AI Search Strategy: The Seen & Trusted Brand Framework

AI is already reshaping how buyers discover and choose brands.

When someone asks ChatGPT or Google AI Mode about your category, two things happen:

• Brands are mentioned in the answer
• Sources are cited as proof

Most companies get one or the other. Very few win both.

And that’s the problem.

According to the latest Semrush Enterprise AI Visibility Index, only a small fraction of companies appear in AI answers as both seen (mentions) and trusted (citations).

That gap is the opportunity.

We’re proposing the Seen & Trusted (S&T) Framework — a systematic approach to help your brand earn mentions in AI answers and citations as a trusted source.

Do both, and you multiply visibility, trust, and conversions across platforms like ChatGPT, Google AI Mode, and Perplexity.

SEO remains the foundation.

But AI doesn’t just look at your site. It pulls signals from review platforms, Reddit threads, news coverage, support docs, and community discussions.

When those signals are fragmented, your competitors will own the conversation.

This guide shows you exactly how to fix that with two playbooks:

• Get Seen: Win favorable mentions in AI answers
• Be Trusted: Earn citations as a reliable source

Run them together and you give AI no choice but to recognize, reference, and recommend your brand.

Why AI Search Strategy Isn’t Just SEO’s Job

Your SEO team can optimize every page on your site and still lose AI visibility to a competitor with weaker rankings but stronger brand signals.

Why? Because AI systems pull signals from everywhere, not just your website.

When AI generates responses, it mines:

• Review platforms for product comparisons
• Reddit threads for pricing complaints
• Developer forums for implementation details
• News sites for company credibility
• Support docs for feature explanations

The challenge is that these signals live across different teams.

For instance, your customer success team drives customer reviews on G2 and Capterra. But if they’re not tracking review quality and detail, AI has nothing substantive to cite when comparing products.

Similarly, your product team controls whether pricing and features are actually findable. Hide everything behind “Contact Sales” forms, and AI will either skip you entirely or make assumptions based on old Reddit threads.

Your PR team lands media coverage and analyst reports. These third-party mentions build the trust signals AI systems use to determine authority.

Your support and community teams shape what gets said in forums and Discord servers. Their responses (or silence) directly influence how AI understands your product.

SEO and content teams own the site structure and content creation. But that’s just one piece now.

Without coordination, you get strong performance in one area, killed by weakness in another.

To grow AI visibility, you need synchronized campaigns — not just an “optimize for AI” line item tacked onto everyone’s OKRs.

That’s where the Seen & Trusted Framework comes in. It gives every team a role in building the signals AI depends on.

Playbook 1 – How to Get Seen (The Sentiment Battle)

Getting “seen” means showing up in AI responses as a mentioned brand, even without a citation link.

When a user asks ChatGPT, “What are the best email marketing tools?” they get names like HubSpot, ActiveCampaign, and MailChimp.

These brands just won visibility without anyone clicking through.

But here’s a challenge:

You’re fighting for favorable mentions against every competitor and alternative solution.

This is the sentiment battle.

Because AI doesn’t just list brands. It characterizes them.

You might get mentioned as “expensive but comprehensive” or “affordable but limited.”

Like here, when I asked ChatGPT if ActiveCampaign is a good option:

In some cases, the response could be more negative than neutral. Like this:

These characterizations stick.

So, how can your brand get more mentions and have a positive sentiment around?

There are four main sources that AI systems mine for context.

Step 1. Build Presence on the Right Review Sites

AI systems heavily weigh review platforms when comparing products. But not all reviews are equal.

A detailed review explaining your onboarding process carries more weight than fifty “Great product!” ratings.

AI needs substance, like specific features, use cases, and outcomes it can reference when answering queries.

G2 is one of the top sources for ChatGPT and Google AI Mode in the Digital Technology vertical, according to Semrush’s AI Visibility Index.

The platform gives AI everything it needs: reviews, features, pricing, and category comparisons all in one place.

Slack ranks among the top 20 brands by share of voice in AI responses for the Digital Technology vertical.

Part of that success comes from their G2 strategy.

When I ask ChatGPT, “Is Slack worth it?” it cites G2 as one of the sources.

Look at Slack’s G2 reviews and you’ll see why.

Its pricing, features, and other information are properly listed and up-to-date

Users write detailed reviews about channel organization, workflow automation, and integration setups.

G2 isn’t the only platform that matters.

