Category: AI SEO

How to Read 2026 Search and Digital Agency Rankings

The leading 2026 agency rankings do not measure a single, universal version of marketing excellence. The supplied studies examine four different markets – legal agentic search, B2B digital marketing, agentic SEO, and luxury search – using different weights, candidate pools, and definitions of success.

Read together, they reveal more than a sequence of winners. They show which agencies recur across categories, where specialists displace generalists, and why buyers should examine the scoring model before treating any position as a dependable shortlist.

Key takeaways

First Page Sage placed first in all four supplied rankings, with its integrated SEO, GEO, content, and agentic-search approach cited repeatedly.
The runner-up changed with the market: Genevate rose in agentic and legal search, Driven Metrics performed well in B2B and performance-oriented categories, and Amsive ranked second for luxury brands.
Different weighting systems materially affect the results. Luxury experience carried the most weight in the luxury study, while AI visibility led the agentic SEO methodology.
A recurring appearance is a useful signal of breadth, but a category specialist may still be the stronger choice when industry knowledge, technical scale, creative positioning, or budget is decisive.
Because the publisher’s namesake agency ranked itself first in every supplied article, the results should be treated as publisher-reported evaluations rather than independent certifications.

Four rankings built to answer different questions

The studies used broadly similar ingredients, including expertise, client history, leadership, reviews, and AI visibility. The proportions assigned to those ingredients were not consistent, however. Even the size and timing of the reviewed fields differed.

Ranking lens	Reported review scope	Most influential criteria	Reported top three
Legal ASO	31 agencies reviewed over three months ending in June 2026	Average reviews, 25%; ASO expertise, 20%; leadership experience, 20%	First Page Sage, Genevate, Driven Metrics
B2B digital marketing	More than 80 agencies analyzed	SEO/GEO expertise, 30%; notable clients, 25%; leadership experience, 20%	First Page Sage, Driven Metrics, Focus Digital
Agentic SEO	38 firms evaluated in the second quarter of 2026	AI visibility, 30%; SEO, GEO, and ASO expertise, 25%; notable clients, 20%	First Page Sage, Genevate, Driven Metrics
Luxury SEO	More than 90 agencies reviewed from January through June 2026	Notable luxury clients, 35%; GEO/SEO expertise, 25%; AI visibility and leadership, 15% each	First Page Sage, Amsive, Relevance Digital

Those methodological differences explain why the tables should not be merged into a simple overall league table. A luxury agency can gain substantial ground through category-specific clients, while an agentic SEO contender receives more credit for appearing in AI citations. The legal study also introduces factors not used in the other rankings, including year established and estimated media references.

The numerical scores are not necessarily interchangeable either. Genevate received a 4.6 average review score in the legal ranking and 4.8 in the agentic SEO ranking. Focus Digital received 4.7 in the legal study and 4.8 in the B2B article. The sources do not provide enough underlying review data to determine whether those differences came from timing, platform coverage, normalization, or another methodological choice.

Where the rankings converge – and where they do not

First Page Sage is the clearest point of convergence. It placed first in every supplied study and received a 5.0 expertise score under each category’s relevant formulation: legal ASO expertise, B2B SEO/GEO expertise, agentic SEO-GEO-ASO expertise, and luxury GEO/SEO expertise. The three rankings that scored AI visibility gave it 4.9, while all four reported leadership at 4.8 and average reviews at 4.9.

The articles consistently attributed that performance to an approach combining long-form thought leadership, traditional organic search, generative-engine visibility, and signals intended to influence AI recommendations. The legal article placed additional emphasis on an AI belief audit and optimization across stages of an agent’s selection process. The B2B and luxury articles focused more heavily on content that can serve both conventional search results and AI-generated answers.

That consistency is noteworthy within the publisher’s framework, but it is not independent corroboration. All four supplied articles appear on the First Page Sage Blog, and each places First Page Sage at the top. Buyers should therefore verify the methodology, supporting case data, and fit through their own diligence.

Recurring agency	Positions in the supplied rankings	Cross-list signal	Source-reported caveats
First Page Sage	First in legal, B2B, agentic SEO, and luxury	Integrated SEO, GEO, ASO, and thought-leadership model	The legal review summary said the investment may require patience; the rankings are published by its namesake blog
Driven Metrics	Third in legal, second in B2B, third in agentic SEO	Performance measurement, conversion tracking, and an SMB or mid-market orientation	The sources described a shorter operating history, a data-intensive process, and more limited experience in some sectors
Genevate	Second in legal and second in agentic SEO	GEO-first work involving AI audits, reputation signals, and digital PR	Founded in 2025, with boutique capacity and a narrower service mix than a full-service agency
Focus Digital	Fourth in legal and third in B2B	More accessible SEO and GEO support with technical attention to LLM citations	The legal article described a more templated model; the B2B article noted narrower portfolio depth and slower replies during busy periods

An absence from one of the shortlists should not be read as a failing grade. Each article published only five, six, or eight finalists, and the sources do not disclose enough common data to determine how an unlisted agency performed outside its relevant category.

Specialization changes the meaning of a strong agency

A broad branching structure and three precision instruments represent generalist and specialist agency capabilities.

Agentic-search specialists

The legal and agentic studies favored firms with explicitly defined AI-search services. Genevate’s high positions were tied to audits of how AI systems describe a brand, external authority signals, and PR-led narrative work. Driven Metrics appeared across both of those lists as well as B2B, but the articles framed it as a more measurement-oriented option with a practical SEO and GEO foundation.

The distinction matters because the sources use ASO to mean Agentic Search Optimization, not simply visibility in a generated answer. Their framing extends the objective from being retrieved or cited to being evaluated, recommended, and potentially selected by an AI agent.

Enterprise and integrated operators

Large organizations may value capabilities that do not dominate an AI-specialist scorecard. The agentic SEO article ranked Seer Interactive fourth and emphasized its enterprise analytics, large-site architecture experience, technical implementation at scale, and published AI-search experiments. The luxury article placed Amsive second on the strength of enterprise SEO and an intentionally developed LLM-optimization practice, while also noting its narrower luxury portfolio.

The B2B list introduced another kind of breadth. REQ was positioned as an integrated communications, authority-building, and demand-generation partner whose GEO practice was less mature than its wider SEO foundation. AMP Agency and Viral Nation appeared farther down that ranking for broader media, creative, and influencer capabilities rather than category-leading search specialization.

Vertical and brand specialists

The luxury table demonstrates why domain fit can reorder a shortlist. Relevance Digital ranked third because of its exclusive focus on ultra-luxury brands and ultra-high-net-worth audiences, despite lower GEO and AI-visibility scores than the two agencies above it. Hudson Rouge ranked fourth as a creative and storytelling specialist, while Amra & Elma ranked fifth with luxury social-media and influencer experience but a developing GEO offering.

Legal marketing creates a different fit test. The legal ranking gave credit for recognized law-firm clients, legal-sector leadership, operating history, and media references in addition to AI-search capability. Consultwebs, 9Sail, and Legal Guardian Digital consequently appeared in that top eight even though they were absent from the broader B2B and agentic shortlists supplied here.

How buyers can turn rankings into a defensible shortlist

Two marketing buyers filter a large group of agency portfolio tiles into a small illuminated shortlist.

Start with the commercial outcome

A buyer should first decide whether the priority is organic traffic, AI citations, inclusion in recommendations, qualified pipeline, signed cases, brand prestige, or a combination. The correct weighting follows from that decision. For example, the legal article credited Driven Metrics with connecting AI-platform selections to consultations and signed cases, while the B2B article emphasized weekly synchronization and reporting tied to leads. Those claims are more relevant to a performance-led brief than a ranking based primarily on creative reputation.

Rebuild the scorecard for the actual market

The published weights can serve as templates, but buyers need not inherit them. A technically complex enterprise site may assign more importance to architecture, analytics, and implementation capacity. A law firm may emphasize jurisdictional accuracy and intake outcomes. A luxury brand may prioritize category experience and preservation of brand positioning. Recalculating the criteria can change the order without disputing any source’s reported scores.

Request evidence behind AI-visibility claims

An AI visibility score is meaningful only when its measurement process is clear. Diligence should establish which platforms were tested, what prompts were used, whether queries were branded or non-branded, how citations and recommendations were distinguished, and how frequently the test set was repeated. Buyers should also ask whether reported gains corresponded with qualified visits, leads, revenue, or another business outcome.

Test operational fit before accepting numerical fit

The source-reported caveats are as useful as the positions. Boutique capacity, slower responses during busy periods, extensive client-input requirements, limited sector history, and diluted senior attention can each affect a campaign. Reference calls and a clearly scoped pilot can help determine whether the people, workflow, and measurement discipline behind a score are suitable for the buyer’s organization.

