The question we asked
We opened ChatGPT and asked the question a homeowner might type after spotting a water stain on the ceiling:
“Who is the best roofing company in Flagstaff, AZ? List 3-5 specific businesses by name with a short description of each.”
This isn’t a hypothetical search. Bain research from 2025 found that about 80 percent of consumers now rely on AI-written summaries for at least 40 percent of their searches. The old path — search, scan ten blue links, open a few websites — is collapsing into a single question and a single answer. When a Flagstaff homeowner asks an AI assistant who should fix their roof, they get a short list of names, and most will call someone on that list. If your company isn’t on it, you were never in the running.
What ChatGPT said
ChatGPT answered immediately and named five companies: Flagstaff Roofing Company, Polaris Roofing Systems, Right Way Roofing, Ridgeline Roofing, and Stonecreek Roofing.
It described Flagstaff Roofing Company as strong on customer service and attention to detail, handling installations, repairs, and maintenance across multiple roofing materials. Polaris Roofing Systems was framed as the residential-and-commercial specialist with an emphasis on high-quality materials. Right Way Roofing got credit for years of service in the area, prompt work, and clear communication. Ridgeline Roofing was noted for competitive pricing and durable materials on both residential and commercial jobs. Stonecreek Roofing was described as professional, consistent, and popular with local homeowners.
Two things stand out. First, the answer was confident — no hedging, no refusal to name names. Second, every description reads like a condensed version of what the web already says about each company. That second point is the whole story.
Why these businesses got recommended
ChatGPT has never inspected a roof in Coconino County. When it recommends a business, it’s reproducing patterns from its training data and the sources it retrieves — which means a recommendation is really a measurement of each company’s citation footprint.
Princeton’s GEO study (KDD 2024) was the first major research to measure what moves visibility inside AI-generated answers, and the signals it identified map cleanly onto this result:
- Complete, consistent profiles on directories and review platforms. These are the sources AI systems lean on hardest for local business questions.
- Review density. Not just star ratings — volume, recency, and detailed review text the model can summarize. Phrases like “prompt service” and “thorough communication” in ChatGPT’s answer are review language, digested.
- Descriptive service content. Pages that name specific services — leak repair, re-roofing, new installation, maintenance programs — give the model concrete material to repeat. Vague “quality roofing solutions” copy gives it nothing.
- Structured data and consistent business information. The same name, location, and service list appearing identically across the company’s site and third-party sources makes the entity easy for a model to recognize and trust.
What the recommended businesses have in common
Looking across the five winners, four patterns repeat:
- Named service depth. Every single description lists specific services. That detail comes from service pages and profiles that spell out the work, not a one-page site that says “roofing done right.”
- Both markets covered. Three of the five were described as serving residential and commercial customers — twice the contexts in which they can surface in an answer.
- Tenure signals. “Serving the Flagstaff area for many years” is the kind of claim that only shows up when a company’s history is stated plainly and repeated across the web.
- A reputation the model can quote. Comments about communication, reliability, and pricing come from a body of reviews large enough to summarize. One or two reviews don’t produce a pattern; dozens do.
What’s missing from the roofing companies that weren’t recommended
Flagstaff has far more than five roofing companies. The ones left out of this answer tend to share the same gaps: sparse or inconsistent directory profiles, low review counts, and websites with no specifics — no years in business, no licensing details, no named services, no numbers of any kind.
Princeton’s research puts hard figures on those gaps. Adding relevant statistics to a page lifted its visibility in generative engine answers by about 41 percent. Citing credible sources lifted visibility by up to 115 percent for sites that weren’t already dominating traditional search. Read that second number again: the biggest gains went to the underdogs. A roofing company that isn’t currently in ChatGPT’s answer has more to gain from these fixes than the incumbents do — because the model isn’t ignoring them out of preference, it’s ignoring them because the written record is too thin to cite.
What this means for your business
If you run a roofing company, do what we did: ask ChatGPT this exact question about your market and see whether you appear. If you don’t, the fix is unglamorous but concrete. Complete every major directory and review profile, and keep the details identical everywhere. Ask happy customers for reviews consistently, not in bursts. Rewrite your service pages so they name every service you offer, your service area, your licensing, and how long you’ve been in business. Add verifiable specifics — the model rewards pages that sound like evidence, not slogans. The five companies above weren’t picked by luck. They were picked because the web’s record of them is dense and consistent, and that’s buildable.
Want to see your score?
RankForward runs a free AI visibility report that shows how AI assistants actually talk about your business — which engines mention you, and what’s holding you back. Get yours at rankforward.ai/score. It takes about a minute.