How to Get Your Software Recommended by ChatGPT

A property manager types “What’s the best maintenance coordination software for a property management company with around 300 doors?” into ChatGPT. The answer arrives in a few seconds, built from a few dozen web pages the model went and read. Which pages those are, and whether one of them is yours, decides whether you get named.

This page covers how ChatGPT with web search picks those pages, what the AI Shortlist Index saw, and what to do about it, in order. The mechanism in full is in the guide to ranking in ChatGPT.

How does ChatGPT pick its sources?

ChatGPT with search does not answer from memory alone. It decides whether to search, writes its own queries, reads what comes back, and links a handful of those pages in the answer. The pages it opened and did not link still shaped what it wrote.

OpenAI’s Responses API documentation returns both lists: url_citation annotations for the links a reader sees, and web_search_call.action.sources for every page the search opened. It describes the second as more comprehensive than the citations alone. The app shows only the first.

How the Index captures it

The Index reads the app, not the API. Each prompt goes into a temporary chat at chatgpt.com, signed in to the operator’s own ChatGPT Plus account with memory off and the chat set to Unpersonalized. The model is left at the default and the choice to search is left to ChatGPT, because that is the product a property manager is using.

The cost of reading the app is that the pages behind the answer are no longer visible. September captures also carry no model id, so a September row can move on a model change with nothing in the run file to show it. From October 2026 the capture records the model that answered; the methodology page has the full method.

What did the one visible run show?

The only ChatGPT run in the Index archive whose consulted pages are visible came from the OpenAI API on 3 September 2026. It was retired from the edition when capture moved to the app and is kept in the archive, and it is the only place these numbers exist.

In that run, prompt mvn-best-01 in the maintenance category, the model made five search calls, consulted 124 pages, and cited six of them inline. Thirty-eight of the consulted pages were on reddit.com, sixteen on appfolio.com, and twelve on buildium.com.

The six cited pages were Property Meld’s integration partners page and its FAQ page, AppFolio’s maintenance feature page, Buildium’s maintenance feature page, the latchel.com homepage, and rentvine.com.

Who was named, and how

ChatGPT named Property Meld first and framed it as the recommendation for a 300-door company that already has a property management system. Latchel came second with neutral framing, as the option for a manager who wants coordination handled as a service.

AppFolio, Buildium and Rentvine were named too, as systems to consider if the manager is replacing the whole platform. Lula, EZ Repair Hotline, Vendoroo, Lessen, HappyCo and Leonardo247 were not named.

What it quoted

What the answer quoted from Property Meld tells you what a vendor page needs to hold.

The run pulled Property Meld’s integrations: AppFolio, Buildium, Rent Manager, Propertyware, Yardi Voyager and Rentvine. It also pulled the per-unit pricing of $1.60 and $2.00 per month, and the FAQ line saying it fits portfolios over 100 units. Those are three plain facts on three plain pages, and the answer used all three to justify the pick.

Review sites barely appeared. One of the 124 consulted pages was on capterra.com, a pricing page for a different product, and none was on g2.com.

Our working hypothesis is that for maintenance software questions ChatGPT reads Reddit threads and the PMS vendors’ own content as the place practitioners talk, and mostly skips review platforms. One run cannot prove that, and the next editions will show whether it holds.

What do the September app captures show?

The edition itself rests on 25 app captures per category. In the maintenance category ChatGPT named Property Meld in 91% of runs, Lula in 65% and Latchel in 64%.

The links in its maintenance answers went mostly to vendor sites, with reddit.com at 6 of 184 citations. The app shows only the links, which is why its list is vendor-heavy where the API run was forum-heavy.

One run per prompt is one observation, with no consistency figure behind it. Do not read it as more than what the model reached for in one month in one category.

What should a vendor fix, and in what order?

1. Be readable by the search tool. OpenAI’s crawler documentation lists OAI-SearchBot as the user agent that surfaces sites in search and GPTBot as the training crawler, controlled separately in robots.txt. Check that OAI-SearchBot is allowed and that your pricing, integrations and FAQ pages return their text without a JavaScript render. The AI crawlers guide has the exact lines.
2. Put the three facts on three pages. An integrations page with the PMS names in plain text. A pricing page with the per-unit number and the minimum, and an FAQ page that says which portfolio size you fit. If your pricing page says “book a call”, the model says so. The run noted that Latchel does not publish pricing and directs prospects to a demo, and that was the framing Latchel got.
3. Be on the pages it reads before it writes. In this category that meant Reddit threads in r/PropertyManagement and r/PptyMgmtSoftware, the PMS side’s own content on buildium.com and appfolio.com, and PMS marketplace and help-center pages. The run also consulted six listicles, among them buildium.com’s roundup of maintenance software and lula.life’s article on the same topic, plus a NARPM magazine PDF.

Then the pages you write

4. Write the comparison and alternatives pages yourself. The prompt library includes “What are the alternatives to Property Meld for maintenance coordination?” It also asks Property Meld vs Latchel for a company with about 600 doors. A page that answers those plainly, with a table, is what the model wants to quote.
5. Make your listings agree. Name, category, integration list and price should match across your site, the PMS marketplaces, Crunchbase, LinkedIn, G2 and Capterra. The entity consistency guide covers the audit.

The category’s source map lists every domain with counts. A vendor absent from all of those pages is asking the model to find it from its own site alone.

What does the Audit find?

The Audit is $3,500 and takes seven days. It runs the full 25-prompt set for your category in ChatGPT, three runs each, and comes back with five things.

Your position, plainly, and the cause, as ranked findings labeled verified, inferred or hypothesis with the evidence linked. Then the source gap: which of the top ten cited sources in your category you are absent from. Then a 90-day fix scoped to a Sprint, and what we won’t promise, which is rankings, traffic, leads or revenue.

On ChatGPT the Audit reads the links in every answer, which is all the app gives it. Perplexity and Gemini also return the pages they opened, which the Audit will read once the Index runs those engines. If we can’t name the cause, you don’t pay for implementation.

Which fix you need

A vendor whose site is linked often and never named has a page problem, and a vendor never linked at all has a findability problem. Those are different fixes, and the 90-day Sprint is scoped to whichever one you have. If you would rather talk first, book a 20-minute call.

The other engines have their own pages: getting cited by Perplexity, appearing in Google AI Overviews, and getting into Gemini’s grounded answers. All of them roll up to the services page.