As conventional SEO, generative discovery, and agent-led selection become more interconnected, useful agency comparisons will need to measure both visibility and business consequence. The strongest future scorecards will make their evidence reproducible and show not only where a brand appeared, but what happened after it was found.

References

July 3, 2026

Why I Judge AI Deliverables by Outcomes, Not Effort

When I think about AI deliverables, I keep coming back to a simple scenario: a client receives two pieces of work.

Both deliverables solve the problem they were hired to solve. Both are accurate, useful, and tied to the same business outcome. The client is happy, and from the outside, there is no meaningful difference in the results.

Then the client learns that one took 20 hours to create, while the other took 20 minutes. That is when the uncomfortable questions begin.

Was AI involved? Should the faster deliverable cost less? Is the person who completed it less skilled because they found a faster, more efficient way to reach the same result?

What I find most interesting is how differently many of us react to AI depending on which side of the transaction we are on. I love using AI when it saves me time, but I also understand why customers can feel uneasy when they discover AI helped create something they paid for.

I recently ran a LinkedIn poll asking a simple question: if the outcome is great, do we really care how it was made?

The responses reinforced something I have been thinking about for a while. Many of the strongest objections people have to AI are not really about quality at all.

The Time vs. Value Fallacy

I think part of the discomfort comes from the fact that we have spent decades tying value to effort.

Long hours feel valuable. Fast work feels suspicious. Struggle often gets mistaken for expertise.

The harder something appears to be, the easier it becomes to justify the price attached to it.

There is an old story about a ship engine that stopped working. After multiple failed attempts to repair it, the owners brought in an engineer with decades of experience. He inspected the engine, tapped it once with a small hammer, and the machine roared back to life.

His invoice was $10,000.

The owners were furious and demanded an itemized bill. The response was simple: hammer tap, $2. Knowing where to tap, $9,998.

People debate whether that story is true or just a useful tale for people like me who believe in value-based pricing. But whether it really happened almost does not matter. The lesson still holds.

People are not paying for the tap. They are paying for the expertise behind it.

That is what makes AI such an important topic for me. It forces us to confront a question many of us have avoided for years: are we paying for expertise, or are we paying for visible effort?

Those are not always the same thing.

The Objections That Actually Matter

To be clear, I do not think every objection to AI is unreasonable. I have shared plenty of my own concerns, and some of them are serious.

In fact, I think the strongest arguments against AI have very little to do with how quickly something was created.

Risk matters. Hallucinations matter. Bad recommendations matter. Compliance, privacy, and security concerns matter. Accountability matters.

Those are legitimate concerns. What stands out to me is that none of them has much to do with how long it took to create the deliverable.

They are questions of trust.

Can the output be trusted? Can the recommendation be defended? Can someone confidently stand behind the work if it is questioned six months from now?

Because when something goes wrong, nobody gets to blame the AI. The employee is accountable. The consultant is accountable. The company is accountable.

That is why I have always found the quality debate to be the least interesting part of the conversation. The more important question is not whether AI was involved. It is whether the outcome is trustworthy enough for someone to put their name behind it.

The Outcome Test

The more I think about AI, the less interested I become in whether it was used.

Instead, I find myself asking a different set of questions. Was the outcome accurate? Was it useful? Was it better than the alternative? Would I be willing to stand behind it with my name, reputation, and credentials on the line?

If the answer to all of those questions is yes, then I have a hard time arguing that the production method matters more than the result.

I suspect this is where many people become uncomfortable because it shifts the conversation away from tools and back toward results.

Ironically, this is also where humans become more important, not less.

The future is not machines versus humans. I know, "The Terminator" and "I, Robot" movies will never feel the same. The real shift is humans using AI versus humans who refuse to adapt.

The premium will not come from avoiding AI. It will come from judgment, taste, decision-making, communication, and accountability.

AI can accelerate execution, but people still decide what should be built, what should be published, and what risks are acceptable. More importantly, people are still responsible for the outcome.

The people who lose to AI will not be the ones using it. They will be the ones still evaluating effort while everyone else is measuring outcomes.

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

Inspired by this post on Search Engine Land.

July 3, 2026
How Brands Earn Authority and Citations in AI Search
AI search visibility is not a single contest for a single ranking. A brand can supply a fact without receiving credit, earn a citation without being recommended, or appear in an answer without generating a visit. The practical challenge is to build authority that survives across those different outcomes.

The source research points to a connected strategy: follow shifting demand, create evidence that cannot be easily replicated, make that evidence easy to extract and attribute, clarify the entities behind it, and measure what people do after an AI mention.

AI visibility is a funnel, not a ranking

Three outcomes are often grouped under “AI visibility,” although they answer different business questions. A citation identifies a page or domain as a source. A brand mention places the company or product inside the generated answer, with or without a link. Downstream behavior covers what happens next, including branded searches, site visits, browsing, and engagement.

Try Profound’s discussion of the “AI mention effect” concentrates on that third layer. Its premise is that visibility inside an AI response should be connected to subsequent user behavior rather than treated as an endpoint. This matters because an AI-generated recommendation can influence a decision even when the user does not click immediately or when the cited source and recommended brand are different entities.

The appropriate success measure therefore depends on the query. For an informational question that an assistant can answer completely, inclusion and attribution may represent most of the available opportunity. For a product or service comparison, a mention can create a new search for the brand, its pricing, reviews, documentation, or product pages. Search Engine Land’s analysis of more than 1 million keywords similarly argued that SaaS and lifestyle queries can retain a downstream search step, while some HealthTech and FinTech questions can end inside the AI interface.

A useful measurement model keeps these stages separate: presence in the answer, citation ownership, the way the brand is represented, and observable activity after exposure. Combining them into one visibility score can conceal an important failure, such as frequently supplying information while another publisher receives the citation.

Search demand is moving unevenly across queries and categories

The broad narrative that AI is simply eliminating search is not supported by the keyword analysis supplied here. Search Engine Land reported that a study of 1,010,848 high-volume keywords across 379 brands and eight verticals found 29% of search volume in measurable decline. Yet the declining keyword set represented about 10.29 billion monthly searches, while growing keywords represented about 10.31 billion. Across a dataset covering 35.4 billion monthly searches, the reported net change was an increase of 16.8 million searches per month.

Those aggregate figures mask substantial differences. The same analysis reported a 37.7% decline for FinTech and a 15.2% decline for Lifestyle. It also found that 90% of tracked search volume was non-branded, including 99.6% in HealthTech and 98.5% in Wellness. Non-branded informational demand is especially exposed because an assistant can often complete the exchange without sending the user to a separate website.

Consumer behavior in the study also looked additive rather than purely substitutive: 70% of surveyed consumers said they were using AI more, but only 17% said they were using traditional search less. The reported survey covered 1,004 U.S. consumers, so it should be read as evidence from that sample rather than a universal forecast.

The strategic implication is not to abandon conventional SEO or apply one forecast to every market. Brands need to distinguish declining generic questions from growing discovery paths and from branded queries that may occur after an AI recommendation. In information-heavy categories, authority inside the answer becomes more important. In categories with a natural evaluation or transaction step, AI mentions, organic rankings, reviews, and branded search can reinforce one another.

Citation selection changes with reasoning depth and buyer intent

A brand’s presence in one AI answer does not establish durable authority. Search Engine Land reported that a Semrush and Kevin Indig test produced only 25.6% overlap between domains cited by ChatGPT in minimal- and high-reasoning modes for the same prompts. The study used 100 prompts across 20 buyer journeys in B2B SaaS, finance, consumer technology, and health and lifestyle, with each prompt run once in each mode.

High reasoning searched more widely in that experiment. It conducted 1,130 web searches compared with 245 in minimal reasoning, while the share of responses containing citations increased from 50% to 68%. Cited responses used an average of 4.5 citations in high reasoning and 2.6 in minimal reasoning. These figures come from a bounded test rather than a complete description of ChatGPT, but they illustrate how a change in answer process can rearrange the source set.

The source mix also changed. Reddit’s reported citation share fell from 15% to 7%, and user-generated content and review sites declined from 14.3% to 6%. Government and academic sources rose from 1.9% to 8.8%, while official documentation and support pages increased from 12.4% to 17.5%. The result does not make community content irrelevant; it suggests that deeper reasoning may place more weight on sources capable of verifying detailed claims.

Comparison prompts created the widest retrieval task. High reasoning averaged 24 subqueries and 9.8 citations at that stage, versus 5.5 subqueries and 5.8 citations in minimal reasoning. A single buying question can therefore break into searches for pricing, integrations, security, support, specifications, and documentation. A polished landing page alone is unlikely to answer every part of that research path.

