Tag: LLM Visibility

Credibility-First Link Building: How We Earn Lasting Authority
At Resolve, we define link building for legitimacy as earning authoritative backlinks, brand mentions, and media coverage that demonstrate trust, expertise, and credibility to search engines and AI systems. Instead of chasing link volume, we use digital PR, original research, thought leadership, and journalist relationships to earn genuine editorial citations. These are the authority signals behind Google’s E-E-A-T framework, and they can help us appear in AI Overviews, earn citations from large language models, and build visibility that survives ranking swings.

We believe a little competition is healthy. It challenges us, sharpens our thinking, and pushes us to pursue bigger and better results.

However, today’s search environment is changing faster than ever. Large language models, AI-generated answers, and frequent algorithm updates are reshaping how people find information, making it increasingly difficult for us to rely on yesterday’s playbook.

The metrics we once used to keep brands afloat — traffic, domain authority increases, and keyword rankings — no longer define SEO success on their own. We can reach the top of a search results page and still see very few conversions.

If we continue chasing those numbers in isolation, we risk being left behind. We have to adapt.

We now widen our view to the outcomes that matter most: trust and brand authority. Unlike a temporary ranking or traffic spike, trust and authority are not earned quickly or easily.

We need time to spread the word about our brand, and we need even more time to prove that people can rely on us. Once we establish that trust, however, it becomes much harder to dislodge.

An algorithm update can cut our traffic overnight. It cannot erase genuine trust overnight.

Our challenge is learning how to build trust and strengthen our brand while every competitor is trying to do the same. We also need meaningful ways to measure concepts that can initially seem difficult to quantify.

We have found that the answers involve some nuance, but they are simpler than they appear. The process begins with a shift in perspective.

Why we see link building as more than rankings

For years, we treated link building like a popularity contest. The site that collected the most votes, in the form of backlinks, was often rewarded with a prominent position in the search results.

Relevance and trust beat volume: 86% of journalists use at least some PR-pitched stories, yet 54% seldom respond, while 61% of Gen Z turn to generative AI instead of Google.

Over time, Google and other search engines updated their algorithms to improve the search experience. With each change, Google cracked down on more sites that tried to manipulate the system with backlink volume instead of earning links with real editorial and audience value. Countless sites lost traffic, and many still feel the effects.

Today, we see Google place more emphasis on relevance, industry trust, and authority. That helps explain why a familiar brand can attract more searchers than a smaller competitor even when both publish similar content and target similar keywords.

Large language models and Google’s AI Overviews have widened this divide. These systems can use retrieval-augmented generation, or RAG, to retrieve relevant sources, often favoring authoritative publications and proprietary information. If we merely repeat a statistic already cited by a top-tier publication, an AI system may choose the better-known source to reduce the risk of spreading inaccurate information.

We also see younger searchers moving toward AI tools. In a 2025 Resolve study, 61% of Gen Z respondents said they used generative AI instead of Google.

None of this means every form of link building looks like spam to Google or an LLM. It means we need backlinks to work alongside a broader set of authority signals.

When publications and journalists cite our brand, they signal authority. When we publish original content and proprietary data, we signal authority. When we create useful graphics and informative videos, we signal authority again.

Once Google and AI systems recognize these signals, the backlinks supporting them become meaningful votes of confidence. Our site may then be more likely to rank prominently, appear in AI Overviews, and receive citations in LLM-generated answers.

How we use E-E-A-T in a competitive search environment

In 2018, Google updated its quality-rater guidance to place greater focus on expertise, authoritativeness, and trustworthiness, commonly shortened to E-A-T. In 2022, Google added another E for experience. Together, these qualities provide a framework for understanding how Google considers credibility and legitimacy.
- Experience: We demonstrate that an author has personally engaged with the subject. Examples include a forum where people describe testing a product or a gardener documenting firsthand pest-prevention trials.
- Expertise: We show that the author has relevant knowledge, qualifications, or credentials supporting the information and advice.
- Authoritativeness: We earn recognition from credible sources and industry voices that cite or link to our work, helping establish us as a respected participant in the field.
- Trustworthiness: We remain transparent, accurate, and honest. We avoid deceiving readers or using manipulative link-building practices.
We apply E-E-A-T both on and off the page. Author biographies can demonstrate expertise, while accurate sourcing can demonstrate trustworthiness. Off the page, we strengthen E-E-A-T signals through the quality of the sites that link to us and the journalists who rely on us as a source. Both dimensions influence how search engines assess whether our information is useful, accurate, and credible.

If we consistently earn backlinks from dozens of irrelevant websites, that pattern can look like a low-quality or manufactured signal. If several respected journalists mention our brand because we published a valuable study, those mentions are much more likely to function as genuine votes of confidence.

Move beyond fragile SEO numbers. This side-by-side graphic shows how earned media, branded searches, industry citations and conversions build authority that can survive algorithm updates.

For us, link quantity is no longer a reliable proxy for legitimacy. We look for backlinks that demonstrate real relevance and value.

We cannot earn those links half-heartedly. We need a coordinated strategy that strengthens credibility both on and off our site.

The off-page SEO tactics we use to demonstrate value

When we ask how to earn links that search engines and LLMs will treat as signs of trust, we do not look for a single outreach tactic. Strong links usually emerge from several activities that we sustain over time.

We create genuinely linkable assets

To prove that people genuinely want to reference our site, we first create content worth referencing. If we are accustomed to quick and easy links, this may require a larger investment in content than we have made before. A routine how-to article or listicle is rarely enough by itself.

We define linkable content as something journalists, publishers, and readers find distinctive and useful — something they have not already encountered dozens of times. We often draw from the following content formats.
- Original data and proprietary research: We publish information people cannot find elsewhere. In a crowded search environment, that means conducting original research rather than recycling familiar statistics. When a journalist needs a statistic and our site is the primary source, we can earn a natural backlink.
- Thought leadership and expert commentary: We share an original perspective from a credible expert within our organization, giving publishers a useful idea or quotation they may cite in future coverage.
- Authoritative long-form guides: We answer the main question thoroughly and anticipate the follow-up questions a reader is likely to ask. This depth can help us earn links as audiences move further into their research.
- Engaging visuals and infographics: We invest in visual assets that make complex information easier to understand and share. Ahrefs found that YouTube mentions strongly correlated with inclusion in AI Overviews. Videos can be especially valuable, but informative infographics also give publishers a useful visual for their own audiences.
These formats demand more time, effort, and money, but we have found that they are often more sustainable than disposable content. They help us earn credible editorial citations and build industry authority that is more resilient to algorithm updates.

We connect link building with digital PR

We place digital PR at the center of authority building because it connects brand development with link acquisition. It helps us earn coverage, attract links, and introduce our organization to new audiences through credible journalists. Those are precisely the kinds of signals search engines can consider when assessing legitimacy.

Unlike traditional PR, our digital PR work focuses on online coverage and backlinks from news organizations and media outlets. We create useful assets or proprietary data, identify the journalists most likely to care, and pitch stories that fit their established beats.

Many of these publications carry significant influence and reach large audiences that can introduce our brand to more people. When a highly authoritative outlet covers our story, other journalists may discover and cite it organically. Syndication can amplify the effect further when a media group republishes an article across its network, potentially producing many relevant links from one story.

Our strongest digital PR campaigns typically use one or more of the following approaches.

Build lasting brand authority in five steps: target trusted publications, create citation-worthy assets, launch digital PR, nurture journalist relationships, then measure and refine your approach.
- Data-led PR campaigns: We begin with what journalists and their readers care about, not simply what we find interesting. We review local news, Google News, and current coverage to understand which subjects are gaining attention. By considering journalist intent from the start, we improve our chances of receiving responses and earning placements.
- Newsjacking or reactive PR: When we can move quickly, we contribute expert opinions, data, or commentary to breaking stories that relate to our brand. This gives journalists material they can use while the topic is still timely.
- Proactive PR: We anticipate trends before they break and prepare insights around recurring news cycles, holidays, and other relevant media moments.
- Contributed content and guest features: We place useful content written by our experts in relevant publications, allowing us to speak directly to established audiences and earn recognition.
When we combine these tactics effectively, we can elevate our brand to a level that competitors cannot reproduce with a batch of low-value links.

We build relationships with journalists and publishers

We know that even fascinating proprietary data, packaged in an expertly designed analysis, can fail if our journalist outreach is poorly targeted.

Journalists receive an enormous number of PR pitches, and those messages can either support or obstruct their work. According to a 2026 Muck Rack study, nearly nine in 10 journalists said at least some of their stories originated with PR pitches.

The same survey found that 54% of journalists seldom or never responded to most pitches. Relevance was a central problem: nearly half said a genuinely relevant pitch was rare.

If we send a journalist at an economics publication a pitch about music-listening habits, we should expect a rejection because the subject may matter to only a small part of that publication’s audience. We do not take that response personally. Journalists build their careers around particular topics and beats, and our job is to support that work rather than distract from it.