• For B2B SaaS: G2, Capterra, and GetApp
• For ecommerce: Amazon reviews
• For local/service businesses: Yelp and Google Reviews

In my experience, the depth of the review matters just as much as the platform — if not more.

You’ll see many very detailed product reviews as a source in AI answers from sites with low domain authority.

So, what does this mean in practice?

You need reviews from customers. And your review strategy needs four components:

• Timing: Email customers after they’ve used your product enough to give meaningful feedbac, but while the experience is still fresh
• Templates: Provide prompts highlighting specific features to discuss. “How did our API save you development time?” beats “Please review us.”
• Incentives: Reward detail over ratings. A $XX credit for reviews over 200 words can generate more AI-friendly content
• Engagement: Respond to every review. AI systems recognize vendor engagement as a trust signal.

Step 2. Participate in Community Discussions

Community platforms are where real product conversations happen. And AI systems are listening.

• Reddit threads comparing alternatives
• Stack Overflow discussions about implementation
• Quora answers explaining use cases

These unfiltered conversations shape how AI understands and recommends products.

Reddit and Quora consistently rank among the top sources cited by ChatGPT and Google AI Mode across industries.

Like in the Business & Professional Services vertical here:

Online form builder Tally is a great example of dominating community discussions and winning the AI search.

AI-powered search is now their biggest acquisition channel, with ChatGPT being their top referrer.

This is their weekly signup growth of the past year, driven by AI search:

How are they doing this?

Marie Martens, co-founder of Tally, writes:

Here’s Marie talking about her product on Reddit:

And answering users’ questions:

And partaking in ongoing conversations:

This authentic engagement creates the context AI needs.

So, when I ask ChatGPT what’s the best free online form builder, it mentions (and recommends) Tally.

Big brands like Zoho take part in Reddit discussions as well. To answer questions, address concerns, and control their brand sentiment.

Like here:

Zoho ranks among the top brands by share of voice in ChatGPT and Google AI Mode responses. Just behind Google.

The community platforms like Reddit, Overflow, Quora, and even LinkedIn matter a lot in AI visibility:

Your community and customer success teams should be active on these platforms.

But presence alone isn’t enough.

Your strategy needs authenticity.

How?

• Answer questions even when you’re not the solution
• Address common misconceptions about your product (don’t let misinformation take over threads)
• Share your actual product roadmap, including what you won’t build
• Give detailed, honest responses to user complaints, even if it means acknowledging past mistakes
• Encourage your product, support, or founder teams to answer technical or niche questions directly

AI systems can detect promotional language. They prioritize helpful responses over sales pitches.

The brands winning community presence treat forums like customer support, not marketing channels.

Step 3. Engineer UGC and Social Proof

User-generated content and social proof create a feedback loop that AI systems amplify.

• When customers share their wins on LinkedIn
• When users post before-and-after case studies
• When teams document their workflows publicly

…all of this becomes training data.

Brands with strong community engagement and visible social proof see higher mention rates across AI platforms.

Patagonia is a fitting example here.

When I ask ChatGPT about sustainable outdoor brands, Patagonia dominates the response.

In fact, Patagonia holds the highest share of voice in AI responses for the Fashion and Apparel vertical.

They consistently appear in discussions around “ethical fashion” and “sustainable brands.”

Not because they advertise, but because customers evangelize. And that advocacy is visible everywhere.

Customers regularly mention their positive experience with Patagonia’s exchange policy.

There are countless positive articles written on third-party platforms about their products.

And on social platforms like Instagram.

These real-world endorsements are the kind of social proof AI recognizes and amplifies.

No wonder Patagonia has a highly favorable sentiment score (according to the “Perception” report of the AI SEO Toolkit).

So, how do you get people creating content (and proof) that AI pays attention to?

• Encourage customers to leave ratings on trusted third-party sites
• Partner with micro-influencers to share authentic product stories, tips, and reviews in their own voice
• Invite users to post before-and-after results or creative use cases
• Design features or experiences users want to show off (like Spotify Wrapped)
• Reward customers who share feedback or use cases publicly (early access, shoutouts, or swag)
• Reply to every public mention or tag because AI recognizes visible engagement

The mistake most brands make?

Asking for just testimonials instead of conversations.

Don’t ask customers to “share their success story.” Ask them to help others solve the same problem they faced.

The resulting content is authentic, detailed, and exactly what AI systems look for.

Step 4. Secure “Best of” List Inclusions

Comparison articles and ‘best of’ lists are key sources for AI citations.

