Category: AI SEO

My AI Content Gap Workflow for Smarter SEO Priorities
I can publish consistently, follow SEO best practices, and still watch competitors outrank me. When that happens, I usually find that the issue is not content quality alone. It is content coverage. Competitors are answering questions my audience is already asking, while my site is not fully part of that conversation yet.

That is where I use content gap analysis. It helps me identify the topics competitors rank for that I do not, then decide which opportunities are actually worth pursuing.

Finding gaps is rarely the hard part. SEO tools make that fairly easy. The real challenge is making sense of thousands of keywords across several reports and deciding what deserves attention first.

My workflow combines competitor data, first-party search data, and AI so I can prioritize content opportunities around business impact instead of search volume alone.

I bring my SEO data together before analyzing it

In this workflow, I use Semrush to identify competitive opportunities, Google Search Console to validate where my site already shows signs of authority, and Google Analytics to add business context. Then I use Claude to bring those datasets together, group related opportunities, identify patterns, and help me decide what belongs on the content roadmap.

I follow this process in one of two ways.
- I export reports directly from the platforms and upload them to Claude.
- If I have connected those platforms through MCP (Model Context Protocol, a standard that allows AI models to connect securely to data sources), I let Claude pull the data directly without manual exports. The workflow changes, but the analysis does not.
Here is the process I use to turn a pile of SEO data into a prioritized content plan.

Step 1: I choose the right competitors

A content gap analysis is only as useful as the competitors I compare myself against. That sounds obvious, but it is one of the easiest places to go wrong.

If I compare my site to Amazon, Reddit, or Wikipedia, I will end up with thousands of keyword “opportunities” that were never realistic in the first place. My goal is not to find every site ranking for my target keywords. My goal is to find businesses competing for the same audience.

I usually start with Semrush’s Organic Competitors report. Instead of relying only on a list of known competitors, I use this report to find domains that compete across many of the same keywords. From there, I narrow the list to three to five sites that closely match the business and target audience I am analyzing.

I do not worry if a few familiar names do not make the cut. Business competitors and organic search competitors are not always the same.

I also filter out sites that can distort the analysis, including large marketplaces like Amazon, community-driven sites like Reddit or Quora, reference sites like Wikipedia, local directories, review sites, and publishers that do not directly compete with the business.

There are exceptions. If I am analyzing a publisher, comparing against other editorial sites makes sense. The key is choosing competitors that create the type of content I am realistically trying to outperform.

A Semrush competitor analysis view turns organic search data into a clear map of rival domains, traffic potential, keyword overlap, and content gap opportunities.

Before I move forward, I sanity-check the competitor list with stakeholders. Sales or product teams may know about newer competitors or strategically important niches that do not yet show up clearly in Semrush.

Once I have settled on the right competitors, I am ready to find the gaps that matter most.

Step 2: I gather and prepare the data

With the competitor list finalized, I collect the data Claude will analyze. Whether I upload exports or connect through MCP, the goal is the same: bring together competitive rankings, my site’s search performance, and engagement data so I can separate meaningful opportunities from noisy keyword lists.

I like to pull data from three core sources.

Semrush: I find the gaps

I start with Semrush’s Keyword Gap tool using the competitors selected in Step 1.

From there, I pay close attention to three buckets: keywords competitors rank for and I do not, keywords where I rank but competitors rank higher, and keywords where I rank but competitors do not.

The first bucket often points to missing topics or content hubs. The second bucket can reveal quicker wins, especially when my site already appears on Page 1 or Page 2. The third bucket shows existing strengths that I should protect and continue building around.

Google Search Console: I validate the opportunity

Next, I check Google Search Console before assuming every missing keyword deserves a new page.

For example, Semrush may show that I do not rank for a keyword, but GSC might reveal that I already receive impressions for closely related queries. That tells me Google has started associating my site with the topic, even if rankings have not caught up yet.

Those “almost there” topics often deserve a higher priority than topics where I would be starting from scratch.

In GSC, I look for queries with high impressions and average positions between 8 and 20, existing pages ranking for related terms, and long-tail queries that reveal additional search intent.

Google Analytics: I add business context

Search volume is only part of the story. Engagement metrics help me answer a more important question: if I improve visibility for this topic, is it likely to support business goals?

A Semrush content gap analysis view reveals where a competitor ranks and the analyzed site does not, turning keyword overlap data into a practical roadmap for SEO content opportunities.

I review metrics such as organic sessions, engagement rate, average engagement time, key events or conversions, and landing page performance.

If a related content hub already drives engaged visitors or conversions, expanding that topic may be a smarter investment than chasing a completely new keyword with higher search volume.

I clean the data before handing it to Claude

If I am manually downloading the data and uploading it to Claude, I clean it first. Claude is excellent at finding patterns, but it can only work with the data I provide. Cleaner data leads to cleaner topic clusters and better recommendations.

I remove duplicate keywords, competitor-branded terms, careers queries, login queries, support queries, locations or product lines outside the business, keywords with clearly different search intent, and high-intent commercial keywords that are too broad to compete for.

For a manual workflow, I export Keyword Gap data from Semrush, query data from Google Search Console, and landing page performance data from Google Analytics, then upload the files to Claude. For a connected MCP workflow, I ask Claude to retrieve the Keyword Gap report, GSC query data, and GA4 landing page metrics directly from connected accounts.

Step 3: I ask Claude to find the story in the data

At this point, I should have a clean dataset that combines competitive keyword gaps, Search Console performance, and Google Analytics data.

This is where the workflow becomes much more useful. Instead of scrolling through thousands of rows looking for patterns, I ask Claude to organize the data into something I can actually build a strategy around.

The mistake I see most often is asking AI to “cluster these keywords.” That usually produces clusters based on keyword similarity alone. That can be useful, but it does not tell me what to do next.

Instead, I ask Claude to think like an SEO strategist. I give it context about the business, including products or services, target audience, primary business goals, content priorities or constraints, and the exported or connected data from Semrush, GSC, and Google Analytics.

Then I ask Claude to organize opportunities by search intent, funnel stage, business relevance, existing authority signals from GSC, user engagement from GA4, recommended content format, and internal linking opportunities.

Rather than returning a spreadsheet of grouped keywords, I want Claude to produce topic clusters with a clear recommendation for each one.

For example, one cluster might be labeled Technical SEO Audits and include supporting keywords, estimated opportunity, existing pages that could be updated, whether a new page is needed, internal linking recommendations, a priority score, and the reasoning behind the recommendation.

A content gap workflow turns scattered SEO signals into topical clusters, showing where AI search visibility, privacy-first analytics, and technical SEO need deeper coverage.

Another cluster might reveal that several competitor keywords can be addressed by expanding an existing guide instead of publishing three separate articles. That is the kind of insight that is hard to spot manually but much easier for AI to surface.

I separate quick wins from long-term investments

Not every opportunity belongs on the same roadmap. As part of my prompt, I ask Claude to classify each cluster into quick wins, new content opportunities, and authority plays.

Quick wins are existing pages that can be refreshed, expanded, or better optimized. New content opportunities are topics that deserve dedicated content because the site has little or no visibility. Authority plays are larger subject areas that may require multiple pieces of content and ongoing investment to compete effectively.

This simple step helps me move from an overwhelming keyword list to a roadmap with both short-term wins and long-term initiatives.

I do not skip the human review

Claude can organize information remarkably well, but it does not know the business the way I do.

Before moving on, I ask whether the topic supports business goals, whether multiple search intents are being combined into one cluster, whether existing content could already satisfy the need, whether the opportunity is realistic given authority and resources, and whether I would actually assign the topic to a writer.

If the answer is no, I refine the cluster or remove it.

The goal is not to accept every AI recommendation. The goal is to spend less time organizing data and more time making strategic decisions.

The biggest prompt lesson is simple: I do not ask Claude to organize keywords. I ask it to recommend what my content strategy should be based on the data I have provided.

Step 4: I score and prioritize the opportunities

Once Claude has grouped the keywords into topic clusters, the next step is deciding what deserves attention first.

This is where many content gap analyses fall apart. Teams naturally gravitate toward the biggest search volumes, but volume is only one piece of the puzzle. A topic that attracts qualified visitors and supports business goals is often a better investment than a high-volume keyword that is difficult to rank for or unlikely to convert.

I score each opportunity across several criteria before I build a roadmap.

