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.
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.
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.
A year ago, I watched the industry place its bets on which AI platform would own discovery. Perplexity looked like the search-native challenger. Copilot looked like the enterprise Trojan horse. In the data I’m seeing now, neither bet has really paid off.
Previsible (disclosure: I’m its CPO and co-founder) just published its third AI Traffic Study, based on 6.77 million LLM-driven sessions. What stands out to me is the level of consolidation. Monthly LLM sessions grew 9.9x, reaching 644,478 in May 2026, and 92.4% of that traffic came from one platform.
The plateau was a pause
In mid-2025, it looked like AI traffic might be topping out in some sectors. I don’t think that was the real story.
Sessions climbed from 65,249 in November 2024 to 396,278 by August 2025. Then they dropped sharply in November 2025 before reaching new highs of 428,203 in February 2026 and 644,478 in May.
That November dip deserves context.
Sessions fell 50% in a single month, driven almost entirely by ChatGPT referrals dropping from 448,412 to 213,345. Other platforms were mostly steady. To me, that points to a model-related change. We’ve already seen small product shifts create major swings in referral traffic, including last fall, when many sites lost half their ChatGPT traffic because the model began favoring Wikipedia and Reddit. By December, sessions had recovered to 442,609.
The lesson I take from this is simple: one vendor’s product decision can cut your AI traffic in half overnight. I would plan for that volatility instead of treating AI referrals as a stable channel.
Consolidation, not competition
When we last published in December 2025, ChatGPT held about 84% share. Perplexity followed at 8.9%, Gemini at 4.5%, Copilot at 2.1%, and Claude at 0.6%. Six months later, the field had moved even more decisively toward the leader.
Across the full dataset, ChatGPT now commands 92.4% of trackable LLM referral traffic. It grew 12.8x over 19 months, with no clear sign of slowing. It is the only LLM sending meaningful referral volume at scale, which means I would not talk about “AI visibility” without putting ChatGPT first.
There is one important caveat. This study measures standalone LLM referral traffic. AI discovery inside Google’s own results, including AI Overviews, almost certainly drives more AI traffic than all standalone platforms combined. But that operates under a different measurement model, so it is not included here.
The challengers flipped
The surprise is not that ChatGPT is on top. What I find more interesting is the movement beneath it.
Claude
Claude grew 64x, moving from 133 sessions in November 2024 to 8,528 in May 2026. It overtook Perplexity in March 2026 for the first time, and it stayed ahead.
Claude was mostly flat through 2025, then accelerated 4x in two months as its agentic tools and enterprise integrations gained adoption. The enterprise advantage many people expected Copilot to win may be materializing for Claude instead.
If your audience includes technical buyers, developers, or professional services, I would treat Claude visibility as material now. The early positioning window is still open, but it may not stay that way for long.
Gemini
Gemini is the quiet number two in this dataset. It delivered 3.2x growth with very little volatility. Because Gemini is tied into Workspace and Android, I suspect referral numbers undercount its real discovery footprint.
Perplexity & Copilot
Perplexity peaked at 17,507 monthly sessions in March 2025 and has fallen 61% since. Copilot fell even harder, dropping 96% from its August 2025 peak, from 8,651 sessions to 339.
I no longer see either platform as a strong traffic-acquisition growth bet. Both are shifting toward experiences that keep users inside their own environments, including browsers, agents, and modes where they do not need to send traffic out at all.
Where LLMs send users, and why it should change your roadmap
The most actionable finding in the study is not market share. It is where LLMs send people after they decide a site is worth visiting.
ChatGPT sends 28.8% of its traffic to internal search results pages. Across industries, roughly 25% of AI-referred traffic lands on internal search.
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.
My read is that the model trusts the domain but cannot always identify the exact right page. So it sends users to the site’s search box and lets them navigate from there. Because this pattern holds across verticals and time periods, I see it as structural to retrieval-augmented generation rather than a temporary quirk.
That changes the role of internal search. The model already did the hard work of choosing your domain. Now your internal search experience decides whether that high-intent visit converts or bounces.
For most sites, internal search is still treated like a neglected navigation feature. I think it needs to be treated as an acquisition surface.