We therefore approach outreach as relationship building: a two-way exchange that should benefit everyone involved. Above all, we remember that there is a real person on the other side of every email.
- We personalize our emails and explain why a story fits the journalist’s audience.
- We respond graciously when a journalist says no because our next idea may be a better fit.
- We share relevant work from journalists and publications through social media.
- We contribute thoughtful comments when we have something useful to add.
- We cite journalists’ reporting in future content when it genuinely supports our work.
As we strengthen these relationships, journalists become more likely to consider future opportunities. A thoughtful follow-up or second pitch can receive a warmer response when a reporter already knows that we provide reliable data and useful commentary.

PR relationships grow over time. Even when our first pitch does not fit a journalist’s beat, we remain willing to return with a better story or a new set of relevant data.

How we measure real brand authority

We recognize that authority, trust, and legitimacy feel less concrete than traffic or keyword position. Yet they have become more important. A traffic surge may look encouraging while reflecting temporary attention, weak intent, or an advantage that disappears after an algorithm update.

Authority and legitimacy are more durable. We can also measure the impact of credibility-focused work through several meaningful indicators.

EZ Contacts’ six-month digital PR campaign delivered 1,000+ media placements, raised Domain Rating from 40 to 43, and doubled visibility across ChatGPT and Google AI Overviews.
- Earned media placements: We track the publications that cover our brand, including coverage containing an unlinked mention. These placements help us assess brand credibility.
- Branded search volume: We monitor whether more people search for our company or products after discovering us through media coverage.
- Industry coverage: We look for the point at which publications we have not contacted begin citing our work. That organic pickup is a valuable sign that our authority is spreading.
- Conversions: We measure whether greater credibility leads more people to trust our organization, products, or services and ultimately take meaningful action.
- Organic ranking improvements for target keywords: We still review rankings, but we treat them as one indicator within a broader picture. Sustained movement can show that search engines increasingly view us as a credible result relative to competing pages.
We do not expect these indicators to appear overnight.
- We invest real effort in creating proprietary data.
- We build trust with journalists through repeated, useful interactions.
- We grow authority through sustained work over time.
Our advice is simple: we stay patient, keep improving, and allow credible results to compound.

How we build a credibility-focused link strategy

Knowing the principles of SEO authority is one thing; building an entire campaign around them is another. We use the following five-step process to turn those principles into consistent action.
1. Step 1 — We define our target publications: We identify five to 10 publications that our audience trusts and that search engines are likely to recognize as authoritative within our field. These become our priority coverage targets.
2. Step 2 — We develop linkable assets: We create at least two content or media assets designed around the interests of those publications. We may use original survey data, visual guides, proprietary analysis, or expert thought leadership.
3. Step 3 — We launch a digital PR campaign: We proactively pitch our assets to relevant publications. We can also use platforms such as Connectively or Muck Rack to identify ongoing opportunities with writers covering subjects related to our research.
4. Step 4 — We nurture relationships: We treat every positive media interaction as the beginning of a longer relationship. We follow up with useful information, engage with published coverage, and build the kind of rapport a journalist can rely on.
5. Step 5 — We measure and iterate: We review our authority indicators each quarter, learn from the response to our campaigns, and adjust our content and outreach accordingly.
We know this process can consume a team’s time, particularly when resources or specialized expertise are limited.

In those situations, we may benefit from working with a link-building and digital PR specialist who can expand our capacity and keep pace with search changes. The right support can help us establish sustainable visibility without allowing every minor ranking fluctuation to pull us off course.

How we build authority that lasts at Resolve

We know that quality usually stands the test of time better than quantity. The difficult part is maintaining that focus when competitors appear to be winning with sudden traffic spikes or eye-catching vanity metrics.

We do not let temporary numbers distract us from the larger goal. We focus on lasting authority and legitimacy earned through sustained content creation, thoughtful PR outreach, and genuine relationship building.

When an internal team lacks the time or patience required to maintain that effort, we can step in.

At Resolve, we work with brands to build credibility-focused SEO campaigns through linkable content, data-led digital PR, and hands-on link building. Our goal is sustainable organic growth, not a burst of visibility that disappears after the next algorithm update.

We have seen this approach pay off. In a recent data-led campaign for EZ Contacts, we earned more than 1,000 placements in outlets including the New York Post and Yahoo. As the coverage grew, the brand’s visibility in ChatGPT and Google’s AI Overviews doubled. That is the kind of durable growth we want to build — growth that extends beyond the next algorithm update.

A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.

When we are ready to build links that last, we can visit growresolve.com to learn more.

Our answers to common link-building questions

How do we distinguish link building from digital PR?

We see considerable overlap between link building and digital PR, but we do not treat them as identical. Link building is the broader practice of acquiring backlinks from other websites to improve search authority. Digital PR is a particular approach within that practice, focused on earning links through media coverage, journalist relationships, and placements in credible publications rather than relying on directory submissions, guest-post exchanges, or other lower-authority tactics.

We often use digital PR to pursue the strongest editorial backlinks because reputable outlets have real audiences and established review standards. At the same time, this work builds brand visibility and consumer trust in ways that many conventional link-building methods do not.

How long do we wait for meaningful results?

We do not expect authoritative backlinks or earned media coverage to produce results overnight. That is an honest trade-off when we choose a credibility-focused approach instead of more aggressive tactics. Most brands can begin seeing meaningful domain-authority gains and early ranking movement after three to six months of consistent execution, while highly competitive keywords and top-tier placements may require more time.

The advantage is that our results can compound. Links from credible publications tend to endure, strong journalist relationships can create repeat opportunities, and the authority generated through consistent coverage can keep delivering value long after the initial campaign.

What do we mean by an authority backlink?

We define an authority backlink as a link from a source that search engines and its audience regard as credible and trustworthy. These sources typically have genuine readers, clear editorial processes, established authority, and topical relevance to our industry.

A regular backlink can come from any website willing to link to us, regardless of its relevance, quality, or editorial standards. That distinction matters because search engines do not evaluate every link equally. One editorial link from a respected industry publication can be more valuable than dozens of links from low-authority sites, while also supporting the kind of E-E-A-T credibility that bulk link acquisition cannot reproduce.

Do we value brand mentions without hyperlinks?

Yes. We recognize that Google can associate brand mentions with entities even when a publication does not include a hyperlink. Relevant, unlinked mentions in credible coverage can still contribute to the wider authority signals surrounding our brand.

That is why we consider digital PR valuable even when every placement does not produce a direct link. A credibility-focused off-page strategy should not be reduced to backlink acquisition alone. Our larger objective is to build a brand that respected publications genuinely want to mention, cite, and cover.

What risks do we face with outdated link-building tactics?

We see risks ranging from wasted effort to serious search penalties. Link buying, reciprocal-link schemes, private blog networks, and manipulative anchor-text optimization can violate Google’s spam policies. These tactics may trigger manual actions or algorithmic suppression that substantially reduces our search visibility.

Even when outdated tactics do not produce an immediate penalty, they can lose their value as search systems become better at identifying manufactured signals. Recovering from a link-related penalty can be slow and expensive. We would rather invest in credible link building from the beginning than repair the damage caused by shortcuts later.

Inspired by this post on Search Engine Land.
July 10, 2026
Hidden ChatGPT Search Pipelines Can Shake Up Citations

I see these two new analyses as an important reminder that ChatGPT citations are not as fixed or transparent as they may look. The sources shown in an answer can change when ChatGPT routes search traffic through different hidden retrieval pipelines.

Research from Chris Green and Suganthan Mohanadasan adds a new wrinkle to AI visibility tracking: the final answer does not reveal how ChatGPT selected its sources. Both researchers found internal source-selection labels, including Labrador, Bright, Oxylabs, and SERP, but those labels sit behind the answer rather than inside the citation cards users see.

Green tested 1,000 prompts up to 10 times each and captured 9,946 completed search runs. In most cases, prompts stayed on one retrieval source. Labrador accounted for 88.1% of primary search sources in his dataset, followed by Bright at 9.9%, Oxylabs at 1.7%, and SERP at 0.3%.

What stands out to me is that 11.6% of prompts changed their primary search source across repeated runs. When that happened, URL overlap dropped from 0.273 to 0.149, and domain overlap fell from 0.265 to 0.155. Green calculated that as roughly 45% lower URL overlap and 42% lower domain overlap.

Mohanadasan looked at the issue from another angle. He inspected two days of raw ChatGPT network traffic from one logged-in Pro account and logged about 1,240 source records across a few dozen searches. He found a result_source field attached to web results, with four observed values: SERP, Labrador, Bright, and Oxylabs.

He described Labrador as including established publishers and reference sites, Bright as tied to Bright Data, Oxylabs as tied to Oxylabs, and SERP as an open-web baseline that appeared mostly in news-style results. While Green’s repeated-prompt test found Labrador dominating his dataset, Mohanadasan saw Bright play a larger role in his sample, especially for commercial, shopping, finance, weather, and local queries.

I also think the skipped-search finding matters. Mohanadasan found that ChatGPT classified some queries before searching, using a turn_use_case field. Some prompts were filed as text and skipped web search entirely, even when they sounded current. In those cases, no page could be fetched, cited, or used as evidence.

Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

More complex “thinking” queries behaved differently. Mohanadasan found that ChatGPT could branch into many searches, including site: probes, pricing checks, and searches for unnamed competitors. That changes which pages can enter the answer process because ChatGPT may search rewritten queries, direct site probes, or follow-up checks instead of the exact phrase a user typed.