Authority should consequently be tested across both reasoning depth and buyer intent. The same study found four of 20 high-reasoning journeys in which a brand cited at the problem stage remained visible through selection; minimal reasoning produced no such full-journey persistence. Although the sample is small, the result frames continuity as a more demanding benchmark than winning an isolated prompt.

An authority system needs evidence, extraction, identity, and corroboration

Publish evidence the brand is qualified to originate

First-party product, usage, pricing, or customer data can give a page information that generic commentary cannot reproduce. Search Engine Land cited an On-Page.ai study of 150 top-three Google pages across 50 keywords and 10 verticals. Pages with no more than one unique figure averaged an information-gain score of 40.2, while pages with at least 15 unique figures averaged 62.1. The study concerned conventional organic results rather than AI citations, so it supports an originality argument without proving that proprietary data automatically earns AI attribution.

An executive perspective in First Page Sage’s interview with Thesis founder Dan Freed reaches a compatible conclusion from a different angle. Freed argued that authority depends on checkable substance such as named mechanisms, specific ingredient forms, studies, and customer data. That is a founder’s stated philosophy rather than independent validation of the products discussed, but it illustrates what defensible specificity looks like in a category filled with broad claims.

Make important claims easy to extract

Original ownership does not guarantee citation ownership. An aggregator can restate a benchmark more clearly and become the source an AI system selects. In a separate analysis of 18,012 verified ChatGPT citations, Search Engine Land reported that 44.2% came from the first 30% of a page. The 10% to 20% band attracted the most citations across seven verticals, while the final 10% accounted for only 2.4% to 4.4%.

Those findings favor an answer-ready research structure: surface the principal result early, define the metric beside it, state the population and comparison, and provide a compact methodology. The percentages should not be treated as a universal page-design formula, but the broader lesson is robust: a buried or undefined number is harder to retrieve and attribute confidently.

Clarify the entities and relationships behind each claim

The GraphRAG account adds an identity layer to the content problem. As described by Search Engine Land, GraphRAG supplements text retrieval with a knowledge graph whose nodes represent entities and whose edges represent relationships such as a company offering a product, holding a certification, or operating in a region. Entity resolution can consolidate alternate names instead of scattering signals across several apparent identities.

This helps explain why strong prose may still be passed over for a complex question. A retrieval system needs to determine not only that several facts are relevant, but that they apply to the same company, product, person, place, and time. Consistent naming, explicit authorship, clear product-company relationships, qualified claims, and supporting documentation reduce the amount of inference required. The GraphRAG article characterizes this as a response to disambiguation, attribution, and relationship problems, not merely a call to produce more content.

Build corroboration beyond the original page

A primary source still benefits when reputable third parties discuss its research accurately, even if one of those publishers occasionally receives the direct citation. External coverage can reinforce the association between the brand, its evidence, and the topic. Official documentation supports verification; independent reporting supplies corroboration; and community discussion can reveal real-world experience. The reasoning-mode study indicates that their relative weight may change by prompt, category, and answer process.

Measurement should follow the same layered design. Prompt tracking can show whether a brand is mentioned, cited, represented correctly, and carried across buyer-journey stages. Web analytics and search data can then test for visits, branded demand, and engagement after exposure. No single metric establishes causation on its own, but the combined evidence is more useful than treating citation count as the final business outcome.

Key takeaways
- Separate citations, brand mentions, representation, and downstream behavior; each measures a different part of AI visibility.
- Audit demand by query type and vertical because AI exposure is much greater for some informational and non-branded searches than for transactional paths.
- Test visibility across reasoning modes and buyer stages instead of assuming that one successful prompt represents durable authority.
- Publish defensible first-party evidence, then surface its result, definition, scope, and methodology where retrieval systems can find them.
- Use consistent entities, explicit relationships, official documentation, and credible external corroboration to make claims easier to verify and attribute.
The next advantage in AI search will come less from chasing a fixed citation formula than from building a body of evidence that remains identifiable, retrievable, and credible as interfaces and retrieval methods change.

References
July 3, 2026

How to Measure AI Search Visibility, Citations and Impact

AI search visibility is no longer a single ranking question. A brand can appear in an answer, earn a citation, receive a visit, influence a later conversion or remain invisible to conventional attribution at each stage.

The practical response is to connect content optimization, citation monitoring and business measurement. The sources collectively show why those disciplines must operate as one system, even though no single metric can yet describe the entire AI-assisted customer journey.

Key takeaways

AI visibility begins with content that can be discovered for a broad topic, understood in context and extracted into an answer.
A citation is evidence of selection, not proof that a user visited or converted.
Referral traffic captures only journeys that include a trackable click; direct visits, calls and delayed conversions can obscure AI influence.
Measurement should progress from answer presence to citations, referrals, conversions and lead quality.
Global standards should govern technical implementation and reporting, while market experts supply differentiated local knowledge.

Visibility depends on retrieval, selection and presentation

Traditional rank tracking starts with a query and a results position. AI-generated answers add intermediate decisions: the system may decompose a request into related subqueries, retrieve supporting pages, synthesize their information and choose which sources to display. Visibility can therefore be gained or lost before a citation is ever shown.

A Search Engine Land article about Google query expansion distinguishes traditional query expansion from AI Mode query fan-outs. In its account, expansion connects searches through synonyms, intent and related topics, while fan-outs generate multiple subqueries during answer construction. The article recommends using Google Search Console impressions and unexpected but relevant queries as signals for strengthening topic coverage, rather than as an invitation to add disconnected keywords.

That retrieval perspective complements HiGoodie’s travel optimization guidance, which emphasizes direct answers, FAQs, schema markup, topical authority and content based on real traveler questions. That source reports that 40% of travelers use AI to research, compare and organize travel decisions. The percentage should be treated as reported by the article, but its strategic implication is clear: content must supply both a concise answer and enough surrounding context to be interpreted correctly.

Selection does not guarantee equal exposure. Search Engine Land’s report on recipe links in Google AI Mode describes a visual treatment that can place creator names, images, ratings and ingredient counts near prominent links. It also notes that Google had been testing a top-stories carousel in AI Overviews but that the feature did not appear to be live at the time reported. These examples make presentation a separate measurement dimension: two cited publishers may receive materially different opportunities to be recognized or clicked.

A citation is not the same as a visit or a customer

A glowing source card begins a branching path of stepping stones that ends with two hands exchanging a parcel.

The recipe treatment illustrates the distinction between attribution and distribution. More recognizable links may improve the path to a publisher, but the report leaves open whether they will generate enough meaningful traffic. Citation counts alone cannot resolve that question because a source can inform an answer without producing a click.

The opposite measurement problem also occurs: AI may influence a customer without producing a visible referral. A Search Engine Land article based on an analysis of nearly 30 million inbound leads reports that AI-attributed leads remained a small share of total volume but were growing and appeared across multiple industries. It also describes customers who encounter a recommendation in an AI service and later call a business, creating journeys that may be classified as direct or remain unattributed.

The same source is explicit about the dataset’s limits: it could identify cases in which customers named an AI platform as part of the route to contacting a business, but it could not reveal their prompts, platform choices or the reasons a particular company was recommended. That is evidence of association within a reported journey, not a complete causal explanation.

Organizational interest is also moving toward this broader view. Profound’s recap of Zero Click New York 2026 says that more than 1,000 marketing leaders gathered on June 11, 2026, and that sessions addressed Claude’s citation mechanics, ChatGPT’s emerging advertising business and content signals associated with AI trust. An event recap is not outcome data, but the subjects it highlights show citations, distribution and measurement being treated as connected management questions.

Use a measurement ladder instead of one AI metric

Analysts examine ascending translucent platforms marked by symbols for visibility, sources, visits, journeys and value.

A workable reporting model separates observable stages rather than combining them into a proprietary visibility score. Each stage answers a different question and carries a different evidentiary limit.

Measurement layer	Question it answers	Useful evidence	Main limitation
Answer presence	Does the brand or page appear for relevant prompts?	Repeatable prompt checks across selected platforms, markets and use cases	Outputs can vary, so a single observation is not a stable benchmark
Citation visibility	Which pages are named or linked as sources?	Citation frequency, cited URLs, placement and visible source treatment	A citation does not establish attention, a click or preference
Referral activity	Did a user arrive through a trackable AI link?	Analytics referrals, landing pages and tagged campaign links where available	Non-click journeys and incomplete referrer data remain unseen
Conversion influence	Did AI discovery contribute to an inquiry or sale?	Lead-source questions, call attribution and customer-reported discovery paths	Self-reporting and multi-touch journeys complicate causal claims
Business quality	Are AI-influenced customers valuable?	Qualified leads, completed transactions and downstream customer outcomes	Low volume can make comparisons unstable

These layers should be reported separately before they are interpreted together. For example, rising citation visibility with flat referral traffic could indicate a zero-click exposure pattern, weak source presentation or a mismatch between cited content and user intent. Rising customer-reported AI discovery without comparable referrals would instead point to an attribution gap. Both observations warrant investigation, but neither proves its suspected explanation by itself.