The vertical-level data tells several different stories. SaaS traffic lands on search pages 34.6% of the time. Publisher traffic lands on news pages 54% of the time, but against 120+ million organic sessions, publisher penetration is only 0.11%. Publishers create the content LLMs cite, yet they capture almost none of the resulting traffic.
Ecommerce traffic tends to land on product pages, often with purchase intent already formed. Education traffic lands directly on course pages 52% of the time, bypassing marketing content. Health traffic lands on About pages 42.1% of the time, suggesting users are evaluating the source before trusting the content. Legal traffic spreads across blog, about, contact, and location pages, which reflects the full evaluation arc.
The platforms have distinct behaviors, too. ChatGPT and Gemini act more like search-pattern models: they show domain trust but page-level uncertainty. Perplexity and Claude behave more like content-selection models, picking specific pages and over-indexing on long-form content.
If your strategy depends on editorial content driving qualified traffic, I would give Perplexity and Claude more attention than their raw share suggests.
What I would do now
First, I would optimize for ChatGPT before anything else and expand to other platforms only when the volume justifies the work. ChatGPT is where the measurable standalone LLM referral traffic is concentrated.
Second, I would monitor Claude closely. It overtook Perplexity in March 2026, and early visibility advantages can compound quickly when a platform is still forming its citation and recommendation patterns.
Third, I would treat product pages as AI entry points. Product pages capture 43% of ecommerce LLM traffic, which makes structured, comparable product data a discoverability requirement rather than a nice-to-have.
Fourth, I would make pricing machine-readable wherever possible. “Contact us for pricing” gives AI systems very little to summarize, compare, or recommend.
Fifth, I would prioritize internal search. It is not just a navigation feature anymore. For AI-referred users, it may be the first real conversion point.
Finally, I would track AI traffic by page type instead of relying only on site-wide averages. Your overall AI traffic number can hide where the real concentration is. A pricing page, for example, might run 3x your site-wide penetration.
The next question I want answered is conversion rate by LLM platform. Which platforms send users who buy, and which send users who bounce?
We built this dataset to answer that. If the last 19 months are any guide, I expect the answers to change faster than most teams are prepared for.
About the data
This analysis includes 166 GA4 properties from November 2024 through May 2026, spanning SaaS, ecommerce, finance, legal, health, insurance, education, publishing, and ticketing. All 166 properties are present throughout the full 19-month window, so I’m looking at behavioral change rather than sample expansion.
I am seeing OpenAI roll out a new feature that lets ChatGPT Ads generate ads for advertisers, and I suspect AI is doing the heavy lifting behind it. The option appears under “Add new ad” and includes a prompt to “generate ads for you.”
From there, I can choose to let ChatGPT create the ad, then review it, edit it, and approve it before it goes live on the ChatGPT Ads platform.
ChatGPT Ads Manager preview highlights OpenAI's generated ad workflow, where marketers can review an AI-created variation before activating it for a campaign.
What it looks like. Anthony Higman posted a screenshot of the feature on X, showing how the ad creation flow appears inside the platform.
A ChatGPT Ads dropdown highlights the quick Duplicate Ad option, pointing marketers to a faster way to copy an existing ad for review, edits, and reuse.
In the screenshot, the interface says, “We generated an ad variation based on your website and campaign settings. Review, edit as needed, and activate when you’re ready.” I can then move forward by selecting “Review and create.”
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 also noticed that Higman spotted a quick duplicate ad option, which could make it easier to create variations faster.
Why I care. It makes sense to me that OpenAI would use AI to help advertisers create ads more quickly. If the tool reduces friction, it could lead to more ads being created, submitted, and activated on ChatGPT Ads, which would also help OpenAI generate more revenue from ChatGPT.
As a marketer, I would still be careful with AI-generated ads. I would review every version closely to make sure the messaging fits the brand, supports the campaign strategy, and aligns with performance goals, including ROI.
I see advanced architecture as much more than a technical framework now. It shapes whether my content can be found, understood, and surfaced by search engines and AI systems.
That is why I am paying close attention to the next SMX Now on July 15, featuring Shari Thurow, co-founder, information scientist, and search director at the Information Architecture Gateway. She will explain how advanced architecture really works and where many AI, SEO, and site development workflows tend to fall short.