Another useful distinction is that fetched does not always mean cited. Mohanadasan separated three outcomes: fetched, cited, and mentioned. A page can be pulled into ChatGPT’s context without being shown to users, cited as support for a specific sentence, or skipped as a source even when a brand is mentioned in the answer.

In his small commercial-query sample, Reddit and YouTube were both fetched often, but Reddit was cited and YouTube was not. He attributed that gap to text availability: Reddit threads expose text, while YouTube search results often provide metadata rather than full video transcripts. Vendor pages were cited for their own facts, such as prices and specs, while third-party pages were more likely to support broader recommendation claims.

The practical takeaway for me is that there is no single ChatGPT visibility result to measure. A page may never be considered if ChatGPT skips search, uses another retrieval source, or finds a clearer third-party page to support the claim.

Both analyses also point back to readability. ChatGPT’s source selection depends partly on what it can retrieve and understand. Mohanadasan found cases where ChatGPT appeared to prefer official pricing pages, then fell back to third-party sources when prices were hidden behind JavaScript or otherwise hard to parse.

Green’s results showed that source routing can change which URLs and domains enter the answer set. That makes plain HTML, crawlable facts, clear pricing and specs, strong third-party coverage, and text-heavy pages more important when source selection depends on retrieval and readability.

Inspired by this post on Search Engine Land.

July 8, 2026
Why My Original Data Gets Cited Only as Benchmarks

In Part 1, I looked at the third-party citation signals that matter so much for AI visibility. In Part 2, I made the case for publishing original data, because it is the strongest single predictor of page originality, and the barrier to earning visibility and authority through this approach is still surprisingly low.

Now I have more evidence for why proprietary data should be part of content creation.

Publishing a number matters, but the number itself is not always what gets cited. I looked at Gauge’s citation data to understand what AI systems actually reward when brands publish first-party data. The answer is narrower, sharper, and more useful than simply saying, “original data wins.” Original data does win, but only when it is packaged in the right way.

The format AI rewards most is the benchmark that answers a clear commercial question: which option is best?

First-party research is scarce and punches above its weight

I worked from Gauge’s cited-URL set: 301 live pages cited by AI systems across 316 unique prompts and 7 verticals. Together, those pages carried 1,075 citations.

After auditing the URLs, I found that only 8 of the 301 pages qualified as primary research. To count, the page had to include the original source of the data and its methodology, rather than simply writing about someone else’s numbers.

That means primary research made up just 2.7% of the cited set. But those same 8 pages earned 90 of the 1,075 citations, or 8.4% of the total citation volume. In other words, first-party research appeared rarely, but when it appeared, it over-indexed by roughly 3x on citation share.

The cleaner way I see this is citation density.

Primary research averaged 11.3 citations per page. Everything else averaged 3.4 citations per page. A primary-research page was 3.3x as citation-dense as a non-primary page.

Primary research is rare, but this Gauge analysis shows it punches above its weight: cited pages with original research averaged 3.3x more citations than everything else.

That is the compounding effect of primary research.

This mirrors the information gain finding I discussed in Part 2, but from the AI citation side rather than the classic 10 blue links side.

There, original data correlated with page originality more strongly than any other trait. Here, original data correlates with citation density. Both findings point in the same direction: the number only you can produce is the lever.

Original research wins when the question has a benchmark

This is where the “original data wins” idea needs more precision.

The 90 primary-research citations were not distributed evenly across the 8 pages. They were not distributed evenly across topics either.

Of those 90 citations, 75 came from one cluster: cloud data warehouse benchmarks. Fivetran’s warehouse benchmark alone earned 44 citations, which was just under half of every primary-research citation in the set.

Once I strip out the benchmark cluster, first-party research barely registers in the citation set. The win is not simply, “I published original data.”

The real win is, “I published a benchmark that answers a buying comparison,” and almost nobody builds those well. By benchmark, I mean a page that measures a set of named things against each other on a specific yardstick and publishes the results as numbers.

A striking citation split: cloud data warehouse benchmarks dominated AI-cited primary research, with Fivetran’s benchmark alone pulling 44 citations from the 90-citation set.

Original research is most powerful when it directly answers commercial comparison queries.

This is also what Google is pushing toward with non-commodity content: new, helpful information that is hard to get elsewhere.

The primary-research citations clustered where prompts asked AI to compare options on measurable specs such as speed, cost, latency, yield, or performance.

That explains the warehouse benchmark spike. The “HR Tech / Compensation” label was noisy, but the citations inside that bucket mostly came from cloud data warehouse benchmark prompts. Fivetran, Estuary, and ClickHouse had numbers AI could use.

Crypto / Solana showed the same pattern at a smaller scale. Marinade and Helius earned citations because staking and MEV questions need firsthand ecosystem data, not generic explainers.

The pattern disappeared in topics without a clear benchmark. B2B SaaS / CRM, Education / TEFL, and Product Analytics returned listicles, product pages, explainers, and case studies. After cleaning the data, I found no cited primary-research page in those topics.

A closer look at the content that held 44 citations

Fivetran’s warehouse benchmark took 44 citations from this dataset on its own. Fivetran’s 2 benchmark pages together took 58 of the 90 primary-research citations. So I wanted to understand why.

The page was published in 2022, but when I examine it, it is easy to see why LLMs still prefer it.

Primary-source visibility is highly concentrated: benchmark-driven topics like HR tech and crypto attract far more AI citations than explainers or listicles.

It answers a measurable comparison head-on. The page names BigQuery, Redshift, Snowflake, and Databricks, then ranks them on speed and cost. It is entity-rich and willing to name the major players directly.

It runs on real first-party data. Fivetran tested against actual customer usage rather than relying on synthetic assumptions, and the page calls that choice out clearly.

It shows the method step by step. The page walks through what data was queried, which queries were used, and how each warehouse was configured and tuned. A reader, or a model, can see how the numbers were produced.

The structure is easy to lift. Descriptive headings such as “Results,” “How much did performance improve?,” and “Why are our results different from previous benchmarks?” help AI map a question to the exact passage that answers it.

It links to raw data and sources. The page footnotes references, including the C-Store paper, and points to the underlying data. That makes the claims verifiable. Few brands put that much work into a data-backed content piece, and even fewer share the full dataset for transparency.

It shows its limits. Dated correction notes from December 2022, named qualitative limitations, and an honest “performance floor” caveat make the claims more credible, not less. The corrections also show care.

The URL never moved. A page from 2022 is still earning citations in 2026 because it stayed live at one canonical address.

The data behind a page like this is easier to pull and analyze than it has ever been. The hard part is everything around the data: the clean method, linked sources, corrections, navigable structure, and willingness to say what the numbers do not prove. That is the craft, and that is the moat.

Fivetran's 2022 benchmark page shows why clear, comparison-led research can become a lasting citation source for AI and search visibility.

This kind of first-party data content is not a thin press release with a few loosely pulled numbers. It requires real work, and it can hold authority for years. My takeaway is simple: AI does not reward “original data” by default. It rewards first-party research when the page gives a clear answer to a measurable comparison and signals depth, expertise, and trust.

The opportunity is to publish a retrievable dataset for a buyer question where AI does not yet have a clean benchmark source. That connects directly to the unanswered-questions finding from Part 2. The opening exists, but in many verticals, nobody has walked through it with a real dataset.

Original data needs a citation-ready package

Original data gives a page something AI cannot get from another explainer. But AI still has to retrieve it, parse it, and map it to the user’s question.

That is where many brands lose the citation. They publish proprietary numbers, but bury them in narrative, gate them behind forms, move the URL, or skip the methodology. The data exists, but the citation never happens.

The pages that won in this dataset had both ingredients: original numbers and a clean citation shape. They had stable URLs, clear methods, named comparisons, and results that answered buyer questions directly.

Who wins: brands with proprietary product, usage, or pricing data that package it into a comparison a buyer can act on, especially one that can inform LLM-generated recommendations.

Who loses: brands that publish original numbers inside dense narratives, on slow or unstable pages, with no clear comparison frame for AI to retrieve and reuse.

When I think about a citation-ready research page, I look for four parts.

Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

Lead with the comparison result. The headline finding, such as “X is fastest” or “Y is cheapest at scale,” should appear in the first 30% of the page. Lead with the result, then explain the method and nuance.

Box the methodology. Show the sample, time window, what was measured, and how the measurement happened. Attribution confidence is part of what makes a number citable, so the method needs to be clear on the page.

Frame the comparison explicitly. AI reaches for benchmarks when prompts ask “which is best.” A table comparing named options on named specs is the format most likely to be lifted.

Keep the URL stable. Use one canonical page and keep it live. Do not migrate it or rename it every redesign. The citation earned this quarter only compounds if the page is still there next quarter. In this dataset, 64 of 365 cited URLs were dead, redirected, or otherwise broken, taking 203 citations down with them.

This is the work behind a citable benchmark, and it is more involved than it looks.

HockeyStack documented its own version in a playbook on launching research reports. The company published 18 original reports built entirely on anonymized first-party customer data, the kind of data no competitor could replicate.