Content research can connect the upper and lower portions of the ladder. Search Console queries can reveal adjacent questions already associated with a page, while citation observations show whether AI systems select that page for related answers. Referral and lead data then indicate whether any of that exposure reaches the business. Optimization becomes a testable cycle when the baseline, content change and subsequent observations are recorded consistently.

Govern shared infrastructure while localizing expertise

Measurement becomes harder when teams use conflicting entity definitions, technical rules or reporting methods. The problem is especially acute for multinational organizations because an AI system can synthesize material across markets rather than respecting the operational boundaries used inside the company.

A Search Engine Land analysis of global SEO ownership argues that hreflang, localization and technical SEO remain necessary, but that hreflang handles routing rather than deciding which market perspective an AI answer should prioritize. It recommends central governance for areas in which inconsistency creates enterprise-wide risk, including CMS rules, structured data, entity definitions, AI crawler policies, measurement frameworks and technical infrastructure.

The same analysis places audience research, regulatory information, local authority building and market expertise closer to in-market teams. Its central tension is not simply standardization versus translation. Multiple near-identical market pages may provide less differentiated evidence than content grounded in local terminology, regulations, customer expectations and industry practices.

That division of responsibility also applies outside international SEO. A central team can define how citations, referrals and AI-influenced leads are recorded, while subject specialists validate the underlying claims and answer the questions their audiences actually ask. The travel guidance’s focus on traveler intent and the query-expansion article’s focus on adjacent questions both support this combination of shared structure and domain-specific knowledge.

The next useful advance will come from disciplined linkage: connecting the content changes made, the answers and citations observed, and the customer outcomes recorded without overstating what any one dataset proves. Organizations that establish that evidence chain can adapt as interfaces and citation treatments change, while keeping investment decisions tied to measurable audience and business value.

References

July 3, 2026

Top Agentic Search Agencies of 2026: My Ranked Picks

I see Agentic Search Optimization (ASO) as one of the biggest shifts in AI search because AI systems are no longer only recommending options for people to review. They can now complete the action themselves. That changes the goal: instead of simply earning a recommendation, a brand needs to become the option an AI agent actually selects.

That is where ASO differs from GEO, or Generative Engine Optimization. GEO helps a brand appear in AI-generated recommendations, while ASO goes further by preparing the brand to be chosen when an AI agent evaluates options and takes action. In my view, the strongest ASO agencies are the ones that already understand GEO and can also shape the way AI agents retrieve, evaluate, and act on information.

During Q2 2026, I reviewed a dataset of 38 U.S. agencies offering ASO and GEO services. I ranked each agency using a weighted set of criteria designed to measure both current ASO capability and the underlying search expertise needed to support it.

ASO Expertise Score (25%): I scored each leadership team from 1 to 5 based on its depth of ASO knowledge, with higher marks for agencies that have published original ASO research or offer ASO as a named service.
Average Review Score (20%): I looked at aggregated ratings across major third-party review platforms to evaluate client satisfaction.
Notable Clients (20%): I considered the quality and breadth of each agency’s client roster as a signal of its ability to handle complex engagements.
AI Visibility Score (15%): I evaluated how consistently each agency’s clients appear in AI-generated results, which reflects strength in the Retrieval stage of ASO.
Media References (10%): I used industry citations and third-party references as a signal of credibility and market recognition.
Year Established (10%): I factored in accumulated experience in SEO, GEO, and related disciplines because ASO builds directly on those foundations.

Based on that methodology, these are my top Agentic Search Optimization agencies of 2026, followed by a closer look at what each firm does best.

The Top Agentic Search Optimization (ASO) Agencies of 2026

Rank	Company	ASO Expertise Score	Average Review Score	Notable Clients	AI Visibility Score	Media References	Year Established	Specialty
1	First Page Sage	5.0	4.9	Salesforce, Logitech, Verizon, Dignity Health	4.9	~840	2009	ASO, GEO, and SEO for lead generation
2	Genevate	4.5	4.8	ZipRecruiter, CBRE, Talentfoot	4.6	~35	2024	ASO/GEO with PR and reputation management
3	Siana Marketing	4.2	4.7	BSA Design, Corcoran, HomeVestors	4.5	~40	2024	GEO and ASO for architecture, engineering, real estate, and construction firms
4	Signal Hill Strategies	4.1	4.7	Keyhole Software, EU Naturals	4.5	~10	2026	SEO and GEO for B2B and B2C
5	Onely	3.7	4.9	eBay, IKEA, ServiceTitan	4.1	~150	2019	Technical SEO and AI search infrastructure
6	Media Cause	3.6	4.8	AKC, NRDC, Stand Up to Cancer	4.0	~200	2010	Full-service digital marketing for nonprofits
7	WebSpero	3.5	4.8	Ubie Health, Artsabers, K9 Academy	4.0	~50	2014	GEO for niche, smaller-market clients
8	Zozimus	3.6	4.4	Bay Path University, Procept BioRobotics, Scholarship America	3.9	~80	2004	GEO for higher education and healthcare brands

First Page Sage

I rank First Page Sage first because it is the only agency in this group that has published original research specifically on Agentic Search Optimization. Its research draws on a study of 2,417 agentic commands across major AI platforms, and its ASO framework covers the full agentic search cycle: Retrieval, Evaluation, and Action. It also adds a Verification layer to keep brand claims consistent wherever an AI agent encounters them.

What stands out to me is the agency’s AI Belief Landscape methodology. Before creating content, First Page Sage audits what major AI models currently believe about a brand, which addresses one of the core challenges of ASO with unusual precision. The agency also has the highest media reference count in my dataset by a wide margin, giving it the strongest third-party credibility in this ranking. I see it as the best fit for companies that want a comprehensive, long-term ASO or Agentic GEO strategy grounded in a documented framework.

ASO Expertise Score: 5.0
Average Review Score: 4.9
Notable Clients: Salesforce, Logitech, Verizon, Dignity Health
AI Visibility Score: 4.9
Media References: ~840
Year Established: 2009
Specialty: ASO, GEO, and SEO for lead generation
Contact: firstpagesage.com

Summary of Online Reviews

Clients describe “a team with outstanding insights into the full agentic search cycle,” praise “strategies that started generating results within the first quarter,” and highlight that “the quality of AI-driven buyers was unlike anything we’d seen before.”

Genevate

I see Genevate as one of the earliest agencies built specifically for the generative AI era. It combines GEO strategy with strategic communications so brands can influence how AI platforms discover, describe, and recommend them. Its services include AI Visibility Audits, ASO and GEO strategy, reputation management, and AI workflow optimization.

Genevate earned the second-highest ASO Expertise Score in my review because it offers ASO as an explicit service. Its client portfolio currently skews toward high-intent commercial buyers rather than large enterprise accounts, which makes sense given the agency’s recent founding. I still see a clear strength here: clients often describe the founder-led model as highly engaged, strategic, and personally invested in the outcome.

ASO Expertise Score: 4.5
Average Review Score: 4.8
Notable Clients: ZipRecruiter, CBRE, Talentfoot
AI Visibility Score: 4.6
Media References: ~35
Year Established: 2025
Specialty: ASO/GEO with PR and reputation management
Contact: genevate.co

Summary of Online Reviews

Genevate clients say “the team understood our goals,” credit the agency with “getting our brand into AI search recommendations,” and describe the content as “well-researched, although slightly dry.”

Siana Marketing

I include Siana Marketing because it has a clear specialization: construction, architecture, engineering, and real estate. Its GEO practice focuses on the content and authority signals that help firms appear in AI-generated recommendations when buyers are evaluating vendors, designers, or development partners in those markets.

Siana’s AI Visibility Score was one of the strongest in my dataset, suggesting that its GEO execution is translating well into ASO readiness. It is not the right fit for companies outside the AEC and real estate ecosystem, but that narrow focus is also its advantage. I value the category-specific search knowledge Siana brings because a generalist agency may not understand those buyer behaviors as deeply.

