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 the one time it asked, and what to do about it, in order.

How ChatGPT picks 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, and describes the second as more comprehensive than the citations alone. The app shows only the first.

The Index reads the app, not the API. Each prompt goes into a temporary chat at chatgpt.com, signed in to an account kept for the Index and nothing else, with memory, custom instructions and chat-history search off, the model left at the default, and the choice to search left to ChatGPT. That is the product a property manager is using. The methodology page states what it costs: the app names no model, so a row can move on a model change with nothing in the run file to show it, and the pages behind the answer are no longer visible. A capture holds the answer text, every link in it, the untouched capture and the time it was collected.

The one ChatGPT run the Index has published came from the OpenAI API on 3 September 2026, before that change, 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.

ChatGPT named Property Meld first and framed it as the recommendation for a 300-door company that already has a property management system, then Latchel second with neutral framing, as the option for a manager who wants coordination handled as a service rather than as software. 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 the answer quoted from Property Meld tells you what a vendor page needs to hold. The run pulled Property Meld’s list of integrations (AppFolio, Buildium, Rent Manager, Propertyware, Yardi Voyager, Rentvine), its per-unit pricing ($1.60 and $2.00 per unit per month), and the FAQ line saying the product fits portfolios over 100 units, and used all three to justify the pick. Those are three plain facts on three plain pages.

Review sites did not appear. None of the 124 consulted pages was on g2.com or capterra.com. Our working hypothesis is that for maintenance software questions ChatGPT reads Reddit threads and the PMS vendors’ own content as the place where practitioners talk, and skips review platforms. One run cannot prove that. The next editions will show whether it holds.

One run is one observation. The Index does not publish a rank on it, and you should not read it as more than what the model reached for on one day in one category.

What that implies you should do, in order

First, 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, that your pricing, integrations, and FAQ pages return their text without a JavaScript render, and that a block someone added for GPTBot last year did not take search with it.

Second, put the three facts on three pages. An integrations page with the PMS names in plain text: Yardi, RealPage, Entrata, AppFolio, Buildium, Rent Manager, whichever you support. A pricing page with the per-unit number and the minimum. An FAQ page that says which portfolio size you fit. That is what the run quoted from propertymeld.com. 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.

Third, be on the pages it reads before it writes. In this category that meant reddit.com threads in r/PropertyManagement, r/PptyMgmtSoftware, and r/maintenance; the PMS side’s own content on buildium.com and appfolio.com; and PMS marketplace and help-center pages such as the Rentvine help center. The run also consulted six listicles, among them buildium.com’s roundup of maintenance software, lula.life’s article on the same topic, and posts on ustechautomations.com and essentify.ai, plus a NARPM magazine PDF. A vendor absent from all of those is asking the model to find it from its own site alone, on the strength of whatever query the model happened to write. The category’s source map lists every domain with counts, from the consulted list where an engine returns one and from the links in the answer where it does not.

Fourth, write the comparison and alternatives pages yourself. The prompt library includes mvn-alt-01, “What are the alternatives to Property Meld for maintenance coordination?”, and mvn-cmp-01, “Property Meld vs Latchel: which is better for a property management company with about 600 doors?” When the model searches for those, a page that answers them plainly, with a table, is what it wants to quote. If the only honest comparison online is on a competitor’s domain, that is the page that gets read.

Fifth, make your listings agree. Name, category, integration list, and price should match across your site, the PMS marketplaces (AppFolio Stack, Buildium Marketplace, Rent Manager integrations), Crunchbase, LinkedIn, g2.com, and capterra.com. A real buyer’s ChatGPT carries the memory and history the Index capture switches off, and a model that has seen three versions of your category tends to reach for the oldest.

What the Audit finds

The Shortlist Audit is $3,500 and takes seven days. It runs the full 25-prompt set for your category across all four engines and comes back with five things: your position, plainly; the cause, as ranked findings each labeled verified, inferred, or hypothesis with the evidence linked; the source gap, meaning which of the top ten cited sources in your category you are absent from; a 90-day fix scoped to a Sprint; and what we won’t promise, which is rankings, traffic, leads, or revenue. If we can’t name the cause, you don’t pay for implementation.

On the ChatGPT column, the Audit reads the links in every answer, which is all the app gives it. On Perplexity and Gemini it also reads the wider list of pages each engine opened. A vendor whose site was opened four times and never quoted has a page problem. A vendor never opened 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 mechanism behind this page, with what changes month to month, is in the guide to how ChatGPT chooses sources for software recommendations. The other three 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 Shortlist practice page.