Its process includes the same steps the Fivetran page demonstrates: list the data points needed, have a teammate pull them with SQL, define and document the method so the numbers can withstand scrutiny, and structure the report around a real ICP question. HockeyStack calls methodology non-negotiable because without it, someone will always dispute the data.

With AI analysis, pulling the data is often the easier part now. Building the content into something citable, trustworthy, and durable enough to keep earning visibility for commercial queries years later is where the harder work sits.

What sites are already trusted for your topic? When a benchmark you did not publish is earning the citations in your category, the Citation Source Mapper can map that trusted set into a ranked, pitchable target list. It is available in the premium library.

This post first appeared on the author’s website and is republished here with permission.

Inspired by this post on Search Engine Land.

July 8, 2026
How I Build a Brand AI Search Can Trust and Recommend

For most of the past decade, I treated organic marketing as a visibility game. I wanted brands on Page 1, inside featured snippets, and in front of the people already searching.

That north star has moved.

When I spoke at SMX Advanced on June 5, the question I put to the room was not simply, “How do I get a brand found?” The harder question was, “How do I get that brand chosen?”

In 2026, those answers are no longer the same. The distance between being discovered and being selected is where I see many brands losing ground.

In AI search, my reputation shows up first

The old user journey was messy and multi-step. People explored, compared, checked reviews, read Reddit threads, visited comparison sites, and moved toward a decision over time. Now, a single AI prompt can compress much of that process into one synthesized answer.

AI search does not reward the brand that shouts the loudest in paid media or stuffs the most keywords into metadata. I see it rewarding the brand with the strongest reputation in the places that matter. Reddit discussions, review sites, comparison pages, expert commentary, forums, and editorial coverage are all being absorbed by large language models and blended into recommendations.

In other words, my brand is no longer defined only by what I say about it. It is shaped by how AI understands it, and AI is reading what everyone else has said, too.

Owned content on websites and social channels will always carry a promotional bias. AI systems look for outside validation to support, challenge, or clarify those claims.

That changes the work of organic marketing. I can no longer stop at visibility. I have to build a brand that is found, correctly understood, and ultimately chosen. Those are three separate challenges, and I need a strategy for each one.

Found: I need to appear where my audience actually looks

The first challenge is still discoverability, but the canvas is much wider than Google. People now discover brands through ChatGPT, Reddit, YouTube, TikTok, Google, Quora, LinkedIn, and word of mouth. I have to understand which of those entry points matter most to the specific audience I want to reach.

That starts with mapping the sources my audience genuinely trusts: the publications, platforms, communities, creators, analysts, newsletters, and peer groups that influence their decisions. The intersection of semantic relevance, domain authority, and audience affinity tells me which third-party properties are worth pursuing.

For one B2B audience, that might mean Wired, Tom’s Guide, or an active LinkedIn group where buyers discuss vendors in a specific vertical. For another, it might be r/smallbusiness or a Substack newsletter with 40,000 engaged subscribers.

Once I know where the audience spends time, I can create useful content, earn credible mentions, and participate in the conversations already shaping decisions. This is audience-first, performance-driven PR and organic strategy, not generic brand awareness.

AI search leans heavily on outside validation: this chart shows third-party communities, reviews, and earned media driving 93% of citations versus 7% from owned channels.

The data makes the case even stronger. Across the top commercial sectors analyzed, 93% of AI search citations came from third-party sources. If I only invest in content on my own domain, I risk being invisible to the systems now doing much of the brand discovery work.

Understood: I need consistent signals everywhere

Getting found matters, but it is not enough on its own. If machines are surfacing my brand, they also need to understand it accurately.

LLMs do more than crawl my website. They build a consensus picture from everything available online: reviews, Reddit discussions, press coverage, YouTube commentary, Trustpilot ratings, forum threads, and more. If those signals conflict with the story I am telling about myself, I have a real problem.

If I claim premium positioning while thousands of articles question whether the brand is truly luxury, heavy discounting is part of the public record, and review scores are poor, AI is unlikely to recommend that brand as a premium option. The model has read the broader story, not just the homepage copy.

That is why brand messaging consistency has become an SEO issue. Owned, earned, and paid content all need to reinforce the same core associations. Conflicting signals do not just confuse customers; they can weaken AI visibility.

Digital PR plays a critical role here because it helps shape the external narrative. Through strategic media placements, expert commentary, and search-informed coverage, I can influence what journalists write, what audiences remember, and what models learn.

I also have to think beyond one obvious keyword. The query fan-out, or the range of prompts a potential customer might use, requires positive and consistent answers across every touchpoint an LLM might evaluate.

Chosen: I need trust signals that influence the decision

The third challenge is the hardest and probably the most important. Trust has always been an SEO currency, but as clicks decline and zero-click search becomes more common, trust matters even more.

According to an Ahrefs study, brand appearance in AI Overviews is most strongly correlated with branded web mentions. In practical terms, that means the number of times a brand is positively named across authoritative third-party sources is becoming one of the most powerful signals organic marketers can influence.

That is also the core output of strong digital PR. Based on the last 4,000 pieces of U.S.- and U.K.-based coverage driven for clients, 91% of AI search citations included expert insight rather than branded content or product pages.

That tells me expert-backed, editorially independent coverage is critical. Internal experts are now one of the most valuable assets a brand has. Brands that invest in real thought leadership, original research, and data-backed studies are giving both people and AI systems stronger reasons to trust them.

The three content formats I see consistently supporting LLM inclusion are product roundups and listicles that place a brand inside trusted “best of” editorials, reliable data-backed research that journalists and LLMs can cite, and expert thought leadership that positions real people as credible voices in their category.

A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.

What does not work is chasing inauthentic mentions through artificial link schemes, fake expert personas, or manufactured coverage. Google has already flagged these kinds of tactics in its GEO guidance, and models are getting better at distinguishing genuine authority from manipulated signals.

The reputational risk is also high. If I try to manufacture authority and get caught, I do not just lose visibility. I damage the trust I was trying to build.

This cannot be a one-time effort. Multiple studies, including research from Waseda University, have identified a correlation between AI brand visibility and content recency.

Brands that maintain a steady flow of credible, expert-backed third-party coverage do not just appear more often in AI responses. They appear with more confidence.

Frequency and freshness both matter. A one-off PR campaign is not enough. I need to treat credible external validation as an always-on strategic investment.

The framework I use in practice

When I think about brand discovery in 2026, I come back to three words: found, understood, and chosen.

Found: I map the audience’s real sources of influence and make sure the brand is credibly present across the fragmented ecosystem where discovery now happens.

Understood: I work to make sure everything said about the brand tells a consistent story, matches the desired positioning, and reinforces the associations that drive preference.

Chosen: I continuously build genuine trust signals through earned coverage, expert commentary, and third-party validation, so that when a person or machine compares the brand with a competitor, credible external evidence tips the decision in my favor.

The brands winning in organic search right now have not unlocked some secret technical trick. They have built reputations worth recommending, and they have made sure machines can understand those reputations clearly.

That is where I believe organic marketing has to go next. Instead of chasing the algorithm, I need to build something worth finding, worth understanding, and worth choosing.

Inspired by this post on Search Engine Land.

July 7, 2026
Prompt-Level AI Visibility: How I Measure What Matters

I do not measure AI search the same way I measure traditional search, because the user journey is no longer built around one query, one ranking page, and one click.

A prospect might ask ChatGPT for the best CRM for manufacturing companies, compare options in Google AI Mode, refine the requirements across several follow-up questions, and build a shortlist without ever visiting a website.

If my company appears in those conversations, I have influenced the buying process. The hard part is proving that influence with a measurement system I can trust.

Prompt-level visibility has become one of the fastest-growing areas of AI search optimization. It is also one of the easiest to misunderstand. I see plenty of promises about complete visibility into AI conversations, but the reality is far more complicated.

Here is how I think about what can be measured today, what cannot be measured reliably, and how I would build useful reporting despite the current limits.

A 5-step framework I use to track AI visibility

1. I accept that AI does not have traditional rankings

The first mistake I avoid is trying to recreate an old SEO ranking report. There is no universal position one inside ChatGPT.

The same prompt can produce different responses depending on conversation history, user location, personalization, follow-up questions, model version, web retrieval availability, and timing.

That means visibility is probabilistic rather than deterministic. Instead of asking, "Do we rank?" I ask, "How often are we included across the conversations that matter?"

That shift changes the entire measurement model.

2. I build a prompt library instead of only a keyword list

Keywords still matter, but I no longer treat them as enough on their own.

Instead of tracking only individual search terms, I build a library of prompts that reflects how real buyers research, compare, validate, and challenge their options.

I usually organize those prompts by intent. Discovery prompts ask for the best platforms in a category. Comparison prompts put vendors side by side. Evaluation prompts focus on specific use cases. Validation prompts ask whether a company is worth the cost. Objection prompts explore disadvantages. Alternative prompts ask what to use instead. Implementation prompts test how difficult a product may be to adopt.

Instead of monitoring 10 keywords, I may monitor 200 to 500 prompts across the full buying journey. That gives me a much more realistic view of AI visibility.

3. I measure prompt clusters, not isolated questions

One prompt rarely tells me enough to make a decision.

