones that matter. The other [free tools](/tools/) pick up where this one stops.

## The five prompts

The Index asks 25 prompts per category across six types. The checker uses five, one per type, from the maintenance and vendor network set, because that is the category with a completed run to compare against. If you sell resident benefits or access control, swap in the matching ids from the [prompt library](/ai-shortlist-index/prompt-library/) (the rbp- and mac- sets). If your category is not in the library yet, write five in the same shape: first person, one question, a real portfolio detail, no vendor names except in the alternatives and comparison prompts.

| Type | Id | Prompt |
| --- | --- | --- |
| best | mvn-best-01 | What’s the best maintenance coordination software for a property management company with around 300 doors? |
| alternatives | mvn-alt-01 | What are the alternatives to Property Meld for maintenance coordination? |
| segment | mvn-seg-01 | I manage 500 scattered-site single-family rentals across two metros. Which maintenance coordination software handles scattered SFR well? |
| integration | mvn-int-01 | Which maintenance coordination software integrates with AppFolio? |
| comparison | mvn-cmp-01 | Property Meld vs Latchel: which is better for a property management company with about 600 doors? |

_Five prompts from AI Shortlist Index Prompt Set v1.0, maintenance and vendor network category, one per type. The problem-led type is left out to keep the run to five; add mvn-prob-01 as a sixth if you want it._

Two adjustments before you run them. In the alternatives prompt, name the incumbent in your category, not yourself. A prompt that names you hands you a mention, which is why the Index does not count seeded mentions, and neither should you. In the comparison prompt, put yourself against the incumbent and score the framing (recommended, neutral, cautioned) rather than presence, for the same reason. Swap the PMS in the integration prompt for the one your buyers run: Yardi Voyager, Buildium, Rent Manager, Entrata, or AppFolio as written.

## How to run them

On ChatGPT, start a temporary chat so saved memory doesn’t shape the answer, and confirm web search is on; OpenAI’s help center documents both settings. Paste the prompt exactly as written. Save the answer text and the source links the answer carries.

On Perplexity, open a new thread in the default search mode, not a research or deep mode. Paste, then record the numbered citations along with the vendors in the answer, in order. The citation numbers matter more than they look; the pages behind a mention are the part you can act on.

On Gemini, open a new chat in the Gemini app, paste, and expand the sources shown under the answer. If no sources appear, write that down. Grounding is not guaranteed on every answer, and an ungrounded answer is a different finding from a grounded one.

On Google, use a fresh incognito window on google.com from a US location on desktop, and paste the prompt as the query. Record whether an AI Overview appeared at all. If it didn’t, write “no AI Overview” in the row rather than a zero. The Index records absence the same way, because a missing overview says nothing about you.

Do all twenty runs (five prompts on four engines) on day one, then repeat on two more days. Sixty rows.

## The scoring table

Keep it in a spreadsheet, one row per prompt per engine per run. The header row:

```
date,prompt_id,engine,run,named,position,vendors_in_order,cited_domains,framing,notes
2026-09-02,mvn-best-01,ChatGPT,1,y,1,"Property Meld; Latchel","reddit.com; appfolio.com; buildium.com",recommended,search on
```

| Column | What goes in it |
| --- | --- |
| named | y or n, for your company only |
| position | ordinal of your first mention among all vendors named, 1 = first; blank if not named |
| vendors\_in\_order | every vendor in the answer, in the order they first appear |
| cited\_domains | the domains behind the answer’s citations or sources, most frequent first |
| framing | recommended, neutral, or cautioned, from the sentence around your first mention |
| notes | anything that would change the reading: no AI Overview, ungrounded answer, a rename |

_One row per prompt, engine and run; sixty rows for a full check. The example row above is the Index’s one completed September 2026 ChatGPT run of mvn-best-01, which named Property Meld first and Latchel second and cited reddit.com more than any other domain._

## How to read the result

Add four numbers per engine when the sixty rows are in. Mention rate is runs where you were named divided by runs. Position score is your average first-mention position across the runs where you were named; 1.0 means always first. Consistency is whether the three runs of a prompt agreed (all named or none named). Engine spread is how many of the four engines named you at all.

Zero mentions across sixty rows usually means the pages the engines read do not connect your name to the category, not that the engines have judged your product. Look at the cited\_domains column. If your domain never appears and the same three or four third-party domains keep appearing, those third parties are where the category gets decided, and your absence from them is the finding.

Named on one engine and not the others usually points to a single source that engine leans on and the others don’t. The cited domains on the runs that named you will show which one.

Named, but at position three or later in most runs, means you are in the pool and framed as an also-ran. The order tends to follow the order in the cited pages, so read the listicle or thread that keeps appearing and see where you sit in it.

Named only in the comparison prompt is the seed. It counts for nothing. If that is your only mention, score yourself as not named.

The cited\_domains column is the to-do list. Compare it with the published source map for your category, for example the [sources cited for maintenance software](/ai-shortlist-index/maintenance-vendor-network-software/sources/), and start with the domains that appear in both.

## Why three runs beat one

The same prompt on the same engine can name a different vendor tomorrow, and the source list moves with it. One run is an anecdote. Three runs on separate days show whether a mention is stable, and the Index treats the consistency number as a finding in its own right, published rather than smoothed. The [Index methodology](/ai-shortlist-index/methodology/) runs every prompt three times per engine in fresh sessions across at least two days and logs the model id per run, because engines change models without notice and a shift in your row can be a model change rather than anything you did.

If all three runs agree you are absent, that is a stable finding and the fix is on the source side. If they disagree, you have a consistency problem, which is a different fix: the sources are split on you, and the engine is flipping between them.

## What to do with the table

Run the [audit checklist](/tools/ai-visibility-audit-checklist/) against the gaps the table shows, starting with entity consistency and the cited domains. If your site is the problem, the [schema templates](/tools/schema-templates/) and the llms.txt template cover the owned-page fixes. Your category’s Index row is free and refreshes monthly, so bookmark it and re-run this check the week after each edition. When the table shows a gap you cannot close by hand, the [Shortlist Audit](/ai-visibility-audit/) runs the full 25-prompt set across all four engines through the APIs, three runs each, and returns the cause with evidence.

## Get your row explained — $3,500

Seven days. Twenty-five prompts across four engines. The cause behind your row and a 90-day fix. Ryan replies with an agreement and an invoice; no call required.

 

Leave this empty 

Name 

Work email  Company domain 

Request the Audit Prefer a call? [Contact](/contact/).

Goes to ryan@webpossible.com with this page’s URL attached. Never shared, never sold. See the [privacy policy](/privacy/).

Part of [the tools](/tools/).