In this session, I will explore a five-phase framework Thurow has tested through decades of client work with organizations including Microsoft, Google Cloud, Abbott Laboratories, CVS Pharmacy, WebMD, Sony Music, the Library of Congress, Best Buy, and Merriam-Webster. I will learn how architecture decisions influence labeling systems, wayfinding networks, taxonomy, wireframes, and AI access to valuable content.
I also expect the session to challenge some long-standing assumptions, including the three-click rule, the idea that taxonomy is only a hierarchy, and the belief that AI can create effective wireframes without a deeper architectural model behind them.
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.
By the end, I will have a practical framework for building sites that communicate more clearly with users, search engines, and human-centered AI systems.
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.
I believe the lines between paid media, PR, and SEO have officially disappeared.
When I look at baked-in YouTube sponsorships, native UGC, and third-party review incentives, I no longer see them as separate from SEO. I see them as the modern equivalent of buying a high-DA backlink. When I fund these channels, I am investing in the information sources that shape how AI systems understand, evaluate, and recommend a brand.
A recent social media screenshot made this shift especially clear to me. A B2B brand was offering a $250 Amazon voucher to anyone who wrote a review on G2.
To a growth marketer, that may look like a familiar user acquisition tactic. But as an SEO, I saw something more important: a direct investment in the semantic infrastructure AI systems use to judge brands.
The evolution of the authority signal
To understand why I consider a $250 G2 voucher or a paid YouTube sponsorship an SEO strategy, I have to look at how LLMs now define authority.
Authority used to feel transactional and mathematical. You built or bought hyperlinks, and those links helped determine how trusted a page or brand appeared to search engines.
When I moved from link building into digital PR and influencer marketing, I realized Google was getting smarter. I could not rely on links alone. I needed unlinked brand mentions, high-tier media coverage, and contextual relevance. In many ways, I was optimizing for Google’s Knowledge Graph.
Today, retrieval-augmented generation (RAG) systems and LLMs do not just count links or parse knowledge graphs. They look for semantic consensus across the web.
When an AI engine like Perplexity or ChatGPT answers a user query, it crawls the data ecosystems it trusts most for that specific topic. For software, that often means G2 and Reddit. For consumer products, it may mean TikTok transcripts, YouTube, and forums.
So when I pay $250 for a G2 review, I am buying a dense, text-based data point that an LLM can use to understand my brand’s sentiment, use cases, and vector positioning. I am strengthening the signals AI systems may use when deciding whether to recommend my brand.
The permanent ad: Why sponsorships and UGC are the new organic infrastructure
This reality breaks the traditional separation between paid media and SEO.
The path to AI search visibility now runs beyond links: from PageRank and PR mentions to consistent brand signals across the datasets LLMs rely on.
Historically, paid ads were temporary. I turned off the budget, the traffic stopped, and SEO had to carry the long-term work. If I run a dynamic programmatic ad on YouTube or a banner ad on a website, that old model still applies because LLM web scrapers generally ignore dynamic ad placements.
But baked-in influencer sponsorships, native user-generated content, and podcast reads behave differently because they become part of the content itself.
First, there is the hardcoded transcript. When a YouTuber reads a native sponsor segment such as, “I use Brand X to manage my business taxes,” that message is baked into the video file, and YouTube automatically transcribes it.
Then comes LLM ingestion. When an LLM crawls the web, or when a multimodal AI watches the video, those spoken words can be indexed. The AI can associate the brand with the semantic concept of business taxes.
That creates a new half-life for paid media. Long after the campaign ends and the initial views slow down, the transcript can remain part of the information an LLM can access.
As someone who spent years bridging the gap between digital PR and SEO, I used to judge a campaign’s ROI by immediate referral traffic, brand search lift, and backlink quality. Now, I also have to think about the algorithmic half-life of my creative assets.
Activating the convincer: Bringing paid and PR into the visibility supply chain
The visibility supply chain treats content like an industrial product that passes through strict organizational “gates” before it enters the digital ecosystem. In that model, companies need a strategic duo: the hacker, or technical architect, and the convincer, or cross-departmental visibility advocate.
This convergence of paid media and AI visibility is exactly where I believe the convincer has to step in.