A prioritized content gap roadmap turns scattered SEO data into clear next moves, ranking quick wins by impact, effort and AI visibility.

Business relevance

I start with a simple question: if this content performs well, does it help the business?

Topics aligned with products, services, or the customer journey should carry more weight than informational topics with little commercial value.

Existing authority

Next, I look at signals from Google Search Console. If my site already earns impressions or ranks on the second page for related queries, Google has likely established some level of topical authority.

In those cases, improving an existing page or expanding a content hub may produce results much faster than starting from scratch.

Search demand

Search volume matters, but I do not let it dominate the scoring model.

A collection of related long-tail queries with moderate demand can sometimes generate more qualified traffic than one broad keyword.

Ranking difficulty

I review the current search results before committing to a topic. I look at whether authoritative brands dominate the first page, whether the intent is informational, commercial, or transactional, what types of content are ranking, and whether I can realistically create something more useful or complete.

This quick reality check keeps me from chasing opportunities that are not practical.

Estimated effort

Finally, I consider the work involved. Some opportunities require a light refresh of an existing article. Others call for a new content hub supported by multiple pages.

Both can be worthwhile, but they should not carry the same priority when resources are limited.

I let Claude apply the framework

Once I define the scoring criteria, Claude can evaluate every topic cluster consistently.

For example, I may ask Claude to score each opportunity on a five-point scale for business relevance, existing authority, search demand, ranking difficulty, and content effort. Then I ask it to calculate an overall priority score and explain why each recommendation received that score.

A tactical SEO refresh brief turns AI-assisted content gap analysis into page-level priorities, surfacing validation lessons, effort estimates, and the biggest opportunities.

The explanation is just as valuable as the number. If I disagree with a recommendation, I can adjust the weighting, add more business context, and ask Claude to score the opportunities again.

By the end of this step, I have more than a list of content ideas. I have a prioritized content strategy that shows what to tackle next, what can wait, and what is not worth pursuing.

Step 5: I turn priorities into page-level recommendations

Once I have prioritized the opportunities, the next step is figuring out exactly what to change.

Rather than handing a team a ranked list of topics, I ask Claude to generate page-level recommendations for the highest-priority opportunities. This is where connected data becomes especially valuable.

Because Claude has access to Semrush research, Google Search Console performance, Google Analytics metrics, and my prioritization framework, it can evaluate each page in context instead of treating every recommendation the same.

For each priority page, I ask Claude to produce a recommendation that explains why the page was selected, the primary keyword cluster, current rankings and impression data, supporting evidence from GSC and competitor research, recommended updates, estimated effort, expected impact, and priority level.

One of the biggest advantages of this approach is validation.

Before recommending a refresh, Claude can compare URL-level Search Console data against the original analysis. Sometimes what looks like a strong opportunity turns out to be misleading. A keyword may have inflated impression counts, a URL could have been mislabeled in an export, or the page may not be as close to ranking as it first appeared.

Catching those issues before assigning work can save hours of unnecessary effort.

The recommendations also make stakeholder conversations easier. Instead of saying, “I think we should update this page,” I can point to the supporting data, explain why it is a priority, estimate the effort involved, and tie the recommendation back to the larger content strategy.

I treat these recommendations as implementation plans rather than full content briefs. They help SEO and content teams understand what should change, why it matters, and where to focus first. Writers can then use those recommendations to create or update content with confidence.

Step 6: I measure whether the gap is closing

Publishing the content is not the finish line. It is the start of the next round of analysis.

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.

I begin with Google Search Console, tracking whether target queries are gaining impressions, improving in average position, and generating more clicks. When I refresh an existing page, I compare performance before and after the update to see whether the changes actually moved the needle.

Next, I look at Google Analytics. Better rankings do not always translate into better business outcomes, so I review organic traffic alongside engagement and conversion metrics. If an updated page attracts more visitors but fails to keep them engaged or contribute to conversions, I know it is time for another round of optimization.

If I am using Claude through MCP, I can also ask it to compare performance over time and summarize what changed. I might ask which refreshed pages improved the most, which content clusters gained the most visibility, which recommendations drove the strongest business results, and which opportunities still need attention.

Instead of comparing reports month after month, Claude can quickly surface significant changes and point me toward the pages that deserve attention.

I do not treat content gap analysis as a one-time exercise. Competitors publish new content, search behavior shifts, and my own site authority evolves. I like to repeat this workflow every quarter, or more often in fast-moving industries, so I can keep finding new opportunities and stay ahead of competitors.

The tools will continue to improve, but the repeatable workflow is what creates the advantage.

I build a repeatable content gap analysis process

A content gap analysis helps me prioritize opportunities worth pursuing instead of chasing every possible keyword.

Semrush helps me uncover competitive gaps. Google Search Console shows where I already have momentum. Google Analytics adds the business context that rankings alone cannot provide. Claude brings those datasets together, helping me identify patterns, prioritize opportunities, and create actionable recommendations in a fraction of the time it would take manually.

Whether I upload reports or connect my tools through MCP, the workflow stays the same. I gather the right data, validate the opportunities, let AI organize the information, and apply my own expertise to decide what comes next. That is the part AI cannot replace.

The biggest advantage is not simply having better prompts or faster analysis. It is having a repeatable process that helps a team make smarter content decisions every quarter.

Prompt template: My prioritized content gap roadmap

Here is the prompt I use after I have gathered the data, whether I have uploaded exports from Semrush, Google Search Console, and Google Analytics or connected those tools to Claude through MCP.

“You are an experienced SEO strategist helping me perform a content gap analysis.
I’ll either provide exported reports from Semrush, Google Search Console, and Google Analytics, or you’ll access those tools through connected MCP integrations.
My goal is to identify the highest-impact content opportunities based on competitor visibility, existing authority, business value, and implementation effort.
Here’s my business context:
– Company:
– Industry:
– Products/services:
– Target audience:
– Primary business goals:
– Geographic focus:
– Any strategic priorities or constraints:
– Tone of voice: [Insert brand voice adjectives here (e.g., authoritative, conversational, technical)].
Using the available data, complete the following tasks.
1. Identify content gaps
Organize keywords into these categories:
– Competitors rank and we don’t.
– We rank below competitors.
– We rank and competitors don’t.
Highlight any content gaps, opportunities to consolidate pages, or keyword cannibalization issues.
2. Validate the opportunities
Use Google Search Console data to determine:
– Which topics already receive impressions.
– Which pages rank between positions 8 and 20.
– Which existing URLs have the strongest chance of improving with optimization.
Use Google Analytics data to determine:
– Which pages drive meaningful engagement.
– Which pages contribute to conversions.
– Which content hubs are worth expanding.
3. Create strategic topic clusters
Group related opportunities by:
– Search intent
– Business relevance
– Funnel stage
– Recommended content type
– Internal linking opportunities
Don’t cluster based only on keyword similarity. Focus on topics that should become part of the same content strategy.
4. Prioritize every opportunity
Score each topic cluster using:
– Business relevance
– Existing authority
– Search demand
– Ranking difficulty
– Estimated effort
Assign each opportunity a priority (High, Medium, Low) and explain why.
Separate recommendations into:
– Quick wins
– New content opportunities
– Long-term authority investments
5. Recommend next steps
For every high-priority opportunity, recommend whether we should:
– Refresh an existing page
– Consolidate multiple pages
– Create a new page
– Build a pillar page with supporting content
Include supporting evidence for every recommendation.
6. Deliver the results
Create:
– An executive summary
– Prioritized topic clusters
– A scored opportunity table
– Page-level recommendations for the highest-priority URLs
– A phased implementation roadmap (30, 60, and 90+ days)
If you find conflicting data between Semrush, Google Search Console, and Google Analytics, explain the discrepancy and recommend which source should guide the decision. The output should both be HTML and a Google Sheet.
Before presenting your final recommendations, validate your own analysis. If reviewing Search Console or Analytics data changes your original recommendation, explain why and update your prioritization accordingly.”

This prompt is only a starting point. I add business context, editorial guidelines, and scoring criteria that are unique to the organization I am analyzing. The more context I give Claude, the more useful and actionable its recommendations become.

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
AI Search Visibility: How Brands Get Used and Cited

I’m seeing traditional Google rankings deliver less predictable value than they once did. Ads, AI Overviews, and other search engine results page features are pushing organic links farther down the page, which means visibility no longer depends only on where a brand ranks in the classic blue-link results.