For example, "best CRM software" might not mention my company, while "best CRM for manufacturing companies" might. A more specific prompt, such as "CRM for manufacturers with field sales teams," could return a different set of recommendations altogether.

That is why I group similar prompts into clusters. A category cluster might include best project management software, best PM platform, and project management tools. An industry cluster might include best CRM for healthcare, manufacturing, and finance. A feature cluster might include CRM with AI automation, forecasting, or enterprise sales support.

The patterns across those clusters are more reliable than the result from any single prompt.

4. I combine synthetic prompts with real customer questions

This is where measurement becomes more difficult.

Most organizations do not know exactly what customers are typing into AI assistants, so I often start by generating synthetic prompts. That may include expanding keyword research into conversational questions, creating AI-generated prompt variations, and building comparison, objection, and follow-up prompts.

Synthetic prompts are useful because they are repeatable, but I do not treat them as perfect. Generated prompts often sound cleaner and more structured than real user behavior.

A real buyer might ask something much richer, such as: "We are a 250-person SaaS company with a small HR team. We already use Workday but need something better for payroll. Budget is not a huge issue. What would you recommend?"

That is much more useful than a short phrase like "best payroll software."

For the strongest measurement program, I use synthetic prompts for consistent benchmarking and then supplement them with real questions from sales calls, customer interviews, support conversations, community discussions, internal search logs, on-site search, and AI transcripts that customers voluntarily share.

I also assume the prompt library will need to change. Customer language evolves, and the measurement set has to evolve with it.

5. I measure multi-turn conversations

Most AI-assisted buying journeys do not happen in a single prompt. A buyer may start by asking for the best cybersecurity vendors, narrow the list to companies strong in healthcare, ask which ones integrate with CrowdStrike, and then compare pricing.

My company may not appear in the first answer, but it may become highly recommended by the third response.

If I only measure the opening prompt, I miss a large share of meaningful visibility.

That is why I want prompt tracking to evaluate full conversation paths, not just one-shot questions. Multi-turn testing often reveals patterns that single prompts hide.

The AI visibility metrics I care about most

Many traditional SEO metrics do not translate neatly to AI search. Rankings, clicks, and impressions still have value, but they no longer tell the whole story.

I focus on measurements that show whether a brand appears, how it is positioned, and how consistently it is recommended inside AI-generated responses.

Inclusion rate

If I could track only one AI visibility metric, I would start here.

Inclusion rate measures the percentage of tracked prompts where my brand appears in the AI response. If I monitor 500 prompts and my company appears in 185 of them, the inclusion rate is 37%.

That number is useful as a benchmark, but it becomes more valuable when I segment it by buying stage, product category, industry, geography, or AI model. Those slices often reveal opportunities that a single overall average would hide.

Position within the response

Being mentioned is not the same as being recommended.

Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.

I want to know whether my brand appears as the first recommendation, one of the first few options, a late mention, or merely an alternative. If the AI response includes a comparison table, I also want to know where my company appears there.

AI answers do not have traditional rankings, but prominence still matters. A top recommendation is more likely to shape a buyer’s perception than a passing mention several paragraphs later.

Brand framing

Visibility tells me whether my brand is included. Brand framing tells me how it is described.

There is a meaningful difference between an AI system describing a company as "widely considered an enterprise leader" and describing it as "best suited for smaller teams." Both may sound positive, but they position the brand very differently.

I look for recurring themes around strengths, weaknesses, differentiators, pricing, ideal customer profile, and competitive comparisons. Over time, those patterns can expose messaging gaps in my own content or show how the broader web is shaping AI’s understanding of the brand.

Sentiment and confidence

Sentiment is more than a simple positive-or-negative label. I also want to know how confidently the AI system presents my brand.

"Company A is generally considered the strongest option" carries a very different level of conviction than "Company A may be worth considering."

Neither statement is negative, but they do not create the same buyer impression. Tracking confidence, uncertainty, caution, skepticism, and strong endorsement gives me a more nuanced view of how AI systems present the company to prospective customers.

Competitive share of voice

My own visibility is only part of the picture. I also need to know how often competitors appear alongside me or instead of me.

If my inclusion rate stays at 40% month after month, that may look disappointing. But if every major competitor dropped by 20 percentage points after a model update, the story changes.

On the other hand, if one competitor jumps from 35% inclusion to 70% while everyone else stays flat, I would want to investigate what changed.

Competitive share of voice helps me separate category-wide movement from changes that are specific to my brand.

How I view the AI visibility tool landscape

The market for AI visibility platforms has grown quickly. Each product approaches the problem differently, but most are trying to answer the same core questions: does my brand appear, how often does it appear, which AI models include it, which competitors show up, and how is the brand described?

Many platforms now include prompt libraries, competitive benchmarking, citation tracking, answer monitoring, and trend reporting. These features can reduce the manual work required to test hundreds or thousands of prompts on a recurring basis.

Still, I have to be clear about what these tools are and are not measuring.

No tool has access to every AI conversation happening in the wild. Most rely on controlled prompt libraries, repeatable testing environments, or sampled interactions to create a representative view of visibility.

That is useful, but it is not the same as observing every real user interaction.

What I still cannot reliably track

This is the part I do not want to gloss over.

Even though AI measurement is improving quickly, some data is still not observable. I cannot comprehensively track every prompt where my brand appeared, every conversation that influenced a purchase, every recommendation made inside ChatGPT, every citation shown to every individual user, or exactly how personalization changed a response.

I also cannot see every multi-turn conversation across every AI platform or know how often someone acted on an AI recommendation without clicking a link.

The underlying AI platforms do not expose that level of data. If a vendor claims it can see every AI conversation involving my brand, I would ask exactly how that information is being collected.

The practical framework I would build

Rather than chasing perfect attribution, I focus on building a repeatable measurement system that I can track consistently over time.

For visibility, I would track inclusion rate, competitive share of voice, prompt coverage, and model coverage.

For response quality, I would track position within the response, brand framing, sentiment, and message consistency.

For technical signals, I would track citation frequency, content retrieval success, entity consistency, and freshness.

For business outcomes, I would look at AI referral traffic, assisted conversions, branded search lift, direct traffic trends, and pipeline influenced by AI discovery.

No single metric tells the full story. Together, these signals give me a more complete picture of how the brand is showing up and how it is being perceived across AI-assisted research.

The goal is not perfect measurement

Prompt-level visibility is not as mature as keyword tracking became over the past two decades.

Some signals are still emerging. Others remain inaccessible because AI platforms do not expose the underlying data. At the same time, user behavior is changing almost as quickly as the technology itself.

That does not mean measurement is impossible. It means the objective has changed.

Instead of trying to reconstruct every AI conversation, I focus on building a representative prompt library, tracking visibility consistently, benchmarking against competitors, and understanding how my brand is being framed.

Those trends are far more actionable than chasing a level of precision the current ecosystem cannot support.

The organizations making the most progress in AI search are not waiting for perfect attribution. They are establishing baselines, watching for meaningful movement, and adapting as both AI models and user behavior continue to evolve.

Inspired by this post on Search Engine Land.

July 6, 2026
Why Paid Media Is Now a Powerful AI SEO Investment

I believe the lines between paid media, PR, and SEO have officially disappeared.

When I look at baked-in YouTube sponsorships, native UGC, and third-party review incentives, I no longer see them as separate from SEO. I see them as the modern equivalent of buying a high-DA backlink. When I fund these channels, I am investing in the information sources that shape how AI systems understand, evaluate, and recommend a brand.

A recent social media screenshot made this shift especially clear to me. A B2B brand was offering a $250 Amazon voucher to anyone who wrote a review on G2.

To a growth marketer, that may look like a familiar user acquisition tactic. But as an SEO, I saw something more important: a direct investment in the semantic infrastructure AI systems use to judge brands.

The evolution of the authority signal

To understand why I consider a $250 G2 voucher or a paid YouTube sponsorship an SEO strategy, I have to look at how LLMs now define authority.

Authority used to feel transactional and mathematical. You built or bought hyperlinks, and those links helped determine how trusted a page or brand appeared to search engines.

When I moved from link building into digital PR and influencer marketing, I realized Google was getting smarter. I could not rely on links alone. I needed unlinked brand mentions, high-tier media coverage, and contextual relevance. In many ways, I was optimizing for Google’s Knowledge Graph.

Today, retrieval-augmented generation (RAG) systems and LLMs do not just count links or parse knowledge graphs. They look for semantic consensus across the web.

When an AI engine like Perplexity or ChatGPT answers a user query, it crawls the data ecosystems it trusts most for that specific topic. For software, that often means G2 and Reddit. For consumer products, it may mean TikTok transcripts, YouTube, and forums.

So when I pay $250 for a G2 review, I am buying a dense, text-based data point that an LLM can use to understand my brand’s sentiment, use cases, and vector positioning. I am strengthening the signals AI systems may use when deciding whether to recommend my brand.

The permanent ad: Why sponsorships and UGC are the new organic infrastructure

This reality breaks the traditional separation between paid media and SEO.

The path to AI search visibility now runs beyond links: from PageRank and PR mentions to consistent brand signals across the datasets LLMs rely on.