If my paid media team is buying YouTube sponsorships based only on demographic reach, or if my product marketing team is buying G2 reviews just to hit a quarterly quota, we may be damaging LLM visibility without realizing it.
The reason is simple: LLMs need information density and semantic alignment.
If a user writes a rushed, generic review like “Great tool, highly recommend!” just to receive a $250 voucher, it may pass the human layer, but it fails the machine layer. To a RAG system, that sentence is low-density noise.
Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.
The convincer’s job is to realign the review strategy and help internal teams understand how every initiative can build LLM visibility.
For example, I would rather incentivize users to write detailed, context-rich problem-and-solution statements, such as: “We used Brand X to solve our cross-border compliance issues in Europe.” That gives AI the entity-relationship mapping it needs to recommend the brand for cross-border compliance.
The new marketing playbook: Optimizing dataset partnerships
If I want a brand to be recommended by AI systems, I have to study where the major AI players are getting their data.
We know OpenAI and Google have struck multimillion-dollar deals to train on Reddit’s real-time firehose. We know Grok trains on X. We also know Apple and others are licensing major journalistic archives.
That means target audience research is no longer just about finding where customers spend time. For me, it is also about dataset matching.
If I am planning an influencer campaign, a digital PR push, or a community-building initiative, I need to ask one critical question: Is this content entering a data pipeline that the primary LLMs trust and crawl in real time?
Stop optimizing pages. Start optimizing budgets.
I no longer believe SEO can be isolated inside a technical department or limited to a content blog. That does not reflect how AI visibility is built anymore.
The next time I sit in a budget allocation meeting and see a line item for influencer marketing, podcast sponsorships, or third-party review incentives, I will not treat it as temporary media buying.
I will reframe it as infrastructure. I am building the digital foundation of a brand’s AI persona. I am buying the AI equivalent of backlinks. If I do not intentionally structure those paid assets to feed the visibility system, I am leaving the brand’s future visibility up to chance.
[Boston, MA, July 6, 2026] — I am sharing that Traffic Think Tank has officially joined the Search Engine Land family, creating more opportunities for search marketers like us to connect, collaborate, and keep learning through one of the industry’s most established professional communities.
I want members to know that Traffic Think Tank will continue operating as a private Slack community. It will remain a trusted place where we can exchange ideas, validate strategies, solve real marketing challenges, and stay current on search engine optimization, paid media, artificial intelligence, and related marketing topics.
As part of this relationship, I see Search Engine Land supporting the community’s continued growth by increasing visibility across its editorial and marketing channels while preserving the collaborative environment members already value.
“For years, Search Engine Land has represented the marketing community through its contributor network in a way few other sites have,” said Kyle Morley, Head of Sales and Marketing at Third Door Media, parent to Search Engine Land. “Launching a community like Traffic Think Tank feels like a natural extension of our identity, and I’m thrilled we now have more opportunity to connect with marketers in our space.”
I am also noting that David Broderick has been appointed Lead Community Manager and will oversee the day-to-day community experience. He will be supported by Liz Dougherty, who will take an active role in encouraging member engagement and helping guide the community’s continued growth.
Beyond ongoing peer-to-peer discussions, I expect members to benefit from expanded community programming and discussions, increased visibility through Search Engine Land and Third Door Media channels, exclusive discounts on Search Marketing Expo events and training, and new opportunities to connect with search marketers across the industry.
For me, Traffic Think Tank fits naturally with Search Engine Land’s mission of helping marketers stay informed and succeed in a rapidly evolving search landscape. Together, the publication and community give us access to trusted journalism, practical education, live events, and an active peer network for ongoing professional development.
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 view Search Engine Land as a leading publication for news, insights, and education covering search engine optimization, paid media, artificial intelligence, and digital marketing. Through editorial coverage, events, training, and professional resources, Search Engine Land helps marketers stay ahead of industry change.
About Traffic Think Tank
I see Traffic Think Tank as a private community for search marketers that connects professionals through expert discussions, peer collaboration, and practical knowledge sharing. Members use the community to exchange ideas, solve challenges, validate strategies, and stay current on what’s working across search engine optimization, paid media, and artificial intelligence.
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.
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.
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
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
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
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.
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.
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
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
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
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.”