As search keeps shifting, I believe brands need to ask a more practical question: how do I make sure my brand is represented accurately inside AI-powered answers?

The more I understand how AI engines use brand information and when they cite it, the easier it becomes to build a real AI visibility strategy. This moves the conversation beyond whether an AI model “knows” a brand and toward how that brand can earn presence, trust, and discoverability in AI search.

The click economy is shrinking

I think most brands should start learning AI search and building an AI SEO strategy now. A full shift from organic search to AI search may still be years away, but the direction is clear enough that waiting creates risk.

Google is already leaning hard into AI search. In an April article from The Verge, CEO Sundar Pichai said that search had a strong quarter, with AI experiences driving usage, queries reaching an all-time high, and revenue growing 19%.

Users are changing their behavior too. A Pew Research study found that when people see an AI-powered summary in search results, they click a blue link only 8% of the time. When no AI summary appears, that click rate rises to 15%.

AI search traffic may still be smaller than organic traffic, but I would not dismiss it. According to Similarweb, AI traffic converted at 11.4%, compared with 5.3% for organic search traffic. That makes AI visibility worth tracking even before it becomes the dominant traffic source.

How I separate AI usage from AI citation

I think about brand presence in AI systems in two main ways: usage and citation.

Usage happens when an AI engine ingests information about a brand and draws on that information when answering a query. In some ways, this reminds me of how Google traditionally indexed pages before ranking and serving them in search results.

When an AI engine uses brand content, it may mention the brand without linking to it. Even an unlinked mention can matter because it can create discovery, influence perception, and prompt users to search for the brand directly.

Ahrefs data shows most Google AI Overview citations still come from high-ranking organic pages, with 76.10% in the top 10 and a smaller share outside the top 100.

Citation is different. A citation happens when an AI engine directly references a brand as a source of information. That reference might be a link to a web page, a social profile, or even a clickable phone link that lets someone contact the business.

Within OpenAI, usage and citation appear to depend on separate technical systems. As OpenAI’s documentation explains, OAI-SearchBot and GPTBot are deployed separately among four distinct user agents. Other AI systems have their own controls, but the same broader distinction still applies.

Why citations do not tell the whole story

I do not see citations as the full AI visibility picture. AI engines often answer questions directly without citing web sources, and this pattern is not entirely new. Before AI Overviews, Google was already moving in that direction with featured snippets.

Ahrefs found that ChatGPT retrieves almost the exact same number of cited and uncited URLs to generate an average response: about 16.57 cited URLs and 16.58 uncited URLs. But Reddit made up 67.8% of uncited URLs, which means comparing cited and uncited URLs is often really a comparison between search results and Reddit API output.

That matters because AI systems are not neutral in the uncited information they surface. Some platforms and websites are simply more influential than others. If I try to push a brand into AI answers without understanding where the model gets its information, I am working at a disadvantage.

How I would improve brand usage and citation

I would start by tracking the brand’s current AI visibility and monitoring progress over time. That means running a representative set of prompts through an AI visibility platform, reviewing the sources that get cited, and asking what those sources reveal about the model’s preferences.

There are already many AI citation tracking tools available, and established platforms like Semrush and Ahrefs have added AI tracking features as well. I would choose a tool based on the prompts, markets, and engines that matter most to the brand.

I would also scale tracking and research as much as budget allows. AI prompt tracking often depends on API calls, so it can cost more than traditional rank tracking. Still, the data is usually richer, even when the sample size is smaller.

As long as the prompt sample is broadly representative, most platforms can pull multiple responses and calculate an average. That gives me a more useful view of recurring patterns instead of relying on one-off answers.

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

I would keep reading studies from AI platforms, SEO vendors, and data providers too. Those reports are valuable because they show which sources AI engines rely on and where brands may have the best chance to appear.

The key is continual monitoring. Over time, I can work to place a brand inside the sources AI engines already trust and use most heavily.

Why I still care about traditional rankings

Yes, I still think traditional search rankings matter, but not for the same reasons they used to. The relationship between organic position and business performance is less direct now, especially as SERP features and AI answers absorb more user attention.

At the same time, Ahrefs research suggests a relationship between AI citations and Google rankings, at least inside Google AI Overviews. A July 2025 study found that 76.1% of pages cited in AI Overviews ranked in Google’s top 10 organic results. If AI Overviews become a dominant AI search experience, traditional rankings will still influence visibility.

I also pay attention to content quality. Semrush found that AI engines rarely cite generic content that simply repeats what other sources already say. The content that earns citations usually contributes something distinct.

That fits closely with Google’s helpful content guidance, which rewards original information and useful perspective. In my view, content with trusted data, original insight, and a clear point of view can support both Google rankings and AI citations.

Because many classic SEO tactics can also support AI citations, I would not abandon traditional SEO. I would treat it as part of a broader visibility strategy that now includes AI usage, AI citations, and brand presence across trusted third-party sources.

Where I think AI visibility is heading

Both usage and citation need ongoing tracking and analysis. If I want AI engines to use a brand’s knowledge and content, I need to understand which sources each model relies on and help the brand appear in those places. If I want citations, I need the brand’s content to stay crawlable, rank well, and say something original.

Classic SEO still earns its place because the same work that improves organic visibility can often improve AI visibility too. But returns from traditional rankings are changing, and AI SEO may eventually become the primary discipline. For now, I would keep ranking, start tracking, and build for both usage and citation.

Inspired by this post on Search Engine Land.

July 7, 2026
Conductor MCP Server: Trusted AEO and SEO Data for AI

I use Conductor’s MCP Server to ground the AI tools my team already relies on in verified AEO and SEO intelligence, instead of depending on a stale snapshot of the web.

A bold launch visual introduces an AEO and SEO Intelligence Layer, framing verified search and AI visibility data as a modern layer for marketing teams.

Inspired by this post on Conductor Blog.

July 7, 2026
Goodie vs. Semrush: A Smarter AEO Platform Comparison

When I compare Goodie and Semrush for AI search visibility, I’m looking beyond traditional SEO dashboards. I want to understand how each platform supports answer engine optimization, from monitoring AI visibility to improving the signals that influence AI-generated answers.

A modern AEO performance dashboard brings AI search visibility, brand mentions, traffic attribution, and revenue signals into one measurement view.

For me, the key difference comes down to focus. Goodie is built around AEO monitoring, optimization, agentic commerce, and revenue attribution, while Semrush brings the depth of a broader SEO and competitive research platform.

A Semrush project dashboard brings SEO health into one view, from keyword rankings and site audit trends to optimization ideas and backlink toxicity signals.

In this comparison, I look at how both platforms help brands get discovered, cited, and recommended across AI search experiences, and how each one connects visibility to measurable business impact.

Inspired by this post on HiGoodie Blog.

July 6, 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
6 SEO Priorities I’m Rethinking for Stronger AI Visibility

I see plenty of overlap between SEO and AEO, but I do not treat them as the same discipline. The SEO playbook that worked reliably in traditional search will not take me as far when the goal is visibility inside AI-generated answers.

So I keep coming back to one practical question: what should I change first?

Instead of revisiting content structure for AI search, I focus on three priorities I believe deserve more attention now and three SEO habits I would intentionally emphasize less.

3 SEO priorities I would emphasize more

Establish brand authority and strong entities

Before an AI system is likely to cite my brand, it needs to understand that the brand exists, what it represents, and why it is credible. Entity recognition has become foundational to AI visibility in a way that traditional search did not always require, even though Google’s Knowledge Graph has been moving in this direction for years. Large language model training data tends to reward brands that show up consistently across trusted platforms.

When I work on this for clients, I pay closer attention to whether brand information is consistent across Wikipedia, LinkedIn, Crunchbase, industry directories, and any other source an LLM might use to understand an entity.

I also think PR and SEO or AEO teams need to work much more closely together. Earned media mentions are no longer just awareness plays; they are entity-building signals.

E-E-A-T was already pushing SEO in this direction, but author entities matter even more in AI search. When bylined experts have their own credible web presence, they strengthen the authority of the content they create.

When I can invest in entity building before scaling content, I usually see stronger AI citation potential because the credibility infrastructure is already in place.

Build topical depth with content clusters

AI systems tend to favor sources that show comprehensive authority on a subject, not just pages that happen to rank for isolated keywords. A thin content footprint is much more vulnerable in AI search than it was in traditional search.