Historically, paid ads were temporary. I turned off the budget, the traffic stopped, and SEO had to carry the long-term work. If I run a dynamic programmatic ad on YouTube or a banner ad on a website, that old model still applies because LLM web scrapers generally ignore dynamic ad placements.

But baked-in influencer sponsorships, native user-generated content, and podcast reads behave differently because they become part of the content itself.

First, there is the hardcoded transcript. When a YouTuber reads a native sponsor segment such as, “I use Brand X to manage my business taxes,” that message is baked into the video file, and YouTube automatically transcribes it.

Then comes LLM ingestion. When an LLM crawls the web, or when a multimodal AI watches the video, those spoken words can be indexed. The AI can associate the brand with the semantic concept of business taxes.

That creates a new half-life for paid media. Long after the campaign ends and the initial views slow down, the transcript can remain part of the information an LLM can access.

As someone who spent years bridging the gap between digital PR and SEO, I used to judge a campaign’s ROI by immediate referral traffic, brand search lift, and backlink quality. Now, I also have to think about the algorithmic half-life of my creative assets.

Activating the convincer: Bringing paid and PR into the visibility supply chain

The visibility supply chain treats content like an industrial product that passes through strict organizational “gates” before it enters the digital ecosystem. In that model, companies need a strategic duo: the hacker, or technical architect, and the convincer, or cross-departmental visibility advocate.

This convergence of paid media and AI visibility is exactly where I believe the convincer has to step in.

If my paid media team is buying YouTube sponsorships based only on demographic reach, or if my product marketing team is buying G2 reviews just to hit a quarterly quota, we may be damaging LLM visibility without realizing it.

The reason is simple: LLMs need information density and semantic alignment.

If a user writes a rushed, generic review like “Great tool, highly recommend!” just to receive a $250 voucher, it may pass the human layer, but it fails the machine layer. To a RAG system, that sentence is low-density noise.

Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.

The convincer’s job is to realign the review strategy and help internal teams understand how every initiative can build LLM visibility.

For example, I would rather incentivize users to write detailed, context-rich problem-and-solution statements, such as: “We used Brand X to solve our cross-border compliance issues in Europe.” That gives AI the entity-relationship mapping it needs to recommend the brand for cross-border compliance.

The new marketing playbook: Optimizing dataset partnerships

If I want a brand to be recommended by AI systems, I have to study where the major AI players are getting their data.

We know OpenAI and Google have struck multimillion-dollar deals to train on Reddit’s real-time firehose. We know Grok trains on X. We also know Apple and others are licensing major journalistic archives.

That means target audience research is no longer just about finding where customers spend time. For me, it is also about dataset matching.

If I am planning an influencer campaign, a digital PR push, or a community-building initiative, I need to ask one critical question: Is this content entering a data pipeline that the primary LLMs trust and crawl in real time?

Stop optimizing pages. Start optimizing budgets.

I no longer believe SEO can be isolated inside a technical department or limited to a content blog. That does not reflect how AI visibility is built anymore.

The next time I sit in a budget allocation meeting and see a line item for influencer marketing, podcast sponsorships, or third-party review incentives, I will not treat it as temporary media buying.

I will reframe it as infrastructure. I am building the digital foundation of a brand’s AI persona. I am buying the AI equivalent of backlinks. If I do not intentionally structure those paid assets to feed the visibility system, I am leaving the brand’s future visibility up to chance.

Inspired by this post on Search Engine Land.

July 6, 2026
GraphRAG SEO: Why Entity-First Retrieval Matters

Making a brand machine-readable and improving its odds of being selected for AI-generated answers are important, but I see them as only part of the larger shift. Under the surface, a retrieval layer is changing how AI systems identify entities, connect facts, and decide which brands deserve to be cited.

That layer is GraphRAG. Once I understand how it works, “optimize for AI” stops feeling like a vague instruction and starts looking like a practical SEO strategy.

What is GraphRAG, actually?

GraphRAG extends traditional retrieval-augmented generation (RAG) by adding a knowledge graph. That graph helps AI understand entities and the relationships between them, instead of treating content as disconnected text fragments.

Microsoft Research introduced GraphRAG in 2024, and a broader ecosystem has formed around it since then. Instead of pulling from a flat sea of text chunks, GraphRAG builds a map.

In that map, nodes are the entities: a company, product, person, certification, location, or concept. Edges are the relationships between those entities, such as “offers,” “is certified by,” “authored,” or “operates in.”

I think of it as a system of things and the lines connecting them. When a model works from a map instead of a pile of scraps, it does not have to guess its way toward an answer. It can follow the relationships.

If the map says Entity A holds Certification B in Region C, the system can follow that path with confidence instead of inferring the connection and hoping it is right. That is why graph-based retrieval can produce more complete, better-grounded answers to complex questions with fewer hallucinations.

Microsoft described this failure mode in its GraphRAG patent, “Knowledge Graph Extraction” (US20250131289A1). The patent calls out a recall problem in naive RAG: a less prominent entity can disappear inside chunk embeddings, which means the system may retrieve nothing useful.

It also describes one of the fixes: entity resolution. When duplicate spellings or variations of the same thing are merged, the system can treat them as one entity instead of scattering their authority across several weak signals. That is one of the core building blocks behind graph-based retrieval.

Dig deeper: What patents reveal about the foundations of AI search

Why strong content still gets passed over

Traditional RAG works by chopping content into fixed chunks, turning each chunk into a vector, and storing those vectors in a database. When I ask a question, the system retrieves the closest chunks in vector space and passes them to a language model to generate an answer.

That can work for simple questions like “What is the capital of France?” It struggles with the questions that usually matter most in business: the multi-step questions.

If I ask a system to find a provider that offers a specific service, holds a specific certification, and operates in a specific region, naive RAG may stitch together an answer from scraps that merely sound related. It does not truly understand how the facts connect, so it guesses across the gaps.

When a system has to guess, the safer move is often to leave a brand out rather than risk saying something inaccurate about it. That is the part I think many SEO teams need to sit with.

This explains a common frustration: “Our content is strong, but AI systems still do not cite us.” The issue may not be content quality. GraphRAG consistently outperforms naive RAG on complex, multi-hop questions where vector search falls apart. That is where the visibility leak often starts.

In many cases, the machine could not reliably tell what the brand is, how its facts fit together, or whether it could trust those relationships enough to cite the brand by name.

The three problems GraphRAG is built to fix

I see GraphRAG lining up with three SEO problems that show up again and again: disambiguation, attribution, and relationships.

Disambiguation matters when the same entity appears under different names and gets counted as several weaker signals instead of one strong one. If “the firm,” “the agency,” and the actual brand name never resolve to a single entity, authority gets split.

Attribution matters when the fact survives but the credit disappears. When content is blended into an AI answer, the brand behind the original insight can easily vanish.

Relationships matter when the connections that give expertise meaning stay buried in prose instead of being declared in a way a machine can read.

If I have ever watched AI repeat something a company wrote without naming it, or credit a competitor for a specialty the company actually owns, I have seen all three problems in action.

What ties them together is simple: this is not only a content problem. It is an identity problem.

Same sentence, more machine-readable context

I want to make the idea of an entity concrete, because it can become abstract quickly. I will use one real-world example and one fictional example.

Start with Wayne Gretzky. Search his name in almost any AI client and I expect to see a confident summary: facts, former teams, records, and related links. That confidence is not luck. It is what a well-established entity looks like. His identity is nailed down and agreed upon across the web, so the system does not have to guess who he is.

Now imagine the opposite. Picture a goaltending coach in Moncton. I will call her Marie Tremblay. Her About page says: “Our head coach, Marie ‘Lefty’ Tremblay, has run elite goaltending camps across the Maritimes for 20 years.”

That is a good sentence. A parent understands it immediately. I would not rewrite it into robotic prose just to satisfy a machine. Optimizing for AI does not mean abandoning human voice.

The better move is to keep the sentence and add context around it. I need to make explicit what a human reader infers automatically.

That means clarifying that “Lefty” and “Marie Tremblay” are the same person. It means connecting Marie to the academy, to goaltending as a discipline, and to the Maritimes as the region she serves. It also means making “20 years” and “elite” verifiable claims rather than loose adjectives.

A human gets all of that from one sentence. A machine may not. My job is to close the gap between what the reader understands and what the system can verify, so Marie becomes as legible to AI retrieval systems as a famous entity like The Great One already is.

Why a flat triple is no longer enough

Knowledge graphs are built on triples: subject, predicate, object. “Acme offers consulting” is clean and useful, but it is flat. A bare triple cannot easily carry the high-stakes details that matter, such as whether the relationship is true, where it applies, who says so, and what evidence supports it.

The standards community is working on that gap. The W3C is extending the model with Resource Description Framework (RDF)-star, which allows site owners to make statements about statements. In practice, that means source, date, confidence, and other metadata can attach directly to a relationship instead of floating around as a disconnected claim. It is moving through the RDF 1.2 standardization process, with the RDF 1.2 Primer serving as a plain-English introduction.

Microsoft’s GraphRAG patent points in a similar direction. It pulls claims into a subject-action-object structure and weights relationships by how often they appear, instead of treating every stated link as equally reliable.

