How to Build a Brand That Google Ranks and ChatGPT Recommends

A few weeks ago, I was looking for a project management tool for a side project. Asked ChatGPT before Google. The model came back with five names, three I knew well, two I had never encountered. The list itself was interesting, but who was missing from it was more interesting still, because the most heavily-advertised incumbents in the category were both absent.

As of 2026, online search has split into two engines, and the gatekeepers have changed, but the trust mechanism underneath has not. Brands that earned Google's confidence over the last decade are largely the ones earning ChatGPT's now. Ahrefs' analysis of 17 million citations across seven AI platforms found brand mentions correlate with AI Overview presence at roughly 3:1 over backlinks.  

What changed is what these engines do with the answer. Google ranks links and lets users choose. Language models curate, summarise, and recommend. Recommendation is a higher bar than ranking, and a far narrower one. AirOps' analysis of 548,534 retrieved pages found that ChatGPT cites only around 15% of the pages it pulls into the pipeline. The other 85% seem to be evaluated and discarded before they reach an answer. 

It’s evident, then, that brands that cleared the old bar do not automatically clear the new one of AI visibility, and the gap between the two is widening every month. 

This article is about closing it.

The Trust Handoff

Both Google and ChatGPT read the same internet, but they differ in what they do with it.

Google's job is to rank, and ranking lets the user choose. Ten links land on the SERP, then the user picks one. The system can be wrong about a few of them, and the user still gets where they were going. 

ChatGPT and its peers (Large Language Models), on the other hand, don’t give options. When a model names three brands in a recommendation, those three are the answer. There is no page two. The model has to commit, and committing requires evidence the system trusts.

Where does that evidence come from? The same place Google has been pulling it from for fifteen years: backlinks, mentions, reviews, expert citations, structured data, distributed presence across the web.

Language models are not running a second internet. They are running a more selective query against the same one. Kevin Indig's research across 815,000 query-page pairs found that a page in the top retrieval position has a 58% chance of being cited; a page in tenth position drops to 14%. Even at the front of the queue, fewer than three in five pages survive selection. 

There Is More than Google

Most growth teams optimise for Google and stop there, but ChatGPT's retrieval layer runs largely on Bing, which means Bing indexing, Bing Webmaster Tools, and Bing's own ranking signals are part of the same pipeline ChatGPT draws from when it builds an answer. 

The basics are straightforward. Submit your sitemap to Bing Webmaster Tools, make sure your pages are indexed, and pay attention to the same signals Bing weights like quality backlinks, structured data, and page authority. If you are already doing this for Google, the incremental lift for Bing is small. The gap for brands that have never touched it is much larger. Both engines reward the same underlying work, just through different retrieval mechanisms.

This explains a pattern most growth teams will have noticed. The brands ChatGPT names tend to share one trait. They sit on a deep layer of independent corroboration; reviews, mentions, citations, earned coverage, built over years. Loudness does not buy this. Budget does not buy it. Time and earned trust do.

That layer is what we are actually building when we talk about brand trust in 2026.¨

What Trust Looks Like to Each Engine

The trust signals overlap more than they diverge. The differences come down to evidence format.

The right column reads as a tighter version of the left. AI inherited Google's trust paradigm and applied a stricter filter on top, because a recommendation engine has to put a name in a sentence and stand behind it.

One practical note on the table above: ChatGPT's web retrieval runs through Bing, so the Google column applies to both traditional search and ChatGPT's retrieval pipeline, just through different interfaces.

Two practical implications follow.

Brands worth ChatGPT's mention tend to already be brands worth Google's first page. The work that builds one mostly builds the other, which means growth teams treating AI visibility as a separate workstream are usually duplicating effort that compounds naturally across both channels.

The bigger divergence is in the evidence format. Google can rank a brand based on aggregate signals across its own properties: domain authority, content depth, on-page optimisation, and internal linking.

Language models lean harder on third-party sources because they need quotable evidence rather than aggregate authority. A claim on your own domain saying you are the best does not register as evidence. The same claim on a third-party domain (backed by a name and a star rating) does.

That distinction sets up the section that matters most.