ASO Expertise Score: 4.2
Average Review Score: 4.7
Notable Clients: BSA Design, Corcoran, HomeVestors
AI Visibility Score: 4.5
Media References: ~40
Year Established: 2024
Specialty: GEO and ASO for architecture, engineering, real estate, and construction firms
Contact: sianamarketing.com

Summary of Online Reviews

Clients say the team produces “content that shows up in AI-generated vendor recommendations.” Others note that “their strategy can feel templated.”

Signal Hill Strategies

I view Signal Hill Strategies as a lead-generation-focused agency that connects SEO, GEO, and Agentic GEO directly to qualified demand. Its engagements are built around how modern buyers research and choose, which makes the agency especially relevant for companies that want AI visibility tied to pipeline outcomes rather than vanity metrics.

Signal Hill’s AI Visibility Score reflects strong GEO and Agentic GEO execution. Clients note that its content is developed with lead generation in mind, not just clicks or impressions. Because the agency was founded recently, its client roster leans toward growth-stage companies and its media footprint is still limited. Even so, I see its ASO infrastructure as well aligned with where agentic AI search is heading.

ASO Expertise Score: 4.1
Average Review Score: 4.7
Notable Clients: Keyhole Software, EU Naturals
AI Visibility Score: 4.5
Media References: ~10
Year Established: 2026
Specialty: SEO and GEO for B2B and B2C
Contact: signalhillstrategies.com

Summary of Online Reviews

Clients highlight that “the strategy was built around revenue goals,” credit the team’s “professionalism and communication,” and describe them as “focused on understanding our buyer.”

Onely

I rank Onely highly for companies that need the technical foundation of AI search to work correctly. Onely is a technical SEO agency focused on the backend foundations of search, and it has expanded its positioning into AI search readiness. Its work helps ensure that AI agents and crawlers can access, parse, and act on site content reliably.

Onely’s strength is also the reason it does not rank higher. Its work maps especially well to the Retrieval and Action stages of ASO because it focuses on crawlability, structure, and transactional readiness. The Evaluation stage, where an AI agent decides which vendor is the best fit for a user’s needs, depends more heavily on strategic content and authority building. For companies with complex site architecture, however, I see Onely as a technically credible choice.

ASO Expertise Score: 3.7
Average Review Score: 4.9
Notable Clients: eBay, IKEA, ServiceTitan
AI Visibility Score: 4.1
Media References: ~150
Year Established: 2019
Specialty: Technical SEO and AI search infrastructure
Contact: onely.com

Summary of Online Reviews

Clients credit Onely with “diagnosing technical crawl and indexing issues,” noting “improvements in organic traffic and site health.” Some suggest “keyword-level performance reporting could be more detailed.”

Media Cause

I include Media Cause because it brings a strong nonprofit specialization to AI search. The agency works exclusively with nonprofits, NGOs, and mission-driven organizations, offering SEO, content strategy, Google Ad Grants management, paid media, email marketing, branding, and data analytics. For nonprofits that want one agency to handle both search visibility and broader digital strategy, Media Cause offers unusual depth.

Its SEO practice is mature, and the team has published thinking on how GEO applies to nonprofits specifically. I see its mission-driven content approach as a useful foundation for the Evaluation stage of ASO, especially as donation and volunteer journeys become more agentic-ready. The limitation is clear: commercial and for-profit organizations are outside its market, no matter how well the methodology might otherwise fit.

ASO Expertise Score: 3.6
Average Review Score: 4.8
Notable Clients: AKC, NRDC, Stand Up to Cancer
AI Visibility Score: 4.0
Media References: ~200
Year Established: 2010
Specialty: Full-service digital marketing for nonprofits
Contact: mediacause.com

Summary of Online Reviews

Clients praise “a team that genuinely cares about mission impact,” credit Media Cause with “strong SEO results,” and note that the agency “can be slow to implement content feedback.”

WebSpero

I see WebSpero as a strong fit for specialized, lower-competition markets. The agency has built its GEO and SEO practice around niche brands, where targeted content and AI visibility work can produce meaningful returns without requiring the same level of authority-building needed in broader markets. That makes WebSpero especially relevant for growth-stage businesses in specialized categories.

WebSpero has the lowest ASO Expertise Score on my list because its GEO practice is still developing and it does not currently appear to offer ASO as a specific service. Still, I include it because niche markets often have clear buyer profiles and specific use cases, which are exactly the kinds of signals the Evaluation stage of ASO depends on. Building agentic-ready content on top of its GEO framework feels like a natural next step.

ASO Expertise Score: 3.5
Average Review Score: 4.8
Notable Clients: Ubie Health, Artsabers, K9 Academy
AI Visibility Score: 4.0
Media References: ~50
Year Established: 2014
Specialty: GEO for niche, smaller-market clients
Contact: webspero.com

Summary of Online Reviews

Clients highlight “visibility gains where other agencies had struggled to move the needle,” praise “a responsive team,” and suggest that “a broader digital strategy will need to be handled in-house or elsewhere.”

Zozimus

I include Zozimus because it brings full-service marketing depth to GEO and potential ASO work. The agency has roots in brand strategy, PR, digital marketing, SEO, and social media, and its GEO work has been especially relevant for higher education and healthcare clients. Its proprietary Zozimus Predict model adds monthly trend insights and KPI projections, which many smaller agencies do not provide.

Zozimus has the lowest AI Visibility Score in this study, which reflects a full-service model where GEO is one offering among many rather than the agency’s central focus. Even so, I see a credible ASO foundation here. Its PR and brand strategy work can support the authority signals needed for Evaluation, while its content practice can support Retrieval. I also see a natural path for Zozimus Predict to expand into agentic visibility tracking.

ASO Expertise Score: 3.6
Average Review Score: 4.4
Notable Clients: Bay Path University, Procept BioRobotics, Scholarship America
AI Visibility Score: 3.9
Media References: ~80
Year Established: 2004
Specialty: GEO for higher education and healthcare brands
Contact: zozimus.com

Summary of Online Reviews

Clients praise the agency’s “ability to manage creative, PR, and digital work under one roof,” while noting that “individual channels can feel less specialized than a single-discipline agency.”

Source

Inspired by this post on First Page Sage Blog.

July 3, 2026

Modern SEO Workflows: From Dashboards to Small Tools

A modern SEO workflow has to do more than collect rankings and audit errors. It must distinguish visibility from traffic opportunity, focus limited time on pages that matter to the business, and turn recurring analysis into reliable automation.

The most useful operating model is therefore not a wholesale replacement of traditional SEO software. It is a layered system in which established data sources reveal the problem, people choose the intervention, and AI-assisted tools reduce the cost of repeating proven work.

The operating model matters more than the size of the stack

Rank trackers, keyword platforms and site crawlers remain useful because search engines still need to discover, interpret and evaluate pages. However, the reported case for a new SEO stack is that those tools describe only part of a more fragmented search environment. AI Overviews, local packs, shopping features and other result formats can change how much value a nominal ranking produces. Historical search volume can likewise remain stable while an answer displayed in the results reduces the traffic available to publishers.

That changes the role of measurement. A ranking is an observation, not an outcome. The workflow must connect traditional visibility, AI-search presence, landing-page behavior and conversion evidence before deciding what deserves attention. The same source reported that LLM referral traffic in its cited dataset grew by 80% between the first and second halves of 2025 and converted at 18%, while accounting for 2% or less of total traffic. Those figures were presented as evidence of a small but potentially meaningful channel, not as proof that conventional search had ceased to matter.

Workflow layer	Question it answers	Typical inputs	Required output
Observe	Where is visibility, demand or performance changing?	Search Console, analytics, rank tracking, crawls and AI-visibility observations	A short list of material signals
Decide	Which signal is worth acting on now?	Business value, intent, conversion proximity and implementation effort	One prioritized intervention
Ship	What can improve the page or remove the constraint?	Content edits, internal links, technical fixes and clearer conversion support	A completed change or actionable brief
Systematize	Which repeated work should become faster and more consistent?	APIs, scripts, notebooks and carefully supervised LLMs	A documented, testable process

This sequence prevents a common tooling mistake: automating a report before establishing which decision the report should support. It also preserves a place for human judgment between data collection and implementation.

A 120-minute loop can connect monitoring with delivery

A top-down desk scene shows four connected stages of an SEO workflow arranged in a circle around a strategist's hands.

The reported 120-minute workflow addresses a practical constraint: on a lean marketing team, SEO competes with campaigns, reporting, email, social publishing and website requests. Its strongest principle is that a weekly session should finish with work shipped, not merely with more metrics reviewed.

The first five time boxes below follow the source’s reported schedule. The final 20-minute block is a synthesis of the other sources’ automation guidance, turning the weekly session into a tool-development feedback loop.