The practical lesson is clear to me: the future is not just saying two things are related. It is saying they are related and showing the proof in a form a machine can verify. A richer triple beats a flatter page.

The publishing layer is starting to respond

I am also watching the publishing layer, because that is where the shift is becoming visible outside the models themselves.

On June 1, the new open standard EntityMap launched a 33-day public consultation ahead of its July 1 launch. It was started by Fred Laurent, CTO of InLinks and Waikay, with backing from Dixon Jones. For anyone following entity SEO and the move from “strings to things,” those names matter.

The concept is deliberately familiar. Where sitemap.xml tells search engines which pages exist, an entitymap.json file tells AI systems what an organization knows: which entities it covers, how they relate, and where the evidence lives.

EntityMap aims at the same three problems: disambiguation, attribution, and relationships. It also builds in the richer-triple idea by allowing declared relationships to carry receipts, including a source URL, publisher, and timestamp.

I would treat it as a signal, not a mandate. EntityMap is a proposal in consultation, not a requirement. No major engine has committed to reading files like these, so I would not turn it into another box-checking exercise yet. The important point is that credible people are building entity-first publishing standards, and that direction is worth watching.

The honest state of GraphRAG

I do not think GraphRAG belongs in hype territory, because two realities keep it grounded.

First, GraphRAG is expensive. Building the map requires a language model to extract entities and relationships, and that is the costly part. By Microsoft’s own estimate, graph extraction accounts for roughly 75% of indexing costs. That LLM cost is one reason web-scale, real-time graph retrieval has not taken over everything overnight.

Second, the cost curve is bending. Recent research is attacking the infrastructure problem directly, including TurboQuant, a vector compression method from Google Research and NYU, presented at ICLR 2026. It reduces the memory footprint of vectors these systems traverse while preserving quality well enough to make the economics more interesting.

That does not mean every engine is running GraphRAG across the open web today. It means the economics are improving, which helps explain why entity-first standards are emerging now. I am cautious about anything framed as inevitable, but this shift makes practical sense.

Structured data still matters. Schema.org markup, a clean Knowledge Panel, consistent NAP, and strong entity signals are not going away. Entity-first work extends that discipline. It does not replace it.

My entity-first action plan

Here is how I would make this practical without betting everything on one standard.

Inventory entities, not just keywords. I would go beyond the search terms that historically brought traffic and list the things the brand genuinely knows about: products, services, people, methods, concepts, locations, and credentials. That becomes an entity map, whether or not it ever gets published as a formal file.

Disambiguate, then connect to the graph. I would claim and confirm the brand’s Wikidata entity and Google Knowledge Panel where possible. I would standardize naming, resolve variants, and keep sameAs links consistent across structured data. This is how “Lefty” and “Marie Tremblay” become one clear identity instead of two weak signals.

Make relationships explicit. I would use Schema.org types and properties such as Organization, Person, Product, knowsAbout, sameAs, and author so expertise is declared rather than implied. I would also mirror those relationships in internal linking.

Attach evidence to every claim. I would connect important facts to verifiable sources: named authors, first-party data, citations, documentation, and dated references. Graph-based systems increasingly need proof behind a relationship, not just the assertion.

Front-load defining facts. Retrieval still works through narrow windows, so I would place the clearest, most verifiable statement of what the brand is and what it does near the top of important pages.

Watch the publishing layer without overcommitting. I would read the EntityMap spec, follow how it develops, and decide later whether an entity index belongs in the stack. Schema.org work should continue either way.

Tie the entity map to revenue. I would map entity coverage to the queries and answer surfaces that influence leads, sales, margin, and retention. That helps leadership see entity work as revenue protection, not an academic exercise.

Measure what AI systems can recognize

Rankings and clicks still matter, but they describe the old search-page model. I would add metrics that show whether AI systems can recognize, trust, and cite the brand.

AI citation share measures how often the brand is named or cited in AI answers compared with competitors. I would track it monthly with an AI visibility tool.

Entity recognition asks whether priority entities have confirmed Knowledge Panels, Wikidata entries, and consistent identity signals. It is simple, but foundational.

Relationship completeness looks at how many priority entities have explicit, marked-up relationships and consistent sameAs links.

Attribution rate tracks how many core claims are backed by linked, verifiable evidence.

Answer-equity proxies include branded-query lift, assisted conversions from AI referrals, and lead stability as raw click volume softens. These business signals help show whether authority is compounding even when CTR is harder to read.

Where graph-based retrieval is heading

I expect graph-based retrieval to keep moving toward multimodal graphs, where text connects to images, audio, video, and structured data. I also expect more streaming and incremental indexing for live data, plus domain-specific ontologies for areas like medicine, finance, and law.

The move from strings to things is gaining momentum. The brands that stay visible will not simply be the ones publishing the most content. They will be the ones machines can understand without guessing, with clear entities, explicit relationships, and claims backed by evidence.

I do not need to wait for a new standard to launch before preparing. I can make a brand more legible now to systems that do not just read pages, but read what the brand knows. In the answer economy, I see the real battleground as identity, not just content.

Inspired by this post on Search Engine Land.

July 3, 2026
Travel AI Optimization Strategies That Get Cited

I’m seeing a major shift in how people plan trips: 40% of travelers now use AI to research, compare, and organize their travel decisions.

That changes how I think about travel content. It is no longer enough to write only for traditional search results. I also need to make content clear, useful, and easy for AI systems and large language models to understand, summarize, and cite.

In this guide, I focus on practical travel AI optimization strategies, including stronger FAQs, schema markup, topical authority, and a content strategy built around the questions real travelers ask.

My goal is simple: create travel content that answers intent directly, builds trust, and gives AI platforms the structured context they need to reference my brand when travelers are planning their next trip.

Inspired by this post on HiGoodie Blog.

July 3, 2026
AI Search Trust Is Falling: What Marketers Must Fix

A year ago, I saw 82% of consumers say AI-powered search was more helpful than traditional search. By 2026, that number had fallen to 54%, a 28-point drop in sentiment in just 12 months.

That does not mean people are abandoning AI search. In fact, 70% of consumers say they are using AI tools for search more than they did last year. The tension is clear: adoption is rising, but trust is slipping.

That is the core issue I believe search marketers need to solve in 2026. It is no longer enough to appear in AI answers. I need my brand, and the brands I work with, to be visible, accurate, credible, and trusted when AI systems surface information.

To understand the shift, Fractl partnered with Search Engine Land to expand our 2025 research. We surveyed 1,008 U.S. consumers and 150 marketers to compare how consumer trust, marketer adoption, and brand strategy are changing in the AI search era. Disclosure: I am the co-founder of Fractl.

Here is what I believe the data means for 2026 search strategy.

Consumers are using AI more, but trusting it less

AI search adoption is no longer the main story. Seventy percent of consumers report increased use of AI tools for search over the past year, while only 3% say their use has decreased. The bigger question is whether people trust what those tools return.

One surprising finding is that baby boomers now find AI more helpful than Gen Z, 63% to 47%. That challenges the assumption that younger users automatically embrace AI while older users lag behind. What I see instead is a more complicated market where trust has to be earned across every generation.

In 2025, only 3% of consumers said AI was less helpful than traditional search. By 2026, that skeptic group had grown to 17%, nearly six times larger than the year before. Even among the 54% who still find AI helpful, enthusiasm is softer: 37% say it is only somewhat more helpful, while 17% say it is much more helpful.

I think hallucinations and low-quality AI content are changing how people evaluate the entire channel. Consumers may use AI because it is convenient, but convenience does not automatically create confidence.

AI content volume has become a brand trust risk

In 2025, 20% of consumers said heavy AI use would reduce their trust in a brand. In 2026, that number rose to 39%. For me, that makes AI content scale a reputational issue, not just an operational decision.

If I publish AI-assisted content at scale without disclosure, strong editorial standards, or obvious quality signals, I am asking my audience to trust a process they are increasingly skeptical of. That is a risk more brands need to take seriously.

Gen Z is especially strict. Fifty-four percent of Gen Z consumers say heavy AI use in a brand’s marketing would decrease their trust, compared with 32% of baby boomers and 33% of Gen X. Women are also more likely than men to penalize brands for heavy AI use, 44% vs. 34%.

That matters because Gen Z is often the audience most likely to engage deeply, share content, shape online conversations, and influence long-term organic visibility. If that audience matters to a brand, AI-generated filler is not a harmless shortcut.

Disclosure is now a consumer expectation

Across every major content format, more than 80% of consumers want AI-generated content labeled. Video leads at 91%, followed by images at 90%, audio at 87%, and written content at 84%. More than half of respondents strongly agree with labeling in every category.

I do not read that as a mild preference. I read it as a near-universal expectation. The brands that treat AI disclosure as optional are creating a gap between how they operate and what their audiences want.

Consumers still believe AI will shape the future of search. Sixty-four percent agree that AI will replace traditional search engines within five years, nearly unchanged from 66% in 2025. The channel is not going away. But being present in AI results and being trusted in AI results are now two different challenges.

Google still leads on trust, especially for buying decisions

When consumers are making purchase decisions, 39% turn to Google first. Reddit follows at 15%, AI tools at 14%, and review sites and friends or family each at 11%. The trust people have built with Google has not automatically transferred to AI tools.