When TechRadar publishes an article on top “Project Management Tools for Remote Teams,” that article becomes source material for hundreds of AI responses.

When Live Science reviews running watches, those comparisons train AI’s product recommendations.

These third-party validations carry more weight than your own content ever could.

In fact, sites that publish “best of” listicles consistently appear as top sources for AI platforms — including Forbes, Business Insider, NerdWallet, and Tech Radar.

Garmin is a perfect example.

Their products appear in virtually every “best GPS watch” article across running, cycling, and outdoor publications.

Like in this Runner’s World article:

Or this piece in The Great Outdoors:

But what makes their strategy work is consistency across platforms.

Yes, the specs are the same by nature.

But what stands out is how consistently those specs, features, and images appear across independent sites.

That repetition reinforces trust for AI systems, which see the same details confirmed again and again.

So, when I ask ChatGPT, “Which is the best GPS watch?” it mentions Garmin.

And it doesn’t stop there. It highlights features that other third-party articles emphasize, like battery life, accuracy, solar charging, and water resistance.

This consistency across independent sources is why Garmin holds one of the highest shares of voice in ChatGPT and Google AI Mode responses for the Consumer Electronics vertical.

So, how do you land in these “best of” lists?

It starts with a great product. Without that, no list will save you.

That aside, you need to make journalists’ jobs easier. Most writers work under tight deadlines and will choose brands that provide ready-to-use assets over those that make them hunt.

So build a dedicated press kit page with specs, pricing, high-res images, and other assets.

Like Garmin does here:

Next, reach out to journalists and niche publications. Don’t wait for them to find you.

Timing matters a lot as well.

Most “best of” lists update annually. So, pitch your updates a few months before refreshes.

Also, don’t just target obvious lists. Focus on category expansion.

For instance, Garmin doesn’t just appear in “best GPS watch” roundups. They also feature in broader outdoor and fitness lists that cover running, cycling, and multisport gear.

That reach multiplies the mentions AI systems can cite.

The bottom line: AI visibility favors the brands that keep showing up in independent comparisons.

Secure those “best of” inclusions, and you increase your chances of being mentioned in AI answers.

Playbook 2 – How to Be Trusted (The Authority Game)

Getting mentioned is half the battle. Getting cited is the other half.

When AI systems cite your content, they’re not just naming you. They’re using you as evidence to support their answers.

Look at any ChatGPT or Google AI Mode response.

At the bottom or side, you’ll see a list of sources. These citations are what AI considers trustworthy enough to reference.

According to Semrush’s AI Visibility Index, certain sources dominate AI citations across industries. Like Wikipedia, Reddit, Forbes, TechRadar, Bankrate, and Tom’s Guide.

They have achieved, what I call, the “Citation Core” status.

Why do these platforms get cited so often?

AI systems trust sources with verified information, structured data, and established credibility. They need confidence in what they’re citing.

This is the authority game.

You’ve earned mentions through the sentiment battle. Now you need to build the trust that also earns you citations.

This is how you maximize your AI visibility.

Here are five ways to build that authority.

Step 1. Optimize Your Official Site for AI

AI platforms can only cite what they can crawl, parse, and understand.

If your details aren’t exposed in clean, readable code, you’re invisible. No matter how good your content is.

Use semantic HTML to structure your content.

That means marking up pricing tables, product specs, and feature lists with tags like <table>, <ul>, and <h2>.

Don’t tuck information inside endless <div>s or custom layouts that hide meaning.

Also, avoid relying on JavaScript to render your main content.

AI crawlers can’t read JavaScript.

If your pricing or docs load only after scripts fire or buttons click, those details will be skipped.

Almost every top-cited site in AI answers passes the Core Web Vitals assessment, which signals that the page loads fast, stays stable, and presents content in a clean structure.

Like Bankrate — the most cited source in Google AI Mode for the Finance vertical:

Or InStyle — the 8th most cited source on ChatGPT in the Fashion & Apparel vertical.

These sites consistently surface in AI responses because their pages are easy to crawl, fast to load, and simple to extract structured information from.

A lot of what you’ll do to optimize your site for AI is SEO 101.

• Structure all key information in native HTML elements (no custom wrappers)
• Keep important content visible on initial load (no tabs, accordions, or lazy-loaded sections)
• Use schema where it reinforces facts: pricing, product, FAQ, organization
• Run regular audits with JavaScript disabled to see what AI sees
• Minimize layout shifts and script dependencies that delay full render

For page-by-page analysis, you can use Google’s PageSpeed Insights.