That means I need to move beyond keyword-by-keyword planning and think more seriously about topic ownership. Instead of only asking, “What do we rank for?” I ask, “What topics do I want AI systems to associate this brand with?”

Internal linking becomes more valuable in this environment because it helps signal relationships between related pieces of content. I also treat content audits as a way to find gaps in topical coverage, not just as a way to identify pages with declining traffic.

When I can go deep in a specific niche, I often see content cited across multiple related queries. One well-built content cluster can create visibility far beyond a single keyword target.

Owning the topic cluster around the problem a client’s product solves can position that brand as a trusted resource before a sales conversation even begins. I also hear more often that buyers are finding those brands in LLMs during their research process.

Earn unlinked brand mentions and community presence

LLMs learn from the broader web, not only from pages with backlinks. A mention on Reddit, Quora, a niche forum, or an industry community can matter even when there is no link attached.

I think this is one of the bigger mindset shifts for SEO teams. AI systems look for patterns in what the web says about a brand across many sources, not only what ranks in Google. Owned content alone cannot manufacture that signal.

Trusted third-party communities such as Reddit can carry particular weight because LLMs have been heavily trained on them and often treat that content as a form of authentic user sentiment.

That makes community participation and digital PR increasingly important SEO-adjacent work. I care about whether a brand is being mentioned in the right places, even when the mention does not come with a backlink.

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.

Monitoring unlinked brand mentions is becoming just as important to me as tracking backlinks. Tools such as Brandwatch and Mention, along with manual Reddit and Quora monitoring, can show where a brand is appearing organically and where it is absent.

I would rather talk with the team about where the brand is being discussed, whether those conversations are accurate, and whether the sentiment is positive than focus only on who is linking to the site.

Brands with an active presence in relevant communities are more likely to surface naturally in conversational, recommendation-style AI answers, including queries such as “What does Reddit think about X?” or “What’s the best Y according to users?”

For challenger brands trying to enter a category, earned community mentions can build AI-visible authority faster than traditional link building, which usually takes longer to accumulate.

B2C brands can benefit especially from genuine community presence because consumer AI queries often lean toward social proof and peer recommendations rather than formal editorial sources.

3 SEO priorities I would emphasize less

Chasing high-volume keywords with thin content

AI Overviews can absorb the click for broad informational queries. Ranking No. 1 for a head term increasingly means I may have invested a lot of effort into winning traffic that never actually reaches the site.

Search volume alone is no longer a reliable proxy for opportunity. A query with 50,000 monthly searches that triggers an AI Overview may send less traffic than a query with 2,000 searches that still requires a click.

I would rather create specific, authoritative content that answers a narrower question better than anything else available. I focus more on queries where the searcher needs to act, compare options, or access something only the site can provide. Those needs are harder for AI to fully resolve.

Keyword traffic potential is no longer the first metric I trust. I first ask whether someone will still need to click after AI answers the query. If the answer is no, the opportunity is not what it used to be.

Pursuing exact-match and manipulative link building

Low-quality link volume does not do much for AI citation likelihood. LLMs care more about the authority and relevance of the sources mentioning or citing a brand than raw link counts. The publications that matter for AI visibility usually have real editorial standards, and those are much harder to game.

I would focus on earning coverage and links from the kinds of sources AI systems are more likely to draw from, including trade publications, respected industry blogs, and academic-adjacent resources. The better long-term move is to build content worth referencing, not outreach that exists only to extract a link.

A hundred low-quality links will not necessarily get a brand cited in ChatGPT. Five links from publications the target audience actually reads might matter much more. Source authority is the metric I would watch more closely than link volume.

Optimizing for CTR on standard blue links

A growing share of informational queries are resolved without a click. That makes title tag and meta description optimization for CTR less valuable on queries dominated by AI Overviews. I would rather spend that time trying to become the cited source inside the AI answer.

For queries where clicks still happen, I put more weight on transactional and navigational intent because those searches are more resistant to full AI resolution.

CTR optimization assumes a searcher is choosing between blue links. For more queries now, that choice is shaped before the traditional results even become the focus. The opportunity has moved higher on the page.

The payoff is not always more traffic

There are more shifts I could make, but these are the first ones I would prioritize. I may lose some volume in traditional SEO metrics such as impressions and clicks, but that should matter less if the downstream business metrics remain strong. In AI search, I care more about conversions, pipeline, and revenue than vanity traffic. That is the tradeoff I believe this new search environment increasingly rewards.

Inspired by this post on Search Engine Land.

July 6, 2026
Why I Think Meta AI Is Search’s Sleeping Giant Now

I do not think enough people are treating Meta AI as a serious AI search contender.

In SEO circles, I hear plenty about Google AI Mode, ChatGPT, Claude, Gemini, Perplexity, RAG, and every new answer engine worth testing. Those conversations matter. But I think Meta AI already has something most AI companies would spend years and billions trying to build: massive distribution.

By May 2025, Meta AI had reached one billion monthly active users across Meta’s apps, according to Mark Zuckerberg.

Zuckerberg has also made the direction clear. He wants Meta AI to become a leading personal AI, shaped around personalization, voice conversations, and entertainment, with monetization through paid recommendations or subscriptions already being considered.

That is why I think Meta AI is becoming one of the most important AI search contenders to watch.

Meta’s Advantage Is Distribution

I think the AI search debate spends too much time on model quality and channel ownership. Which tool is smarter? Which answer engine cites better? Is this just SEO with a new label?

Those questions matter, but distribution matters more than the search industry often wants to admit.

Meta reported 3.56 billion family daily active people across its apps in March. In that same quarter, revenue reached $56.31 billion, up 33% year over year.

WhatsApp passed 3 billion monthly users in 2025. Instagram reached 3 billion monthly active users in September 2025. Threads reached 500 million monthly active users in June.

I know Facebook is not the cool platform anymore. The metaverse stumbled. Threads can still feel like a corporate response to Elon Musk running, or ruining, the artist formerly known as Twitter.

But none of that changes the important point. Meta can put AI inside the apps where people already spend their time. In doing that, it can bring search-like behavior directly into the places where discovery already happens.

I think that could push public AI adoption faster than almost anything else in the market.

The First Search Is The Search That Matters

Google’s historic power has always rested on a simple habit. When people wanted to know something, compare options, buy a product, find a local business, or settle an argument, they started with Google.

That starting point became the most valuable real estate on the internet.

AI search changes where that starting point can live. If someone sees a product on Instagram, they do not have to leave the app and search Google. They can ask Meta AI whether the product is any good, what alternatives exist, whether the brand is trustworthy, or where they can buy it.

If a WhatsApp group is planning a weekend away, they do not need to switch to Google to compare hotels, restaurants, venues, or train times. Meta AI can sit inside the conversation at the exact moment intent appears.

If someone is scrolling through a Facebook thread full of local recommendations, they can ask Meta AI to summarize what people are saying across Groups, Reels, and public posts.

That is not traditional SEO. I see it as search behavior being absorbed into social platforms.

The strategic question is no longer only, “Who ranks?” I think the better question is, “Where does the question begin?”

Meta AI Is More Than Another Chatbot

I think search marketers often approach AI through too narrow a lens. We find the chatbot, test a few brand queries, check which sources get cited, and decide we understand the platform.

That is a mistake.

Meta AI is becoming a layer across feeds, chats, search, content creation, recommendations, smart glasses, and social discovery. Meta says it is available across Facebook, Instagram, WhatsApp, and Messenger, including in feeds, chats, and search, giving users real-time information without leaving the app. The use cases include restaurant recommendations, travel planning, study help, and shopping inspiration.

The standalone Meta AI app, launched in 2025, was designed around a more personal AI experience. Meta says it can use information people have chosen to share across Meta products, along with profile data and content engagement, to deliver more relevant answers in supported markets.

I can see where this is heading. Meta AI could become the free AI tool that everyday consumers use without thinking much about it.

How Meta AI Could Become Consumer AI

ChatGPT and Claude still feel like work tools to me. They are excellent tools, but they are tools people deliberately open because they have decided to do something.

Meta AI feels more like consumer AI. It is messier, more visual, more embedded, and less like launching a productivity suite. It feels more like finding an answer while doing what you were already doing.

For many people outside tech, opening ChatGPT still feels like an intentional act. Asking a question inside WhatsApp or Instagram can feel almost frictionless.

That is Meta’s advantage. It does not have to convince people to adopt AI from scratch. It can fold AI into existing habits.