Platform preference also changes by query type. Google dominates five of six major search categories. It is the first stop for local businesses, product research, travel planning, and health questions. YouTube overtakes Google for how-to content, while ChatGPT is now the second-most-used destination for health questions and ranks strongly for product research, travel planning, and how-to content.

That tells me there is no single AI search platform to optimize for. I need to map content strategy to actual user behavior: where people search, what they are trying to decide, and which platforms influence confidence at each stage.

Before making a purchase decision, the average consumer checks 2.4 platforms. Gen Z checks 2.5, millennials 2.4, Gen X 2.3, and baby boomers 2.2. This behavior is consistent enough that I now think of search optimization as a multi-platform visibility strategy, not a rankings-only discipline.

A brand that appears in Google results but nowhere else can lose to a brand that appears in Google, shows up in Reddit discussions, gets cited by ChatGPT, and has strong third-party review content. Visibility now has to travel with the buyer.

AI is changing marketing operations quickly

AI now touches 53% of marketing work on average, up from 38% in 2025. In practical terms, the equivalent of one full workday per week has shifted to AI-assisted workflows in just 12 months. Fifty-nine percent of marketers say AI is involved in at least half their work, while 27% say it is involved in three-quarters or more.

For SEO and content teams, this means competitors are moving faster. But speed alone is becoming commoditized. Accuracy, original insight, expert judgment, and brand credibility are much harder to copy.

Marketers are also feeling pressure to adopt AI. Fifty-five percent of marketing roles report a 7-out-of-10 level of pressure to use it. SEO and analytics teams feel that pressure most, while PR is not far behind. As AI makes generic content easier to produce, the advantage shifts toward what AI cannot automate well: judgment, relationships, trust, and reputation.

The quality tradeoff is real. Only 26% of marketers say AI made their work both faster and better. Nearly half say it made their work faster but more generic, and 7% report an outright quality decline.

That is where I see a major competitive opening. If other teams are scaling generic AI content while I invest in original data, expert quotes, third-party validation, and earned brand mentions, I am building assets that are more visible, credible, and retrievable across search engines, social platforms, and LLMs.

AI governance is still too weak

About three in four organizations conduct human editorial review before publishing AI-generated content. Sixty-two percent check for brand voice, 54% check facts, and 42% conduct legal or compliance review. Only 27% evaluate content for bias.

That means nearly half of AI-generated content may enter the market without fact-checking, legal review, or plagiarism checks. Too many teams are still relying on surface-level review: Does it sound right? Is the tone appropriate? Are there typos?

In a year when consumers are already prepared to distrust generic AI content, I see governance as one of the cheapest gaps to close and one of the most expensive to ignore.

The disclosure gap is just as serious. Heavy, generic AI use is now a brand-trust liability, yet only 20% of organizations always disclose AI use to their audiences. Compare that with the 84% average consumer demand for labeling written content, and the disconnect is obvious.

The takeaway is not to abandon AI. It is to stop treating governance as optional. Every AI workflow needs accuracy checks, transparency standards, bias review, and human accountability before content reaches an audience.

AI hallucinations are already a brand problem

A year ago, about 22% of marketers tracked LLM visibility. In 2026, that figure barely moved to 24%. At the same time, 27% of brands have already been misrepresented in AI-generated responses, and 14% say an AI inaccuracy has affected a customer relationship, sale, or PR situation.

More brands have been misrepresented by AI than have a formal monitoring process. That should concern every search and communications team.

If AI is summarizing my category, comparing my product, or explaining my brand incorrectly, that is not only an SEO issue. It is a reputation risk, a revenue risk, and a PR issue waiting to escalate.

When AI misrepresents a brand, I believe fixing the source matters more than arguing with the output. That can mean reaching out to publishers for updates, correcting owned profiles, improving brand pages, and publishing clear correction content tied to the entity.

Organic traffic is under pressure, not in freefall

Half of the marketers surveyed reported organic traffic declines since the launch of AI Overviews, and 61% blame AI. That is meaningful, but it is not the whole story.

The larger shift is not simply from Google to ChatGPT. It is from search as a destination to search as a behavior. People are asking, comparing, validating, and deciding across platforms, communities, assistants, and review environments.

The same marketers reporting organic losses are often finding visibility elsewhere. Fifty-seven percent report growth from social platforms such as TikTok, Reddit, and YouTube. Forty percent see growth from AI assistants such as ChatGPT, Gemini, and Perplexity. Thirty-one percent see growth in direct or branded traffic, while only 10% report no visibility growth anywhere.

That is why I think 2026 brand visibility depends on brand mentions and entity authority across the web, not just individual page rankings in Google.

Marketers are prioritizing the easiest tactics

Many teams are moving in the right general direction: community building, earned authority, owned audiences, expert content, and traffic diversification. The most prioritized strategies include building brand presence on social platforms at 59%, GEO and AEO optimization at 54%, and creating authoritative expert content at 44%.

Half of surveyed marketers say organic traffic has fallen since AI Overviews arrived, but the data points to pressure rather than collapse, with 30% reporting no change.

But the least prioritized strategy is original research and data, at only 15%. I see that as a strategic inversion.

Original, proprietary research is one of the hardest content assets for AI to replicate or commoditize. It earns citations, attracts links, builds topical authority, and gives journalists, communities, search engines, and AI systems something distinctive to reference.

In GEO, the same pattern appears. Many marketers are using content-led tactics that AI can easily replicate. Long-tail FAQs can help with AI Overviews, and schema can support structure, but neither one builds credibility by itself.

As organic search pressure grows, marketers are finding brand visibility gains across social platforms, AI assistants, direct traffic and Google AI features, according to Fractl and Search Engine Land.

The stronger moat is entity authority: proprietary data, expert perspectives, topical depth, and third-party validation. These are the assets that make a brand worth citing.

GEO measurement is lagging behind execution

Only a little more than half of marketers are confident in their GEO strategy, and only 12% have measurable results. That is understandable for a newer channel, but GEO is becoming too important to manage casually.

Marketers are leaning into practical GEO tactics, with FAQ optimization leading the pack, while entity authority, original research and citations trail behind.

I believe visibility tracking, citation monitoring, branded search lift, and AI-assisted conversion analysis all need more attention. Teams that can prove GEO ROI will be able to defend and grow investment while others are still guessing.

The main barrier to deeper AI integration is not leadership buy-in. Only 2% cite that as the obstacle. The top barrier is team training and skill gaps at 26%, followed by tool fragmentation at 20%, budget constraints at 19%, unclear ROI at 12%, and legal or compliance concerns at 12%.

For search teams, that means AI literacy, prompt strategy, content quality control, and GEO measurement skills may be more valuable right now than adding another tool to the stack.

Most marketers see early signs their GEO strategy is working, but only 12% report measurable results, highlighting a major gap in AI search measurement.

What I would do for a 2026 search strategy

First, I would audit the brand’s AI footprint. I would query the brand name across ChatGPT, Gemini, Perplexity, and Google AI Overviews, then document what is accurate, what is missing, and what is wrong. Waiting until an AI error becomes a PR issue is too late.

Second, I would invest in entity authority and original research. AI cannot invent legitimate proprietary survey data, named expert perspectives, verified brand facts, or original market analysis. Those assets become more valuable as AI systems get better at rewarding genuine authority.

Third, I would distribute visibility across multiple platforms. Google organic remains necessary, but it is no longer sufficient. A brand needs a consistent presence in Reddit discussions, YouTube content, AI assistant responses, review platforms, and earned media.

Fourth, I would build AI content governance, not just AI content workflows. Consumer demand for AI disclosure ranges from 84% to 91% across formats, while only 20% of brands always disclose. That gap is a reputational liability and may become a legal and regulatory one.

Fifth, I would close the GEO measurement gap. If I can connect AI search mentions to traffic, lead quality, and revenue, I can prove ROI at a time when most teams cannot. That creates a budget and strategy advantage that compounds.

Finally, I would double down on what AI cannot easily replicate: proprietary data, named experts, human-verified claims, transparent sourcing, and a consistent high-quality brand voice. In 2026, the brands that treat quality as a strategic differentiator are the ones most likely to be surfaced, cited, and trusted.

Methodology

Fractl and Search Engine Land surveyed 1,008 U.S. consumers and 150 marketers in Q2 2026. The consumer sample was nationally representative across age, gender, and region. The marketer sample included companies ranging from fewer than 10 employees to more than 5,000 and covered roles in SEO, content, social, analytics, paid media, PR, and marketing leadership.

Where noted, findings are compared year over year against the same questions asked in Fractl’s 2025 consumer study conducted with Search Engine Land.

Inspired by this post on Search Engine Land.

July 3, 2026
Why I’m Watching the Profound Index for AI Visibility

I’m introducing the Profound Index as a new way to understand AI visibility. It is the first leaderboard built to rank brands by how often they appear in answers from leading AI models.

For me, this matters because visibility is shifting beyond traditional search results. As more people rely on AI-generated answers, I want a clearer way to see which brands are being mentioned, recommended, and surfaced across the AI platforms shaping discovery.

Inspired by this post on Try Profound Blog.

July 3, 2026