u/Velocitas_1906

Trustpilot just dropped a study (commissioned by Seer Interactive, March 2026)

analyzing 800,000 AI responses across ChatGPT, Gemini, Perplexity, and Google AI Mode.

The headline number: only 1% of AI responses cite a brand with no Trustpilot profile.

That jumps to 53.5% just by having an active profile, and hits 75.3% for brands that

collect 80+ reviews and respond regularly.

Two things stand out to me:

1. Absence isn't neutral

AI tools don't just ignore brands without reviews. According to the study, they

actively describe a missing profile as a warning sign for consumers. That's a

meaningful distinction. You're not invisible, you're flagged.

2. Review sites are now the #2 citation source in AI responses

14% of all citations go to review/trust sites — behind only brand websites. That's

ahead of news sites, forums, and editorial content.

The mechanism makes sense when you think about how AI systems evaluate sources:

- Recency: Trustpilot gets ~200k new reviews/day. Fresh, consistent content.

- Relevance: Detailed experiential data that directly answers "is this brand good?"

- Authority: Domain authority of 94/100. AI systems trust it.

What this actually means for GEO strategy:

Most GEO work focuses on what you control : your own content, structure, schema,

llms.txt. This study is a reminder that AI systems also evaluate what third parties

say about you.

Review collection and response management isn't just reputation work anymore.

It's GEO work.

One caveat : This study was commissioned by Trustpilot. The conclusions directly benefit their platform. The mechanisms described are real — but read with that context in mind.

Curious if anyone here has seen review signals show up in their own citation tracking.

Does the platform matter (Trustpilot vs G2 vs Capterra vs Google Reviews) or is it

purely about domain authority + volume?

On my side we worked on G2 reviews and it has definitely helped get clients from the US. Most of them tell us they came from LLM recommendation.

I got asked this question by an ecommerce client recently. I also saw Google's announcement at Google I/O.

For me, for now, the best advices are:

- Use semantic HTML over div soup Agents rely on the accessibility tree to understand your page. A <button> or <a> tag is immediately recognized as interactive. A <div> styled as a button is invisible to agents unless you add role="button" and tabindex.

2. Stable layouts matter more than you think If your "Add to Cart" button moves depending on the product category, an agent taking screenshots will get confused. Consistency is key.

3. Label your form fields properly Use <label for="..."> linked to inputs. Agents need to know what a field is for, not just where it is visually.

4. Make interactive elements large enough Google recommends a minimum of 8x8px visible area for interactive elements — below that, visual analysis filters them out.

5. Avoid ghost overlays Transparent or invisible elements sitting on top of interactive ones will block agents even if they look fine to humans.

What about you — are you already thinking about agent-readiness for your clients? Any tools you use to audit the accessibility tree?

I was working with a client on a content strategy in a competitive health niche. We identified a topic that every tool showed as 0 search volume. The conventional advice would have been to skip it entirely.

We published anyway — because we'd spotted the topic being actively discussed on Reddit. A few days later, Google Search Console was already showing 125 impressions. The topic existed, people were searching for it. The tools just had no data on it. (on the specific keyword)

Adding to that: prompts have no real search volume at the moment.

What signals are you using to prioritize content for LLM visibility?

Shopify quietly launched commerce-readiness.shopify.io — a free, no-login scanner that runs 31 checks on any storefront across five categories: AI discoverability, product schema, transaction readiness, trust signals, and operational maturity.

I've now run it on about ten different e-commerce sites across different verticals. Here's what I found — including where the tool is genuinely useful and where it falls short.

What the scanner actually checks:

— Product schema completeness (is your JSON-LD server-side rendered?)
— Trust signals readable by agents (return policy, contact info, structured)
— Shipping policy detail and machine-readability
— llms.txt presence (more on this below)
— Whether your storefront is blocking major AI crawlers in robots.txt

The llms.txt point is interesting. Shopify is systematically recommending it. We ran a  test on whether AI bots actually check for this file — results were basically nothing in our dataset. But if Shopify is now pushing it as a readiness criterion at scale across millions of merchants, the calculus might shift. It's a signal worth watching.

Where the scanner is useful: It's a solid baseline technical audit. If you're blocking AI crawlers unintentionally, have incomplete product schema, or have no structured shipping/return policies, the scanner catches that fast.

Where it falls short: Passing 31 checks ≠ being recommended.

The scanner tells you if you're readable. It says nothing about whether you're cited, preferred, or chosen.

From what we track at Qwairy across real brands: the gap between "technically readable" and "actually mentioned in AI responses" is large, and it's not closed by schema markup alone. Share of Voice in AI responses is driven by things the scanner doesn't touch — entity association, third-party citations, query-level brand presence across ChatGPT/Gemini/Perplexity.

Did you test the tool? Do you believe it will force LLMs to look at llms.txt?