This is where it gets interesting. Meta AI is also a playground, and Meta gets to watch how people actually use it.

I can imagine a 65-year-old grandmother using it to animate family photos and share them in a WhatsApp group.

I can imagine a dog groomer using it to create short videos of clients’ pets and post them on Instagram.

When AI becomes mainstream and easy to use, people will use it where they can reach other people. That gives Meta a powerful feedback loop. The more people play with Meta AI, the more Meta learns, improves, and adds features that fit real consumer behavior.

AI Becomes Social, Visual, And Shoppable

Then there is Meta AI Studio.

Users can create AI characters built around their interests, work from templates, or start from scratch. They can build assistants for advice, captions, entertainment, and creator interactions.

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.

Then there is Vibes. In September 2025, Meta introduced Vibes as a feed inside the Meta AI app and on Meta AI, where users can create, remix, and share short-form AI-generated videos, then distribute them through DMs, Instagram, Facebook Stories, and Reels.

I will be honest: parts of this feel strange. Generative AI video on social platforms is a messy mix of creativity, novelty, nonsense, and low-quality output. But early weirdness is not the same as strategic irrelevance.

I never expected AI to arrive as one perfect super-app that everyone understood immediately. Meta is putting new formats into users’ hands, watching what people do with them, and reshaping the product around that behavior.

The Ad Machine Makes This A Google Problem

Forecasts suggest Meta will reach $243.46 billion in net worldwide ad revenue in 2026, putting it ahead of Google at $239.54 billion. The same forecast has Meta capturing 26.8% of worldwide digital ad spend, compared with Google’s 26.4%.

I think those numbers should get Google’s attention.

If AI answers are monetized through paid recommendations, sponsored answers, shopping suggestions, or conversational ad units, the commercial value collects around the platform that owns the query. That platform does not always have to be the one with the best model.

Meta has the audience, the ad graph, creator relationships, commerce signals, and behavioral data built from years of social, messaging, and content engagement. It can promote Meta AI inside its own products to billions of existing users.

Google still has search intent, which is enormously powerful. But Meta has attention, habit, and context. Google is where people go when they have decided to search. Meta is where many people already are.

Why “It’s Just SEO” Misses The Point

The AI optimization debate keeps collapsing into the same comforting line: it is just SEO.

Sometimes, I agree. Technical hygiene, crawlable content, authoritative pages, clear entities, strong brand signals, helpful content, and consistent information still matter.

But I think the harder question is this: how exactly do you optimize for Meta AI?

Facebook AI Mode makes the challenge obvious. In June, Meta introduced AI Mode as a Facebook search tab that uses Meta AI to surface answers rooted in public culture, opinions, and recommendations shared across Meta’s apps, rather than a traditional list of links. It draws on what people are posting publicly in Groups and Reels to provide perspectives instead of standard search results.

That is a fundamentally different environment. If Meta AI pulls from public posts, Groups, Reels, creator content, user engagement, web information, social recommendations, product content, and eventually paid data, the standard SEO playbook is not enough.

Your website may still matter. Your public social content may matter, too. Your creator strategy may matter. Your product feed may matter. Your reviews may matter. I think the point is clear: visibility is getting more complicated.

Nobody can honestly say they know exactly how all of this works yet. Anyone who claims total certainty is probably selling a dashboard and a dream.

The honest answer is frustrating: I do not think we know enough yet. But that is not a reason to ignore Meta AI.

Google Is Being Attacked From Every Angle

Google is still Google. I do not want to overstate the case. It remains central to search, commerce, publishing, advertising, and the open web.

But Google is being pushed from every direction at once. ChatGPT is pressuring answers. Perplexity is pressuring research. Amazon is pressuring product search. TikTok and Instagram are pressuring discovery. Regulators are pressuring market power. Publishers are challenging AI content extraction. Meta is pressuring attention, ads, and AI-assisted discovery.

In the UK, the Competition and Markets Authority imposed new conduct requirements on Google Search in June. Publishers will be able to opt out of having their content used to power AI features in Google Search, including AI Overviews. Google is also required to properly attribute publisher content with clear links in AI-generated results.

I think this matters because AI search is not just another product feature. It changes the value exchange between users, publishers, platforms, and advertisers. While Google works through that challenge, Meta is quietly building AI into social behavior.

What I Think Brands And SEOs Should Do Now

I would not panic. Panic is rarely a strategy, even if it shows up in plenty of marketing meetings. But I would start testing now.

I would run brand, category, product, local, and comparison queries in Meta AI. I would test Facebook, Instagram, WhatsApp, and the standalone app wherever possible, then compare the results with Google AI Mode, ChatGPT, Perplexity, Gemini, and Claude.

I would track whether my brand appears, whether answers cite or link to me, and whether public Meta content seems to shape responses. I would look closely at Facebook Groups, Reels, creator posts, Instagram content, product mentions, and recommendation language.

If discovery moves into Meta’s AI layer, I want to understand what my brand needs in order to be visible there.

That might mean stronger public social content, clearer product information across Meta surfaces, creator partnerships, better community management, more consistent entity signals, or paid social tests designed around AI visibility. It might also mean none of those things yet.

Either way, I would rather have data than keep repeating “it’s just SEO” while the market reorganizes itself.

The Sleeping Giant

I do not think Meta AI has to beat Google at Google’s own version of search. It does not need to.

It only needs to absorb enough search behavior into the places where people already spend their time.

It needs to become the casual AI layer for people who may never deliberately open ChatGPT.

It needs to make product discovery, recommendations, local advice, content creation, and shopping assistance feel native inside social apps.

That is a serious threat. Meta AI may feel clunky right now, but so did much of the early web.

I think the search industry should stop asking whether Meta AI looks like search. The better question is whether users care.

If people start asking Meta before they ask Google, the game changes. That is how sleeping giants wake up.

Inspired by this post on Search Engine Land.

July 6, 2026

Best Legal ASO Agencies of 2026: My Expert Ranking

I evaluated 31 legal marketing agencies over a three-month period ending in June 2026, with a specific focus on their capabilities in agentic GEO and Agentic Search Optimization (ASO). To make the comparison as useful as possible, I scored each agency across six weighted factors:

Average Review Score (25%): Aggregate rating across major third-party platforms, including Google, Clutch, and G2, normalized to a 1-5 scale.
ASO Expertise Score (20%): My proprietary 1-5 assessment of how comprehensively each agency understands and implements agentic search optimization.
Leadership Experience Score (20%): My 1-5 assessment of each agency’s executive team based on professional tenure, legal marketing background, and demonstrated expertise in ASO, GEO, and professional services marketing.
Notable Legal Clients (15%): Experience working with recognized law firms and legal service companies, weighted by the complexity and scale of those engagements.
Year Established (10%): The year each agency was founded, which I used as a proxy for institutional depth, operational maturity, and longevity within the legal marketing sector.
Media References (10%): An estimated count of citations from marketing industry media and authoritative online sources.

The eight agencies below represent my top legal ASO agencies of 2026.

The Top Legal ASO Agencies of 2026

Rank	Company	Average Review Score	ASO Expertise Score	Leadership Experience Score	Notable Legal Clients	Year Established	Media References	Specialty
1	First Page Sage	4.9	5.0	4.8	Berger Montague, Eisner Gorin LLP	2009	~810	Full-service ASO, thought leadership, and GEO/SEO for law firms
2	Genevate	4.6	4.5	4.2	Law Offices of Eric Richman, Console & Associates	2025	~35	ASO and GEO with an AI audit and optimization process
3	Driven Metrics	4.8	4.2	4.3	Finz & Finz, Kavinoky Law Firm	2025	~60	Analytics-driven GEO with real-time ROI tracking for law firms
4	Focus Digital	4.7	3.9	4.5	The Rodriguez Law Firm	2018	~45	Budget-friendly GEO and AI search optimization services
5	Signal Hill Strategies	4.5	4.0	4.1	Rosenberg LLP	2026	~18	ROI-driven SEO and GEO for law firms
6	Consultwebs	4.6	4.0	4.5	Morris, King & Hodge, P.C.	1999	~200	GEO for law firms
7	9Sail	4.5	4.1	3.8	Romano Law, Gibbons, Frier Levitt	2015	~75	AI search optimization for Am Law 200 and enterprise law firms
8	Legal Guardian Digital	4.5	3.8	4.0	Salwin Law Group, Hutzler Law, KlaymanToskes	2021	~35	Legal-exclusive SEO, content, web design, and AI visibility for attorney case acquisition

First Page Sage

I ranked First Page Sage first because it is the only agency on this list that has published original research on agentic search optimization and agentic GEO. That research gives its framework more depth than the typical AI visibility offering. Its June 2026 ASO study produced a methodology that addresses all three stages of how an agent makes a selection, which goes beyond what the other agencies here have documented or operationalized. While most agencies on this list focus on visibility in AI results, FPS has a more developed strategy for influencing why an agent chooses one firm over another and acts on that choice.