To check your entire site’s health and performance, use Semrush’s Site Audit tool.

Get a detailed report showing technical issues on your website and how you can fix them.

At the end, you want a fast, stable, and easy-to-parse website.

That’s what earns AI citations.

Step 2. Maintain Wikipedia + Knowledge Graph Accuracy

AI systems rely on public data sources to build their understanding of your brand.

If that information is wrong, every answer AI generates about you will be too.

Wikipedia is one of the most cited sources on ChatGPT for all industries covered in Semrush’s AI Visibility Index.

Interestingly, Google AI Mode leans heavily on its Knowledge Graph to validate facts about companies and products.

When your Wikipedia page contains outdated info — or your Knowledge Graph shows old details — those inaccuracies get baked into AI responses.

That hurts trust, sentiment, and your chance of being cited in the long-term.

So your job is twofold:

1. Make sure your brand exists in these systems
2. Keep the data clean and current

Start with your Wikipedia page.

If you have one, audit it quarterly.

Fix factual errors, like outdated product names, revenue ranges, or leadership bios.

Support every edit with a credible third-party source: news coverage, analyst reports, or industry publications.

Wikipedia doesn’t allow brands to directly promote themselves. And promotional edits get removed.

But updates to fix factual errors usually stick. As long as you provide solid citations.

You can use the “Talk” page of your Wikipedia entry to propose corrections.

If you don’t have a Wikipedia page, you’ll need to meet notability guidelines.

That typically means coverage in multiple independent, well-known publications.

Once that’s in place, a neutral editor (not on your payroll) can create the page.

Next, fix your Knowledge Graph.

Google pulls its brand facts for its knowledge graph from multiple sources. Like Wikidata, Wikipedia, Crunchbase, social profiles, and your own schema markup.

Start by “claiming” your Knowledge Panel.

This means a knowledge panel already exists for your company when you search its name. You just have to claim it by verifying your identity.

If you don’t see one, you’ll need to feed Google more structured signals.

Start by adding or improving your Organization schema on your homepage.

Then, make sure your company has a proper Wikidata entry. Google may use this to build its Knowledge Graph.

A strong Wikipedia page and Google knowledge panel shape how AI understands your brand.

Get them right, and you build a foundation of factual authority that AI systems can trust.

Step 3. Publish Transparent Pricing

Hidden pricing creates negative sentiment that AI systems pick up and amplify.

When users can’t find your pricing, they turn to Reddit and LinkedIn. And the speculation isn’t always favorable.

For instance, Workaday doesn’t show its pricing.

And the Reddit comments aren’t helpful to its potential customers.

According to Semrush’s AI Visibility Index, when enterprise software hides pricing behind “Contact Sales,” AI uses speculative data points from Reddit and LinkedIn.

And it often links that brand with negative price sentiment.

Because AI systems are biased toward answering, even if it means citing speculation.

They’d rather quote a complaint from third-party sites about “probably expensive” than admit they don’t know.

Without clear pricing, you’re also excluded from value-comparison queries like “best budget option” or “most cost-effective for enterprises.”

Publishing transparent pricing creates reliable data that AI trusts over speculation.

Now I understand this isn’t always possible for every brand. Whether to show pricing depends on various other decisions and strategies.

But if you want to build trust for higher AI visibility and positive sentiment, transparent pricing is important.

Which means:

• Include tier breakdowns with feature comparisons
• Spell out annual vs. monthly options
• List any limitations or user caps
• Update your pricing on G2, Capterra, and other review sites

When reliable sources like your pricing page and G2 have clear information, AI stops turning to speculation.

That transparency becomes part of your brand identity and authority.

Step 4. Expand Documentation & FAQs

Your support docs and help center often get cited more than your homepage.

Because AI systems look for detailed, problem-solving content. Not marketing copy.

Apple holds one of the highest shares of voice in ChatGPT and Google AI Mode responses for the Consumer Electronics vertical.

Its support documentation appears consistently in AI citations across tech queries.

When I ask ChatGPT how to fix an iPhone issue, it cites support.apple.com.

Product documentation dominates citations in technical verticals.

Why?

Because it answers specific questions with step-by-step clarity.

Your product documentation is a citation goldmine if you structure it right.

Start by creating dedicated pages for common problems. “How to integrate [Product] with [Product]” beats a generic integrations page.