Minutes 0-15: inspect Search Console and analytics for meaningful movement, including clicks, impressions, click-through rate, landing-page performance, conversions and critical indexing warnings. Record the largest win, concern and investigation target rather than building a presentation.
Minutes 15-35: identify a small number of query opportunities. The source recommends examining queries in positions 4-15 with meaningful impressions, pages with weak click-through rates and results where the ranking page only partly satisfies intent.
Minutes 35-60: improve one page close to revenue, such as a product, service, category, pricing, comparison or consultation page. The change might address an objection, clarify the audience, add proof, answer a relevant question or make the next action easier to understand.
Minutes 60-80: resolve one consequential technical or indexing problem. If a direct fix is not possible, produce an assigned issue or a developer brief with affected URLs and the expected behavior.
Minutes 80-100: strengthen internal links between useful informational pages and relevant commercial destinations, while also connecting supporting guides and newer strategic content.
Minutes 100-120: verify what changed, document the result and mark one repetitive task as a possible automation candidate. That candidate should enter a backlog rather than becoming an improvised build during the same session.

The value of this cadence is not the clock alone. It creates a recurring path from signal to decision to change. It also generates concrete automation ideas: a comparison performed every week, a recurring CSV cleanup, a repeated title check or a manual alert that depends on the same thresholds each time.

Small tools should begin with a bounded decision

The source on vibe coding describes a low-barrier pattern: specify a program in natural language, run the generated code in an environment such as Google Colab, inspect the output and return errors to the AI for another iteration. It distinguishes this from AI-assisted coding, where a developer remains more directly responsible for the system, and from no-code platforms, which expose automation through visual interfaces.

The distinction helps set an appropriate ceiling. Vibe coding is presented as suitable for prototypes, internal utilities, demonstrations and tasks where a useful result does not have to be perfect. Commercial software, sensitive systems and products requiring dependable maintenance call for stronger engineering, security and testing practices.

A reported SEO example makes the right project shape clear. After a site crawl produced vector embeddings, the author prompted an AI to create a Colab tool that would compare vectors with cosine similarity and suggest related pages within each locale. The program had an explicit input, a defined matching rule and a CSV output. It did not attempt to automate an entire SEO strategy.

Before generating code, a useful tool brief should define:

The decision or bottleneck the tool is meant to improve.
The exact input source, required columns and accepted file format.
The transformation or rule applied to the data.
The expected output format and who will use it.
A small set of known examples for checking correctness.
The behavior when data is absent, duplicated, malformed or unexpectedly large.
The APIs, credentials, usage charges and execution environment involved.

Tool choice can then follow complexity. An LLM may be enough to explore a one-off dataset or review copy. An API becomes useful when manual exports are the bottleneck. A lightweight script suits a stable transformation such as flagging performance changes or checking metadata. A notebook is appropriate when code, commentary and outputs need to remain together. A maintained application is warranted only when the process has durable users, permissions, interfaces and support requirements.

Validation is part of the workflow, not a final polish

A compact modular tool moves a web page tile through several visual validation checkpoints while rejected variants remain separated.

All three sources point toward speed, but they also expose different reasons to retain human control. The new-stack article recommends using LLMs for analysis, content review, competitor comparison, metadata and structured data while keeping editorial and strategic oversight. The weekly workflow keeps prioritization tied to commercial importance. The vibe-coding account shows why plausible-looking output cannot be accepted on appearance alone.

In one example from the vibe-coding source, an underspecified prompt failed to explain that the input would be a CSV. The generated tool responded with invented URLs, traffic figures and charts. The same source reports that generated code can depend on packages that are not installed, and that paid APIs may introduce authentication steps and usage costs. These are not edge concerns: they demonstrate that execution, factual grounding and operating cost must all be tested separately.

Ground the run: identify the authoritative input and reject synthetic substitutes unless test data is explicitly requested.
Test a sample: compare several outputs with results that can be checked manually, including an ordinary case and an edge case.
Inspect failure behavior: confirm that missing columns, empty files, invalid credentials and API errors produce understandable messages.
Protect access: keep credentials out of prompts, shared notebooks, exported files and source code intended for distribution.
Track cost: estimate which calls consume paid API units or usage-based platform resources before scheduling repeated runs.
Preserve review: require a person to approve consequential content changes, redirects, canonical decisions, schema deployment or other site-wide actions.
Document ownership: record the tool’s purpose, dependencies, expected inputs, validation method and person responsible for maintenance.

A prototype should be promoted into a recurring workflow only after it produces repeatable results on known data. If the logic affects many pages or a revenue-critical system, code review and stronger testing become proportionally more important.

Key takeaways

Keep traditional SEO data, but interpret rankings and search volume alongside result features, traffic opportunity and business outcomes.
Time-box reporting so that every weekly SEO session produces a shipped improvement, an assigned fix or a precise implementation brief.
Use recurring manual work to discover automation opportunities; do not begin with a tool and search for a problem afterward.
Give every small SEO utility explicit inputs, transformation rules, outputs, test cases and failure behavior.
Treat LLMs, APIs and scripts as accelerators within a reviewed process, not as substitutes for strategy, factual checks or technical ownership.

As search interfaces continue to diversify, the durable advantage will come from shortening the distance between a trustworthy signal and a verified improvement. Teams can build that capability incrementally, one weekly decision and one well-scoped tool at a time.

References

July 3, 2026

How I Turn AEO Data Into Action With Profound Projects

With Projects in Profound, I can turn my AEO data into a clear, ranked list of opportunities instead of another report I have to interpret from scratch.

Each opportunity is broken into practical tasks, with an agent ready to help do the work. That makes it easier for me to move from insight to execution without getting stuck in endless analysis.

For me, Projects is about spending less time deciding what to do next and more time acting on the opportunities that can improve visibility, performance, and momentum.

Inspired by this post on Try Profound Blog.

July 3, 2026
6 Claude Content Audit Workflows I Reuse for Better SEO
I see existing content as a goldmine, but only when I have a practical way to improve it. The hard part is usually finding the time, and that is where Claude has made a large, messy job feel much more manageable for me.

I do not start by building a giant content audit system. I start with one article, run one focused audit, refine the output, and then turn the prompt into a reusable Claude skill. Over time, those one-off audits become a working library I can improve every time I use it.

I use Claude to uncover topical gaps, flag outdated information, check brand voice, and evaluate whether a page is easy for AI systems to retrieve and cite. The real value comes from iteration: each time I improve a skill, the next audit becomes faster and more useful.

Here are six content audit workflows I would build in Claude. The first four work at the page level, so I can start with a single article before moving into larger library-wide analysis.

Page-level audits

When I am not ready to build a full workflow, I start with page-level audits. These audits only require one article, which means I do not need a content inventory, a data export, or a complicated setup. After each session, I ask Claude to turn the process into a reusable skill for future page-level reviews.

1. Brand voice consistency

I use a brand voice consistency audit when a content library has drifted over time. Voice can shift because of new writers, changing services, product updates, or evolving positioning. This audit helps me spot where a page no longer sounds aligned with the brand.

If I do not have detailed brand guidelines with strong examples, I let Claude extract the voice guide from high-quality content. That usually works better than relying on vague phrases like “conversational but authoritative” or “educational, not too formal.”

I pick three to five articles that represent the brand at its best. If possible, I download them as markdown files and ask Claude to describe how the voice works in concrete terms.
- How the articles usually open, such as whether they begin with a direct claim, a counterintuitive statement, or a specific scenario.
- How sentences and paragraphs are built, including average length, range, rhythm, and how paragraphs tend to close.
- Three to five personality dimensions framed as “We say X, but not Y,” with do and don’t examples.
- Words and phrases the brand tends to use, and words or phrases it should avoid.
- Specific constructions, phrases, and conventions the brand never uses.
Instead of accepting a vague voice description, I want Claude to return concrete observations. For example, it might say that articles open with a direct claim rather than a scene-setting paragraph, sentences average 15 to 20 words and rarely exceed 30, and transitions are functional, such as “here’s why that matters,” rather than formulaic, such as “furthermore.”

I also want example pairs, such as: “We’d say ‘the data shows three things,’ not ‘there are multiple factors to consider.’” The goal is not to create a voice guide for writers. The goal is to create one an LLM can understand and apply consistently.

Once I like the output, I ask Claude to save it as a skill and evaluate an article against it. If Claude flags issues I disagree with, I update the skill until the feedback becomes useful and repeatable.

I can then use that skill to find voice inconsistencies in older content, check new drafts for alignment, and even generate more on-brand first drafts. I still edit the output, but the starting point is much stronger.

Dig deeper: How to train Claude to sound like your brand

2. Coverage comparison

When I need to improve content performance, I use a coverage comparison to find topical gaps. This helps me understand what competing pages cover that my article misses.