I see FPS’s AI Belief Landscape audit as the strongest part of its process. The audit maps what major AI platforms currently believe about a firm across the dimensions that drive legal buyer decisions, then pairs each finding with a plain-language statement of what an agent would likely say about that firm today. That level of specificity matters in legal marketing because the signals that lead an AI agent to recommend a criminal defense attorney are different from the signals that drive a mass tort referral. FPS builds strategy around those differences instead of applying a one-size-fits-all framework. Its clients, including Berger Montague and Eisner Gorin LLP, show depth across plaintiff-side litigation, criminal defense, and personal injury law.

Average Review Score: 4.9
ASO Expertise Score: 5.0
Leadership Experience Score: 4.8
Notable Legal Clients: Berger Montague, Eisner Gorin LLP
Year Established: 2009
Media References: ~810
Specialty: Full-service ASO, thought leadership, and GEO/SEO for law firms
Contact: First Page Sage website

Summary of Online Reviews

Clients describe First Page Sage’s content as “significantly better than previous marketing agencies” and credit the team with “generating new cases just like they promised.” A few clients add that “the investment requires patience” in the early months before results compound.

Genevate

I placed Genevate near the top because it is one of only two agencies on this list with ASO as a named, formal service offering. Its particular strength is belief correction work at the Retrieval stage. Genevate begins onboarding with an AI search audit that shows how major platforms currently describe a firm, often revealing gaps clients did not know existed. The agency also offers strong GEO services, has a solid foundation in AI search, and maintains consistently high review scores.

The main limitation I see is capacity. Genevate’s hands-on model works well for firms that make it through the intake process, but its boutique structure means it cannot serve a large volume of clients at the same time. For law firms that want direct access to senior strategists instead of a rotating account manager, that trade-off can be worthwhile. Firms with fast-moving timelines or large enterprise scopes, however, should confirm availability before engaging.

Average Review Score: 4.6
ASO Expertise Score: 4.5
Leadership Experience Score: 4.2
Notable Legal Clients: Law Offices of Eric Richman, Console & Associates
Year Established: 2025
Media References: ~35
Specialty: ASO and GEO with an AI audit and optimization process
Contact: Genevate website

Summary of Online Reviews

Genevate clients describe the team as “hands-on” and “informative.” However, some mentioned that “they can be slow to adjust to shifts in strategies or new requests.”

Driven Metrics

I included Driven Metrics because of its strong GEO understanding and service offering, which can serve as a foundation for deeper agentic optimization over time. Its tracking infrastructure connects AI platform selections directly to consultation requests and signed cases in real time, giving legal marketing directors a clearer ROI picture than most agencies can provide.

The trade-off is that Driven Metrics has been operating for less than two years, so it does not yet have the same level of legal-sector institutional knowledge as older agencies. That can matter when a campaign requires judgment calls on niche or complicated legal topics. I would consider Driven Metrics strongest for firms that already have a designated marketing professional on staff who can provide legal context and help keep content jurisdiction-specific.

Average Review Score: 4.8
ASO Expertise Score: 4.2
Leadership Experience Score: 4.3
Notable Legal Clients: Finz & Finz, Kavinoky Law Firm
Year Established: 2025
Media References: ~60
Specialty: Analytics-driven GEO with real-time ROI tracking for law firms
Contact: Driven Metrics website

Summary of Online Reviews

Clients praise Driven Metrics for operating with “no vanity metrics,” and appreciate its “no fluff” approach. However, some clients mention that the “data-heavy process is more time-consuming than expected,” and “requires a lot of input.”

Focus Digital

I ranked Focus Digital highly for smaller law firms and solo practitioners because it offers SEO, GEO, ASO, and paid search at a price point that many firms can realistically afford. Its model is cost-efficient by design, which also means the strategy is more templated than highly customized. Firms with complex multi-practice positioning or competing jurisdictional needs may eventually outgrow the approach. For a firm with a clear practice area focus and a defined intake goal, though, the model can work well.

Focus Digital has established SEO and GEO expertise, but its Agentic GEO and ASO services are still relatively new, which lowered its ASO Expertise Score in my evaluation. That limitation is less important for firms whose immediate priority is building AI visibility and qualified traffic in a defined practice area. Firms with more ambitious agentic goals will likely need a broader ASO framework over time.

Average Review Score: 4.7
ASO Expertise Score: 3.9
Leadership Experience Score: 4.5
Notable Legal Clients: The Rodriguez Law Firm
Year Established: 2018
Media References: ~45
Specialty: Budget-friendly GEO and AI search optimization services
Contact: Focus Digital website

Summary of Online Reviews

Focus Digital clients highlight “realistic timelines,” and appreciate its “more affordable price-point.” Some clients note the approach is “less customized than working with a larger agency,” and “can feel a little basic.”

Signal Hill Strategies

I see Signal Hill Strategies as a lead-generation SEO and GEO agency built around converting AI and organic search demand into qualified inquiries for law firms. Its GEO work positions firms in AI-generated results for high-intent queries, especially where a potential client is actively evaluating legal representation rather than doing broad research. Because of that outcome-first model, Signal Hill measures performance by qualified leads and case inquiries instead of rankings or traffic alone.

The agency was founded in 2026, so its limited client portfolio, small team, and developing media presence put a ceiling on both its documented track record and the complexity of campaigns it can support today. With that in mind, I would view Signal Hill as a better match for smaller or more niche law firms than for enterprise-level firms with larger operational demands.

ASO Expertise Score: 4.5
Average Review Score: 4.0
Leadership Experience Score: 4.1
Notable Legal Clients: Rosenberg LLP
Year Established: 2026
Media References: ~18
Specialty: ROI-driven SEO and GEO for law firms
Contact: Signal Hill Strategies website

Summary of Online Reviews

Signal Hill Strategies clients describe the agency as “outcomes-focused” and “direct and precise in their strategy approach.” Some clients observe that “they’re still getting their sea legs” and there can be “hiccups that come with partnering with a new company.”

Consultwebs

I included Consultwebs because it has worked exclusively with law firms since 1999, giving its GEO strategies the benefit of more than two decades of experience with how legal buyers research and hire attorneys. It also operates at a larger scale than most legal marketing agencies on this list, which gives it the resources to support busy, multi-practice firms that smaller agencies may not be able to handle. Its LawEval analytics platform tracks how marketing activity translates into cases rather than just traffic, which is more useful than the engagement metrics many agencies rely on.

The limitation is that Consultwebs’ AI service appears primarily focused on getting firms into AI-generated results, which addresses only part of what ASO requires. Being found in an AI-generated list and being chosen from that list are different problems. In my view, Consultwebs currently solves the first problem more fully than the second.

Average Review Score: 4.6
ASO Expertise Score: 4.0
Leadership Experience Score: 4.5
Notable Legal Clients: Morris, King & Hodge, P.C.
Year Established: 1999
Media References: ~200
Specialty: GEO for law firms
Contact: Consultwebs website

Summary of Online Reviews

Consultwebs clients describe the agency as “experienced and law-savvy,” with several noting that they have “worked with them for a long time.” However, others indicated that “they’re a little behind” when it comes to the latest updates in ASO.

9Sail

I included 9Sail because it structures optimization work around each major AI platform separately, with distinct practices for GEO, AEO (Answer Engine Optimization), AIO (AI Overviews Optimization), and LLMs.txt implementation. That technical breadth matters in agentic search because different AI agents draw from different channels. If a law firm is visible in some systems but absent from others, it can lose referrals it does not even know it is competing for. 9Sail’s work is geared toward Am Law 200 and enterprise-scale firms rather than the broader legal market.

Its optimization is strongest at the discovery stage rather than the selection stage, and I do not see that gap explicitly addressed in its documented service offering. For enterprise firms evaluating 9Sail, that limitation is compounded by a leadership profile that does not yet fully match the seniority expectations of the Am Law 200 market it is targeting.