Reviews Carry the Most Weight, in Both Directions

Reviews sit at the intersection of every signal both engines care about. They are independent, structured, frequent, dated, attributable, and quotable. No other content type checks all six boxes.

The numbers are stark. Feefo found that ChatGPT references reviews in 58% of its responses. Perplexity cites reviews in 100% of cases. Companies recommended by ChatGPT carry an average rating of 4.3 stars; for Gemini and Perplexity, the floors sit at 3.9 and 4.1 respectively. Brands with ratings below those thresholds are typically filtered out before they reach a recommendation, regardless of how strong their other signals are.

Three mechanics explain why reviews carry this weight.

• The first is structure. Schema markup (Review, AggregateRating, Organization) turns customer feedback into machine-readable data. Language models do not have to interpret intent or extract sentiment from prose. The data arrives pre-parsed: who said it, what they rated, when, and on what platform. That is exactly the format a language model can lift cleanly into an answer.

• The second is independence. A claim on your own website is testimony from the defendant. A review on a third-party domain is testimony from a witness. Models built to minimise hallucination weigh the second far more heavily than the first. A hosted review page on an authoritative third-party domain (the kind any recognised review platform provides) becomes one of the cleanest sources a language model can cite, because the source's reputation is itself a signal.

• The third is freshness. Google can wait. Its index updates regularly, and historical authority compounds over time. Language models behave differently. Retrieval-augmented systems lean on recent content because they are trying to answer a question now, with current evidence. A brand with fifty reviews from 2022 and nothing since reads as either dormant or declining. A brand with steady review velocity reads as actively serving customers, which is exactly the signal a recommendation engine needs to make a confident call.

Review collection works best when it's embedded into the customer journey rather than treated as a standalone campaign. Brands that identify the right touchpoints for feedback requests tend to generate steadier review velocity than those running one-off collection pushes.

Most B2B brands fall behind here. Review collection gets treated as a one-off campaign. Set up the page, push for testimonials, move on. That works for the human-trust use case it was originally designed for. It collapses under the AI use case, where the mechanism rewards continuous evidence.

Reviews are no longer an asset you build. They are a signal you maintain.

How Trustmary Builds the Signal Layer

The operational problem with reviews-as-AI-signal is that running it well requires three things at once. 

1. You need the schema layer so the data is machine-readable. 
2. You need automated collection so the signal stays fresh. 
3. And you need the third-party authority layer so language models treat the source as citable. 

Trustmary's AI search product is built around exactly that gap. Reviews collected through the platform are embedded on your site as crawlable HTML with Review and AggregateRating schema, putting the data into the format language models actually use. Collection runs automatically and continuously, which keeps the signal velocity that retrieval-augmented systems weigh. And the hosted profile page on the Trustmary domain operates as the authoritative third-party source (the page language models can cite with confidence, because it sits on a recognised platform with editorial standards behind it).

For brands serious about AI visibility, the operative question is whether your review infrastructure keeps pace with what the engines now expect.

The Citation Source Most Brands Overlook

One of the most underleveraged citation sources for both Google and AI search engines is long-form audio and video content. Podcast transcripts, webinar recordings, and video interviews are all crawlable, quotable, and attributed to named individuals. This is the kind of sourced, expert content language models prefer to cite.

AI engines read beyond your website. They pull from the broader corpus of what has been said about your industry, and they weigh named expert opinion heavily. A transcript where your head of product explains a technical concept in their own words carries a different signal than a blog post making the same claim anonymously. The attribution ties a claim to a real person with a verifiable track record, which is precisely what EEAT asks for at the Experience and Expertise layers.

The same logic extends across formats. Each one generates a different kind of trust signal, and together they build a citation surface that purely text-based strategies rarely match:

• Podcast transcripts and webinar recordings are indexable, quotable in other articles, and referenceable by AI engines.
• YouTube videos are indexed by Google directly and increasingly surfaced in AI overviews.
• Instagram Reels and LinkedIn videos build attributed presence across platforms that both engines treat as independent corroboration.
• Community and user-generated content. For fast-moving or technical topics, AI engines show a notable tendency to surface Reddit threads and community discussions ahead of polished corporate pages. If your brand or team is active in relevant communities, that presence counts.
• Founder and team clips. A genuine opinion in a 60-second video puts a name, a face, and a point of view on a claim, carrying more EEAT weight than an equivalent anonymous blog post

The practical implication is to treat your team's subject matter experts as a content asset. A single conversation can be repurposed into a transcript, a YouTube video, a clipped Reel, and a LinkedIn post. 