I use the Claude in Chrome extension to have Claude review the top three to five ranking pages for my target keyword. Then I ask Claude to compare those pages against my content and highlight the most important gaps.
- What competitors are doing well.
- What my article already does well.
- Where I can improve the piece without bloating it.
If I want the output in a table, I ask Claude to format it that way. If I want a downloadable DOCX for review or handoff, I ask for that instead.

When Claude recommends additions I would never publish, I make a note of those exclusions before packaging the workflow into a skill. That way, the skill gets closer to my editorial standards each time I refine it.

3. Freshness audit

Old content adds up quickly, and it is hard to prioritize refreshes while I am also producing new material. A freshness audit skill helps me identify what needs attention without rereading every older article from scratch.

I give Claude an older article and ask it to flag anything time-sensitive: statistics tied to a specific year, named tools or platforms, references to “current” or “recent” trends, and claims that depend on a market, regulatory, or product context that may have changed. I am not asking Claude to rewrite the article yet. I am asking it to build an issue list I can act on.

If my company has launched new products, removed old services, changed positioning, or updated terminology, I include that context in the input. That helps Claude flag what should be added, removed, or revised.

Dig deeper: How to turn Claude Code into your SEO command center

4. AEO and AI retrievability

I use an AEO and AI retrievability audit to understand whether a page is likely to be surfaced in AI-generated answers. Tools such as ChatGPT, Perplexity, and Google AI Overviews tend to favor content that answers questions directly. If an article buries the answer under too much preamble, or structures key information in a way that is hard to extract, it becomes less useful for those systems.

I give Claude the article and the target query, then ask it to evaluate several retrieval signals.
- Whether the article answers the main question directly and early.
- Whether key statements are specific enough for an LLM to quote or cite.
- Where an FAQ-style section would improve clarity.
- Whether the page includes authority signals, such as primary research, first-person experience, outbound citations, or specific examples.
Once I save this as a skill, it becomes an extra editor focused specifically on AI visibility and answer retrieval.

Library-level audits

Once I am ready to move beyond individual pages, I use library-level audits. These require performance data, a content inventory, a connector, or a manual export.

5. Performance triage

When I think about a traditional content audit, performance triage is usually what comes to mind. It helps me analyze a content library and identify the pages that deserve attention first.

Before I begin, I make sure Claude has access to the right data through a connector such as BigQuery or the Semrush API. If that is not available, I export the data I normally use for large-scale audits, such as traffic, clicks, engagement metrics, conversions, rankings, and related performance signals.

I ask Claude to prioritize pages that have suffered meaningful performance drops in the past six to 12 months, pages with high impressions but consistently low click-through rates, and pages that have been live long enough to rank but never gained traction.

I also define what a meaningful performance drop looks like for the site I am analyzing, because traffic patterns vary by industry, audience, and page type. Then I ask Claude for a prioritized list of what is worth investigating and why. From there, I use the page-level audits above to diagnose the problem.

If I have run this analysis before, I give Claude the previous output. That helps the skill learn the kind of prioritization and reasoning I expect.

Dig deeper: How to build a Claude Code-powered second brain for agency work

6. Topical gap analysis

I treat entities as a major part of AEO and semantic search. A topical gap analysis helps me see whether my content library has enough coverage to build authority around the entities tied to my brand.

The core question I ask is simple: what is my content library not covering that it should?

To start, I create a list of target entities. For example, at my agency, I want to be known for SEO and AEO. If I have a clear list of services or products, I can use that instead of a formal entity list.

Using Cowork or Code, I ask Claude to analyze my sitemap and compare it to those target entities. If I have a Screaming Frog export with URLs, page titles, and meta descriptions, I use that as input for a more accurate analysis.

Then I ask Claude to identify topic clusters that are missing or underrepresented based on the target entities, services, or products. If I want prioritization, I can use the Semrush MCP so Claude can check search volume for potential keywords.

Not every gap is worth filling. I filter the results against audience needs, business relevance, and editorial standards. Then I feed those decisions back into Claude so the skill produces better recommendations next time. The final list can go directly into my content creation workflow or be handed off to a content team.

I do not try to audit everything at once

I have seen content audits stall because the scope feels too large, not because the team lacks data. My preferred approach is to pick one audit and one article, run the workflow, save the skill, and use it again on the next piece.

For me, iteration is part of the value. I enjoy taking one Claude skill, improving it, and then chaining it with other skills to uncover more content opportunities. Starting small is what makes the system easier to keep using.

Inspired by this post on Search Engine Land.
July 3, 2026
How AI Brand Discovery Turns Visibility Into Recommendations
AI brand discovery is not one visibility problem. It is a sequence: a system must find and understand a brand, select its material as evidence, include the brand in an answer, and sometimes recommend it strongly enough to influence what the buyer does next.

The source material reveals why conventional search reporting captures only part of that sequence. Organic rankings can coexist with weak AI citations, while an AI recommendation can influence a later search visit without receiving credit in referral analytics. Brands therefore need a measurement and content strategy that follows the full path from discoverability to commercial action.

AI visibility is a chain, not a single ranking

The sources describe different stages of the same process. The B2B benchmark reported by Search Engine Land examines whether brands ranking in Google are cited in AI Overviews. HiGoodie’s guidance concentrates on making content clear, credible, and understandable to answer engines. A separate Search Engine Land report covers what users did after ChatGPT recommended a brand. Its assistive-agent framework then extends the journey from recommendation toward transactions completed by software.

Combined, these perspectives suggest four distinct visibility questions. Can an AI system discover the relevant material? Can it interpret and trust that material as evidence? Does the resulting answer cite or recommend the brand? Does that exposure influence a visit, comparison, or purchase? Success at one stage does not establish success at the next.

This distinction matters because citations and recommendations serve different functions. A citation identifies a source used in an answer. A recommendation places a brand into the buyer’s consideration set. Either can create value, but the downstream effect of a recommendation may be easier to see in buyer behavior than in a referral report.

Strong organic reach can conceal an AI citation deficit

The clearest evidence of a broken handoff comes from Walker Sands’ B2B AI Search Visibility Benchmark, as reported by Search Engine Land. The analysis covered more than 45 million March search queries associated with 828 enterprise B2B companies in 14 industries. It reported that the median company ranked for about 9,700 queries and encountered AI Overviews on 48.8% of its relevant ranking keywords, yet appeared as a citation in only 3% of those AI Overviews.

The benchmark also reported that 4.6% of the companies received no AI Overview citations for any relevant keywords. Even its top quartile reached a citation inclusion rate of only 4.5%, compared with 1.7% for the bottom quartile. These findings do not show that organic search has stopped mattering. They show that ranking coverage and selection as evidence are separate outcomes.

Category exposure also varied. According to the report, AI Overviews appeared in a median 59.9% of cybersecurity searches, where brands achieved the study’s highest median citation rate of 4.2%. Distribution and logistics had the lowest reported AI Overview incidence, at 29.6%, while both that category and professional services recorded median citation rates of 2.1%. A visibility target should therefore reflect how often AI answers appear in the category as well as how frequently the brand enters them.

The benchmark associates stronger citation performance with topical depth, direct explanations, structured information, and consistent coverage across related pages. HiGoodie’s article arrives at a compatible editorial prescription: organize content around real questions, connect related topics, and support claims with credibility signals. Together, the sources favor focused subject-matter coverage over simply publishing more pages for more keywords.

Recommendations can create demand that attribution misses

Citation inclusion is an intermediate metric; buyer response is closer to the business result. Search Engine Land’s account of a Similarweb study reported that U.S. desktop users who received a specific ChatGPT brand recommendation were, on average, 2.5 times more likely to visit the recommended brand than a direct competitor within seven days. The study followed activity from July through December 2025 across selected finance, travel, and beauty brand pairs. It excluded users who had recently visited the brand or explicitly named it in their prompt.

The reported pattern appeared in all three sectors, although its size differed by brand pair. After a Capital One recommendation, for example, 14.2% of users visited Capital One and 3.8% visited American Express. After a Kayak recommendation, 12% visited Kayak and 3.4% visited Skyscanner. These are reported observations from an opted-in desktop panel, not proof that every recommendation will produce the same effect in other audiences or categories.

The more consequential measurement finding is where those visits appeared. Similarweb reportedly attributed 55.9% of AI-influenced visits to search, versus 40.4% of non-AI-influenced visits. Direct traffic accounted for 19.9% of AI-influenced visits and 38.8% of standard visits. If a user learns about a brand in ChatGPT and later searches for it, a conventional last-touch view can credit search while overlooking the conversation that formed the preference.