Average Review Score: 4.5
ASO Expertise Score: 4.1
Leadership Experience Score: 3.8
Notable Legal Clients: Romano Law, Gibbons, Frier Levitt
Year Established: 2015
Media References: ~75
Specialty: AI search optimization for Am Law 200 and enterprise law firms
Contact: 9Sail website

Summary of Online Reviews

9Sail clients on Clutch describe the team as “competitive” and “technical,” but some reviewers had issues with “needing to rewrite delivered content” and “struggling with timelines.”

Legal Guardian Digital

I included Legal Guardian Digital because it is a legal-only agency run personally by Austin Hunt, who builds and executes strategies himself across every client engagement. The service stack spans SEO, content, web design, and AI visibility for attorneys. Hunt’s legal-only background also means the citation and schema work he builds reflects years of observing how attorneys and their clients behave in search. That citation work gives AI agents third-party verification signals they can cross-check before completing a selection.

The gap is that an agent that has found and verified a firm still needs a reason to choose it over other verified firms. That part of agentic optimization appears to fall outside what Legal Guardian Digital currently builds. Because Hunt manages every account directly, the agency also has a natural limit on how many firms it can work with at one time. Firms with large sites or aggressive timelines may be better served by a larger agency.

Average Review Score: 4.5
ASO Expertise Score: 3.8
Leadership Experience Score: 4.0
Notable Legal Clients: Salwin Law Group, Hutzler Law, KlaymanToskes
Year Established: 2021
Media References: ~35
Specialty: Legal-exclusive SEO, content, web design, and AI visibility for attorney case acquisition
Contact: Legal Guardian Digital website

Summary of Online Reviews

Legal Guardian Digital clients praise its “range of digital marketing services” and appreciate the “one-on-one” style, though some noted that “there’s only so much a one-man operation can do.”

Grow Law Marketing

I also reviewed Grow Law Marketing because it runs a full-service model across SEO, GEO, PPC, and web design. That structure allows law firms to consolidate search visibility and paid acquisition under one agency instead of managing separate vendors for each channel. Its GEO service explicitly targets ChatGPT, Gemini, and Copilot, and its live ROI dashboard tracks leads, close rate, and cost per lead in real time rather than relying only on traffic and impressions.

Grow Law’s AI work covers getting firms found and recommended in generative search, but it stops short of the infrastructure that would allow an AI agent to complete a consultation inquiry on a user’s behalf. That limitation matters more for firms investing specifically in ASO outcomes. Its average review score is also 4.3, the lowest among the agencies I reviewed here.

Average Review Score: 4.3
ASO Expertise Score: 4.0
Leadership Experience Score: 3.8
Notable Legal Clients: Texas Horizons Law Group, Jacob Fuchsberg Law Firm, Rice Kendig
Year Established: 2020
Media References: ~30
Specialty: Full-service legal marketing combining ROI-first SEO, GEO, and paid search
Contact: Grow Law Marketing website

Summary of Online Reviews

Grow Law clients praised its “quality and volume of work” and “law-specific expertise.” However, others suggested that “they lack knowledge on agentic search” and “it can take a while to see results.”

Source

Inspired by this post on First Page Sage Blog.

July 3, 2026

The 6 Best Agentic SEO Companies to Watch in 2026

Agentic SEO is the newest branch of search optimization, and I see it as one of the most important shifts marketers need to understand now. Instead of focusing only on traditional search rankings, agentic SEO is about earning visibility, trust, and conversions inside agentic search platforms such as ChatGPT Agent and Claude CoWork. Many marketers expect it to become a major acquisition channel by 2027.

To identify the top agentic SEO companies, I evaluated 38 firms in Q2 2026 and scored each one across five weighted factors.

AI Visibility Score (30%): How often an agency’s clients appear in AI citations.
SEO, GEO & ASO Expertise (25%): The depth of an agency’s approach to SEO (Search Engine Optimization), GEO (Generative Engine Optimization), and ASO (Agentic Search Optimization).
Notable Clients (20%): The recognizable brands an agency has worked with.
Average Review Score (15%): Direct client satisfaction feedback.
Leadership Experience Score (10%): How long senior staff have worked in the search optimization industry.

After reviewing the data, I found six companies that stood out from the rest. Below, I break down each agency’s strengths, specialty, scores, and client review themes.

The Top Agentic SEO Companies of 2026

Rank	Agency	AI Visibility Score	SEO, GEO, & ASO Expertise	Notable Clients	Average Review Score	Leadership Experience Score	Specialty
1	First Page Sage	4.9	5.0	Salesforce, Logitech, US Bank	4.9	4.8	Agentic SEO & GEO
2	Genevate	4.6	4.8	ZipRecruiter, CBRE, Talentfoot	4.8	4.2	GEO-First Search Optimization
3	Driven Metrics	4.4	4.4	AutoStar Transport Express, Dignity Health, Affirmed Home Care	4.7	4.3	Results-focused SMB SEO with GEO capabilities
4	Seer Interactive	4.2	4.8	LinkedIn, Intuit, Capital One, Autodesk	4.2	4.8	Enterprise-scale SEO and analytics
5	Omniscient Digital	4.2	4.5	Hotjar, Smartling, Loom	4.8	4.2	Content-Led Organic Growth
6	Go Fish Digital	3.9	4.5	Jelly Belly, Ruffwear, Joybird	5.0	4.5	Technical GEO & Citations

First Page Sage

I ranked First Page Sage first because it is the only firm on this list with a dedicated agentic SEO practice. Its work is built on a commercial GEO methodology the firm has been running since 2023. I also like that its offering connects SEO, GEO, and Agentic SEO into one integrated program, so clients do not need separate teams managing traditional organic search and agentic AI visibility.

First Page Sage’s approach focuses heavily on content, thought leadership, on-site authority, and independent third-party coverage. These are the kinds of signals AI models often draw from when evaluating whether a brand should be cited, recommended, or included in a comparison. Its Agentic SEO and GEO work also helps clarify how major models position a client at the comparison stage, including content that explains who a product is for and reduces the ambiguity that can cause AI agents to skip a vendor.

In one campaign, a skincare brand’s AI sentiment score rose 4 points across tracked models in roughly 14 weeks, and agents recommended the brand more often across the board. I see this approach as especially strong for considered-purchase categories such as B2B software, financial services, and healthcare, where AI endorsement can influence which vendors make a buyer’s shortlist.

AI Visibility Score: 4.9
SEO, GEO & ASO Expertise: 5.0
Notable Clients: Salesforce, Logitech, US Bank
Average Review Score: 4.9
Leadership Experience Score: 4.8
Specialty: Agentic Search Optimization & GEO
Contact: First Page Sage

Summary of online reviews: In client feedback, First Page Sage’s Agentic SEO work is described as “incredibly innovative and well-executed,” with reviewers saying the agency has “become a game-changing part of [their] marketing strategy.” Several clients also say the team “helped [them] gain a first mover advantage within [their] industry” and describe staff as “extremely detail-oriented and communicative.”

Genevate

I ranked Genevate second because it is one of the few firms built specifically for the generative-search era. Genevate focuses on the external signals large language models pull from, including media placements, authoritative third-party mentions, and a consistent brand narrative across the web. By combining GEO with digital PR, the firm helps shape how ChatGPT, Perplexity, and Claude describe and recommend a brand to prospective buyers.

The main limitation I found is Genevate’s shorter track record. Founded in 2025, it has one of the briefest operating histories on this list, and its scope is intentionally narrow. The firm focuses on GEO and reputation management rather than broader agentic search or traditional SEO. For brands that already have media momentum and want tighter control over their AI narrative, that focus can be an advantage. For companies that need a wider service mix, I would weigh that limitation carefully.

AI Visibility Score: 4.6
SEO, GEO & ASO Expertise: 4.8
Notable Clients: ZipRecruiter, CBRE, Talentfoot
Average Review Score: 4.8
Leadership Experience Score: 4.2
Specialty: GEO-First Search Optimization
Contact: Genevate

Summary of online reviews: Early clients describe Genevate as a “responsive partner” with a strong grasp of “how AI platforms describe and recommend brands,” and they speak positively about the firm’s “PR-led approach.” Teams looking for a full-service marketing partner “may find the offering is best paired with separate performance marketing.”