Getting that expertise into crawlable formats consistently, across platforms, is one of the more durable investments a brand can make in its AI visibility.

Audit What the Open Web Actually Says About You

Most growth teams have a precise picture of what their owned media looks like. The website, the blog, the video content, the case studies, the social channels; all of it sits in a content library someone owns and updates.

What the rest of the internet says is less well mapped.

Forum threads, comment sections, comparison articles, podcast transcripts, scraped aggregators, partner pages, employee profiles, journalist mentions. Each contributes to the corpus a language model is reading. None of them is under your direct control. All of them shape how AI describes your brand. Tracking your social media analytics is a start, but the audit needs to go further than your own channels.

The audit work this requires is uncomfortable for two reasons. The surface is fragmented and partially invisible. And it includes the personal digital footprint of the people who are the brand. For most growth companies, the brand is the founder's name on a podcast, the head of marketing's LinkedIn, the engineering lead's conference talk. Language models build a picture of a company aggregate from these sources as readily as they aggregate from corporate ones.

Map first. Fix second.

When AI Cites the Wrong Brand

The reverse problem is starting to surface, and most growth teams are not ready for it.

Language models hallucinate brands. They confidently cite domains that do not exist. They attribute reviews to companies that never collected them. They surface lookalike sites that have spun up specifically to be recommended by AI. The mechanism that makes recommendation engines useful (confidence in a source) is the same mechanism scammers are now exploiting.

For an established brand, the threat is impersonation. Lookalike domains designed to be cited instead of you. Scraped review content republished elsewhere with light edits. Customer support pages mimicking yours that route real customers to fraudsters. The old version of this problem was about humans clicking the wrong link. The current version is about AI confidently recommending the wrong source.

The defence is unglamorous but effective. Verifiable identity across every brand surface. Consistent corporate information on every directory and platform. Active monitoring for impersonation attempts. And, increasingly, removing personal data from the open web for the people whose names appear on your byline list, because spear-phishing a CEO, scraping their credentials, and using them to publish fraudulent content under their name is part of how brand impersonation now operates.

The brands that hold up under AI-mediated discovery are the ones whose identity layer is hard to spoof. That work happens before AI gets involved, not after.

The Playbook

Building a brand that Google ranks and ChatGPT recommends is a single body of work. The actions that compound across both engines come down to five.

1. Build the review layer with structure and velocity. Schema-marked, third-party-hosted, continuously refreshed. Treat it as infrastructure, not a campaign.
2. Earn citations from sources both engines weigh. Industry publications, peer-reviewed work, podcast transcripts, original data. The aim is to be quotable, not just rankable.
3. Audit the open web's view of your brand. Including the personal digital footprint of the people who carry the brand publicly.
4. Tighten the identity layer. Verified profiles, consistent corporate information, monitoring for impersonation, and protection for the names on your byline list.
5. Maintain the work. Trust signals decay without renewal. Google forgives gaps in a way language models do not.
6. Check your Bing presence. ChatGPT's retrieval layer runs on Bing, which means Bing indexing and Bing Webmaster Tools are part of the AI visibility stack, whether you have been treating them that way or not.

Brands winning in 2026 treat their trust infrastructure as an operating discipline. Reviews come in every week. Mentions get earned every month. Identity gets protected continuously. The shortcuts have a shorter shelf-life than they have ever had, and the gap between brands doing this work and brands postponing it widens with every model update.

The engines have caught up. Trust is the only signal that scales with them, and trust is earned slowly, in the open, by brands willing to do the work where the open web can see it.If you want to see this in action, book a demo with Trustmary and see how their review infrastructure can improve your AI visibility.

Author: Irina Maltseva is the founder of ONSAAS and Seen, and an SEO Advisor. For the last eight years, she has been helping SaaS companies to grow their revenue with inbound marketing.