For example, Dialpad has dedicated pages for each app it integrates with.

And each page clearly explains how to connect both apps.

Next, write troubleshooting guides that address real user issues.

(You can learn about these issues from your sales teams, account managers, and social media conversations.)

Also, build a comprehensive FAQ library that actually answers questions. Not marketing-friendly softballs, but the hard questions users really ask.

Make sure every page is crawlable:

• Use static HTML for all documentation
• Create XML sitemaps specifically for docs
• Implement breadcrumb navigation
• Add schema markup for HowTo and FAQ content

The goal is to become the default source when AI needs to explain how your product works.

Not through SEO tricks, but by publishing the most helpful, detailed, accessible documentation in your space.

Step 5. Create Original Research That AI Wants to Cite

Original research gives AI systems something they can’t find anywhere else. Your data becomes the evidence they need.

Take SentinelOne as an example. It’s a well-known brand in cybersecurity.

They regularly publish threat reports, original data, and technical insights.

This is one of the reasons they often get cited as a source in AI responses.

In the intro, I said very few brands are both mentioned and cited by AI. Remember?

SentinelOne is one of those brands that has built dual authority.

According to Semrush’s AI Visibility Index, it’s the 15th most cited and 19th most mentioned brand in the Digital Technology vertical.

Because it publishes original insights that aren’t available anywhere.

And AI systems want: verified data, industry insights, and quotable statistics.

But not all research gets cited equally.

• Annual surveys with significant sample sizes (think: 500+) carry weight. But “State of [Industry]” reports based on 50 responses might not.
• Benchmark studies comparing real performance data become go-to references. But thinly-veiled sales pitches disguised as research might get ignored.

You can use your proprietary data to create original research reports.

Or team up with market research companies like Centiment that can help you collect data through surveys.

When creating these reports:

• Lead with key findings in bullet points
• Include methodology details for credibility
• Provide downloadable data sets when possible
• Add structured data markup for datasets

Also, promote findings through press releases and industry publications.

When Forbes, TechCrunch, and other leading publications cover your research, AI systems are more likely to notice.

Like this SentinelOne report covered by Forbes:

The compound effect here is powerful.

Your research gets cited by news outlets → which gets cited by AI → which drives more coverage → which builds more authority.

That’s how you go from being mentioned to being the source everyone (including AI) trusts.

Pulling It All Together – Running Both Playbooks

You’ve seen the framework. Now it’s time to execute.

Step 1. Audit Your Current AI Visibility

Start by understanding your baseline.

Run test queries in ChatGPT and Google AI Mode. Search for your brand, your category, your product, and the problems you solve.

Note where you’re mentioned (in the answer itself) and where you’re cited (in the source list). Screenshot everything.

If you’re using Semrush’s Enterprise AIO, you can use Competitor Rankings to see how often your brand shows up in AI answers compared to your competitors.

Step 2. Build Parallel Campaigns

Both playbooks need to run simultaneously.

You can’t wait to be “seen” before building trust.

• Playbook 1 (Seen): Customer success drives review campaigns. Community managers engage in forums. PR pushes for “best of” list inclusion.
• Playbook 2 (Trusted): Product publishes transparent pricing. SEO and engineering improve site structure. Support expands help content. Marketing creates original research.

The key is coordination.

Create a shared dashboard to track each team’s contributions to AI visibility.

Step 3. Monitor and Iterate

AI visibility shifts fast. What worked last month might not work today.

Track your mentions and citations monthly.

Use an LLM tracking tool like Semrush or a manual prompt list to see how you’re showing up (and how often).

Watch for imbalances.

Strong mentions but weak citations? Focus on authority signals from Playbook 2.

Cited often but rarely mentioned? Ramp up your community and sentiment work.

Also: watch your competitors. When someone jumps in AI visibility, reverse-engineer what changed.

New PR coverage? More reviews? A pricing update?

The brands winning AI search aren’t waiting for perfect strategies. They’re testing, learning, and adjusting faster than their competition.

The AI Visibility Window is Open

In addition to listing your brand, AI platforms influence what buyers see, trust, and choose.

And right now, AI visibility is anyone’s game. Only a few brands in each industry have cracked the code of being both mentioned and cited.

That means even established giants can be outmaneuvered if you move faster on AI strategy.

So while competitors debate whether AI search matters, you can build the presence that captures tomorrow’s buyers.

The Seen & Trusted Framework gives you the direction.

Run both playbooks. At once.