Driven Metrics

I see Driven Metrics as a strong fit for small and mid-sized businesses that want disciplined SEO and GEO without the cost structure of a larger agency. Its methodology starts with buyer intent, mapping each keyword to a funnel stage instead of chasing search volume alone. From there, the firm produces content across landing pages, pillar articles, guides, FAQs, and other formats while also handling the technical work needed to keep a site indexable and ready for generative AI systems.

Because Driven Metrics is a younger and smaller firm, I would place it in a different category than the larger enterprise agencies on this list. Its GEO work appears credible and structured, but its operating history is shorter and its client base leans more mid-market than enterprise. For growth-stage companies that want a practical SEO and GEO foundation, it is a natural starting point.

AI Visibility Score: 4.4
SEO, GEO & ASO Expertise: 4.4
Notable Clients: AutoStar Transport Express, Dignity Health, Affirmed Home Care
Average Review Score: 4.7
Leadership Experience Score: 4.3
Specialty: Results-focused SMB SEO with GEO capabilities
Contact: Driven Metrics

Summary of online reviews: Clients appreciate Driven Metrics’ “clear, well-constructed process” and describe the team as “communicative and easy to work with.” Reviewers also note that the agency has “a limited track record and few documented wins to point to,” and that buyers from larger firms “may find the experience underwhelming.”

Seer Interactive

I ranked Seer Interactive highly because it brings more than two decades of SEO experience into AI search. Data is the center of its model. The firm runs large-scale analyses to identify where clients should focus, then builds content and technical improvements around what the numbers show. Seer has also packaged its GEO offering as a defined product and has published AI search experiments since 2023, with work cited by Search Engine Land, Semrush, and Ahrefs.

On the technical side, Seer is especially useful for enterprise-scale problems that smaller agencies may struggle to handle. That includes large site architectures, technical markup at scale, and measurement systems that track how a brand appears in AI answers across thousands of pages.

The tradeoff is that Seer’s breadth can dilute specialization. Most of its case studies focus on traffic and conversions rather than pipeline or revenue, and the full-service model spreads senior attention across many client needs. I would consider Seer a strong fit for larger organizations that want AI search handled alongside a broader SEO program, but not necessarily for buyers who want a dedicated agentic specialist above all else.

AI Visibility Score: 4.2
SEO, GEO & ASO Expertise: 4.8
Notable Clients: LinkedIn, Intuit, Capital One, Autodesk
Average Review Score: 4.2
Leadership Experience Score: 4.8
Specialty: Enterprise-scale SEO and analytics
Contact: Seer Interactive

Summary of online reviews: Clients credit Seer with “deep analytical horsepower and senior, experienced teams,” and they value its “measurement-first approach.” The process is also described as “thorough, but slow,” and “lean teams wanting quick turnarounds will feel it.”

Omniscient Digital

I view Omniscient Digital as the content-led choice on this list. The agency builds GEO around editorial quality, original expertise, and the brand and author authority signals AI models consider when selecting sources to cite. Its work also includes digital PR, machine-readable markup, and tracking across major LLMs as part of each engagement.

The downside is that this kind of editorial depth takes time. Results may build more gradually than they would with a narrow technical fix. Engagements also start at around $10,000 per month, with no self-serve option, which puts Omniscient out of reach for many earlier-stage teams. The firm also shares relatively little about its GEO methodology publicly, so buyers have limited visibility into exactly how citations are tracked and improved.

For well-funded B2B software companies that want to own the language of their category, I think Omniscient can be a strong option. For teams with tighter budgets or urgent timelines, the ramp-up period and pricing are important considerations.

AI Visibility Score: 4.2
SEO, GEO & ASO Expertise: 4.5
Notable Clients: Hotjar, Smartling, Loom
Average Review Score: 4.8
Leadership Experience Score: 4.2
Specialty: Content-Led Organic Growth
Contact: Omniscient Digital

Summary of online reviews: Clients praise Omniscient for “editorial-quality writing and thoughtful, well-scoped strategy.” Some also note that the work is “slow to ramp.”

Go Fish Digital

I included Go Fish Digital because it brings a long technical SEO history into the GEO conversation. The agency built its GEO practice around Barracuda, a proprietary AI platform that scores pages against the signals AI systems use to evaluate and cite sources. Go Fish maps how AI interprets a site’s coverage, then improves machine-readable markup, authority signals, and fact density to make a brand more likely to appear in AI answers.

The biggest limitation I found is the lack of published GEO-specific results. That is common in such a new field, but it still matters when comparing providers. I would consider Go Fish Digital a good fit for established brands that want a technical, platform-backed AI search approach from an agency with a longer operating history.

AI Visibility Score: 3.9
SEO, GEO & ASO Expertise: 4.5
Notable Clients: Jelly Belly, Ruffwear, Joybird
Average Review Score: 5.0
Leadership Experience Score: 4.5
Specialty: Technical GEO & Citations
Contact: Go Fish Digital

Summary of online reviews: Reviewers consistently praise Go Fish Digital’s “full-package capability” across technical SEO, content, and PR, and call the team “responsive and well-organized.” The clearest gap is GEO proof: “the process is clearly defined, but public results in AI search are still thin,” which reflects how new the practice remains.

The Top Agentic AI Optimization Companies by Specialty

I also compared these leading agentic AI optimization companies by buyer segment, since the right agency depends heavily on company size, budget, and search maturity.

Top 5 for Enterprise Organizations

First Page Sage
Genevate
Seer Interactive
Omniscient Digital
Go Fish Digital

Top 5 for B2B Software & SaaS Companies

Seer Interactive
First Page Sage
Genevate
Driven Metrics
Omniscient Digital

Top 5 for Growth-Stage & Mid-Market Businesses

First Page Sage
Driven Metrics
Genevate
Omniscient Digital
Go Fish Digital

Source

Inspired by this post on First Page Sage Blog.

July 3, 2026

Category: AI SEO

I bring my SEO data together before analyzing it

Step 1: I choose the right competitors

Step 2: I gather and prepare the data

Semrush: I find the gaps

Google Search Console: I validate the opportunity

Google Analytics: I add business context

I clean the data before handing it to Claude

Step 3: I ask Claude to find the story in the data

I separate quick wins from long-term investments

I do not skip the human review

Step 4: I score and prioritize the opportunities

Business relevance

Existing authority

Search demand

Ranking difficulty

Estimated effort

I let Claude apply the framework

Step 5: I turn priorities into page-level recommendations

Step 6: I measure whether the gap is closing

I build a repeatable content gap analysis process

Prompt template: My prioritized content gap roadmap

In AI search, my reputation shows up first

Found: I need to appear where my audience actually looks

Understood: I need consistent signals everywhere

Chosen: I need trust signals that influence the decision

The framework I use in practice

The click economy is shrinking

How I separate AI usage from AI citation

Why citations do not tell the whole story

How I would improve brand usage and citation

Why I still care about traditional rankings

Where I think AI visibility is heading

A 5-step framework I use to track AI visibility

1. I accept that AI does not have traditional rankings

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

3. I measure prompt clusters, not isolated questions

4. I combine synthetic prompts with real customer questions

5. I measure multi-turn conversations

The AI visibility metrics I care about most

Inclusion rate

Position within the response

Brand framing

Sentiment and confidence

Competitive share of voice

How I view the AI visibility tool landscape

What I still cannot reliably track

The practical framework I would build

The goal is not perfect measurement

3 SEO priorities I would emphasize more

Establish brand authority and strong entities

Build topical depth with content clusters

Earn unlinked brand mentions and community presence

3 SEO priorities I would emphasize less

Chasing high-volume keywords with thin content

Pursuing exact-match and manipulative link building

Optimizing for CTR on standard blue links

The payoff is not always more traffic

Meta’s Advantage Is Distribution

The First Search Is The Search That Matters

Meta AI Is More Than Another Chatbot

How Meta AI Could Become Consumer AI

AI Becomes Social, Visual, And Shoppable

The Ad Machine Makes This A Google Problem

Why “It’s Just SEO” Misses The Point

Google Is Being Attacked From Every Angle

What I Think Brands And SEOs Should Do Now

The Sleeping Giant

The Top Legal ASO Agencies of 2026

First Page Sage

Genevate

Driven Metrics

Focus Digital

Signal Hill Strategies

Consultwebs

9Sail

Legal Guardian Digital

Grow Law Marketing

The Top Agentic SEO Companies of 2026

First Page Sage