The study also reported deeper activity among AI-influenced visitors: averages of 12 pages and 11.8 minutes on site, compared with 6.5 pages and 5.6 minutes for other visitors. That pattern is consistent with users reaching the website after narrowing their options, although it does not by itself establish why they engaged more deeply.

A practical operating model joins content, evidence, and measurement

A useful program begins by separating opportunity from performance. Organic keyword coverage shows where a brand is discoverable. AI Overview incidence shows where generated answers can mediate that discovery. Citation inclusion shows whether the brand’s material is selected. Recommendation monitoring asks whether the brand enters consideration. Branded search, site engagement, qualified actions, and sales outcomes then help reveal downstream demand.

Build the evidence layer before chasing mentions

The shared foundation across the sources is content that both people and machines can interpret. Pages should answer a defined buyer question promptly, explain relevant concepts precisely, and make important claims easy to evaluate. Related pages should collectively demonstrate depth rather than repeat a shallow definition. Earned media and corroborating information can complement first-party material by strengthening the wider evidence available about the brand.

The assistive-agent framework reported by Search Engine Land places this work above, rather than in place of, SEO. In that model, search supplies crawled and indexed information, assistive systems add language-model reasoning and corroboration, and agents can eventually interact with business systems. This is a conceptual framework, not a measured result, but it clarifies why technical accessibility, entity understanding, and accurate business data belong in the same plan as editorial quality.

Audit the questions closest to a decision

Broad awareness coverage can reveal demand, but recommendation visibility becomes especially important when buyers compare providers, test suitability, or seek a shortlist. An audit should examine what an AI answer says, which sources it cites, whether the brand appears, how it is characterized, and which competitors receive stronger treatment. Because AI answers may vary, repeated observation is more informative than treating one response as a permanent ranking.

Measure influence without forcing false precision

AI referral traffic remains useful, but it should not be treated as the full contribution of AI discovery. Teams can examine changes in branded search, direct visits, engaged sessions, assisted conversions, and customer-reported discovery alongside citation and recommendation monitoring. None is a perfect substitute for controlled attribution; together, they can expose demand that a referral-only dashboard would miss.

Key takeaways
- Organic rankings create discoverability, but they do not guarantee inclusion in an AI-generated answer.
- AI citations, brand recommendations, website visits, and transactions are different stages and require different measures.
- Clear answers, topical depth, structured information, and corroborating authority form the content foundation described across the sources.
- AI-influenced demand may later appear as search traffic, so referral analytics alone can understate AI’s role.
- Category-level AI exposure should shape priorities because the incidence of generated answers and citation rates can differ substantially.
As more discovery and evaluation move into generated answers, the defensible advantage will come from connecting machine-readable evidence with trustworthy buyer experiences. The next step is not merely to seek more AI mentions, but to learn which questions create recommendations and whether the business is prepared to convert the demand they produce.

References
July 3, 2026
Why I Stop Positioning AI as a People Replacement

I think one of the biggest mistakes in AI marketing is positioning a product as a replacement for people. That message can win attention in the short term, but I believe it quietly drains trust over time.

This is a little different from what I usually write about, but it matters. The way we talk about AI shapes how customers, employees, executives, and markets respond to it.

In this memo, I want to focus on three things: why “substitution positioning” feels powerful at first but weakens a brand later, what the data says about whether AI is actually replacing people, and how I think companies should position AI instead.

The cardinal sin of positioning in the AI era is replacement. I call it substitution positioning. It is tempting because it sounds bold, efficient, and disruptive. But over time, it creates anxiety, skepticism, and credibility problems.

We have seen this pattern already. Anthropic CEO Dario Amodei predicted that software engineering jobs could disappear within 6 to 12 months as models began doing most or all of what software engineers do end to end. Yet demand for software engineers has continued to look strong.

OpenAI CEO Sam Altman also predicted that many customer support jobs would go away because AI could handle that work better. Soon after, customer service hiring began outpacing the broader job market.

I understand why fear works as a marketing tool. The fear of being replaced gets attention fast. It got me, too. When powerful AI models gained traction, I worried about my own future. But when I still see AI companies hiring copywriters, SEOs, engineers, and support teams, I sleep better.

Fear sells because it taps into fight-or-flight. Layoffs make that story even louder. They let companies frame cost-cutting as innovation and make the replacement narrative feel more real than it may actually be.

But I do not think the facts support the clean replacement story. In New York, companies can indicate when mass layoffs are caused by technological innovation or automation. In one reported period, more than 160 companies filed mass layoffs affecting roughly 28,300 workers, and not one chose AI as the reason. That list included companies such as Amazon and Goldman Sachs.

Researchers at Yale also studied employment data from the Current Population Survey over 33 months and found no evidence of job displacement from AI. To me, the pattern looks less like instant replacement and more like the earlier waves of computers and the internet changing how work gets done.

That is why I keep coming back to this point: stop trying to make replacement happen. It is not happening in the simple, dramatic way many AI narratives suggest.

AI is powerful, but it is also inconsistent. In its current form, it can do some tasks better than humans and fail badly at others. That paradox is often called the Jagged Frontier.

The Jagged Frontier idea matters because it explains why some people see AI as transformative while others remain lukewarm. A BCG and Harvard study of 758 knowledge workers found that people get the most value from AI when they understand what it is good at and where it breaks down.

Microsoft reached a similar conclusion in its 2026 Work Trend Index Annual Report. The company found that a small group of advanced AI users, described as Frontier Professionals, were not simply using AI more often. They also knew which mode of AI use fit each task.

That distinction is important. The best AI users are not handing everything over blindly. They are applying judgment. They know when to use AI as a helper, when to use it as a collaborator, when to use agents for multi-step workflows, and when to keep a human firmly in control.

I still do not trust most AI workflows enough to leave them running with no maintenance, review, or quality assurance. The question I ask is simple: would I bet my brand, customer experience, or revenue on a fully automated workflow with no human oversight?

Klarna is a useful warning here. The company publicly promoted the idea that AI was doing the work of hundreds of agents and helping reduce headcount. Later, it reversed course and rehired humans after leadership acknowledged that aggressive cost-cutting had lowered quality and that customers still wanted a human option.

That is the tradeoff I see with substitution positioning. It creates immediate attention, but it can damage long-term credibility. The words often do not match the operational reality.

Replacement positioning could work if customers truly wanted full replacement and if the technology were consistently ready for it. I do not think either condition is true.

Cost reduction is a strong AI argument because it shows up quickly on the P&L. Productivity gains usually take longer. They build inside companies over time and often take even longer to appear across the broader economy.

But when replacement positioning goes beyond cost-cutting and becomes people-cutting, I believe it starts to antagonize the very people companies need to win over.

We have already seen backlash. Duolingo’s AI-first memo drew heavy criticism before the company reframed AI as a tool to accelerate work rather than replace contractors. Surveys have found that some workers refuse to use AI tools because they fear job loss. Pew has reported that many U.S. adults are more concerned than excited about AI in daily life. Reuters/Ipsos polling has shown widespread fear that AI will permanently displace workers.

There is also a quality problem. When employees believe the purpose of AI is to replace them, they may disengage or produce lower-quality work. In my view, that is not just an adoption issue. It is a positioning failure.

Executives often feel more excited about AI than the employees asked to use it every day. That gap matters. If leadership talks about AI as a replacement engine, employees hear a threat. If leadership talks about AI as leverage, employees have a reason to learn.

Token economics also complicate the replacement story. Some companies have bragged about massive AI usage, but token costs are still a real business variable. As those costs normalize, the math may make junior employees look interesting again, especially when human judgment, context, and accountability are part of the output.

So what should replace replacement? I think the answer is enhancement. Instead of positioning AI as a way to remove people, I would position it as a way to make capable people more effective.

AI can be used in two broad ways. A company can try to reduce the number of people, or it can grow output with the same number of people. The data I have seen suggests that productivity gains often create the stronger return.

A National Bureau of Economic Research paper surveyed 750 executives about AI’s impact on productivity and labor markets. Larger firms showed more interest in replacing labor costs, but the highest ROI came from productivity growth.

That is the lesson I take from the research: doing more with the talent you already have is often stronger than trying to remove the talent that knows what good work looks like.

Building products has become easier, but distribution has not. When supply explodes, the scarce thing is not output. The scarce thing is being the product, brand, or service that actually gets chosen.

That is why positioning matters more than ever. Product quality still matters, but the way I frame AI use can determine whether people see it as empowering or threatening.

My takeaway is simple: I would stop selling AI as a people replacement. I would sell it as judgment leverage, workflow acceleration, and creative expansion. Fear can get attention, but empowerment is a better long-term strategy.

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

Inspired by this post on Search Engine Land.

July 3, 2026