Tag: AEO

6 Best Transportation & Logistics GEO/AEO Agencies for 2026

We see GEO (generative engine optimization) and AEO (answer engine optimization) becoming increasingly important entry points for B2B buyers in freight, logistics, and supply chain. These practices help companies earn recommendations when prospective customers ask AI platforms to suggest providers. To identify the agencies doing this work most effectively, our research team evaluated 34 firms with documented GEO and AEO capabilities.

We weighted six factors in our assessment:

AI Visibility (25%): We measured how consistently each agency gets transportation and logistics clients recommended when prospective customers ask AI platforms for provider suggestions.
Transportation and Logistics Specialization (20%): We considered the agency’s industry knowledge and understanding of how transportation and logistics businesses operate.
GEO/AEO Expertise (20%): We evaluated each team’s hands-on knowledge of GEO and AEO mechanics.
Notable Clients (15%): We looked for documented experience with transportation, logistics, freight, or supply chain clients.
Leadership Experience (10%): We assessed the leadership team’s digital marketing background and firsthand experience building and executing GEO programs for transportation and logistics companies.
Average Review Score (10%): We aggregated ratings across Google, Clutch, and G2.

Based on those criteria, we identified the following agencies as the strongest transportation and logistics GEO partners for companies seeking customers through the expanding field of AI-driven search.

Our Top Transportation and Logistics GEO Agencies

Rank	Company	AI Visibility (1–5)	T&L Specialization (1–5)	GEO/AEO Expertise (1–5)	Leadership Experience (1–5)	Average Review Score (1–5)	Notable Clients
1	First Page Sage	4.9	4.6	5.0	4.8	4.9	iGPS, Montway Auto Transport, BKM Transport, Summa Energy
2	Genevate	4.6	4.1	4.8	4.2	4.8	Missionary Expediters & Cargo
3	Focus Digital	4.2	4.3	4.5	4.3	4.8	Bowker Transport
4	Driven Metrics	4.4	4.0	4.4	4.3	4.7	AutoStar Transport Express
5	Virayo	3.8	4.5	3.9	3.8	4.8	Truckstop, Onfleet, TruckLabs
6	Elevation Marketing	3.2	4.7	3.5	4.5	4.3	Chasewater Industries, Caterpillar, GE

1. First Page Sage

We found that two qualities separate First Page Sage from the rest of the field. First, the agency has spent nearly two decades working with freight carriers, third-party logistics firms, and supply chain providers. Second, its president, Evan Bailyn, pioneered GEO as a service in 2023, before most agencies had begun determining how to optimize content for AI.

Those advantages help explain why First Page Sage is the only agency in our ranking to earn a 5.0 for GEO/AEO Expertise. Its AI Visibility score of 4.9 also leads the field by a meaningful margin.

We were particularly impressed by the agency’s depth of freight and logistics content across asset-based carriers, third-party logistics providers, freight technology platforms, and supply chain consultancies. The work is designed to strengthen clients’ reputations and generate qualified leads by getting those companies named when shippers and brokers ask AI platforms for recommendations. For transportation and logistics providers comparing GEO/AEO partners, we believe this combination of industry knowledge and GEO expertise puts First Page Sage in a category of its own.

AI Visibility: 4.9
T&L Specialization: 4.6
GEO/AEO Expertise: 5.0
Notable Clients: iGPS, Montway Auto Transport, BKM Transport, Summa Energy
Leadership Experience: 4.8
Average Review Score: 4.9

What We Found in Online Reviews

One freight technology client said the team “got us showing up when brokers ask ChatGPT for recommendations.” Another reported that “leads actually started coming in around month four.” We also noticed that several reviewers had continued working with the agency for years.

2. Genevate

Founded in 2025 by PR and communications leader Brett Kleinberg, Genevate was built specifically for the generative AI era instead of being retrofitted from an older SEO model. We found its approach especially interesting because the team considers not only whether AI platforms mention a company, but also whether they describe that company accurately.

Genevate uses strategic PR and citation building to influence how AI platforms characterize a brand, helping the company appear as the specialist it truly is. In our view, this focus addresses an important part of GEO/AEO that many agencies overlook.

We should note that Genevate is a newer agency, so its portfolio is still developing, which is reflected in its Leadership Experience score. Even so, we consider it a strong fit for logistics companies that want GEO support and are comfortable partnering with a newer firm.

AI Visibility: 4.6
T&L Specialization: 4.1
GEO/AEO Expertise: 4.8
Notable Clients: Missionary Expediters & Cargo
Leadership Experience: 4.2
Average Review Score: 4.8

What We Found in Online Reviews

Clients describe Genevate as a company that “makes sure AI actually describes us right, not just that we show up.” We also found a few clients who pointed out that the agency is “still pretty new, so their portfolio’s thinner than other options.”

3. Focus Digital

We see Focus Digital as an appealing option for smaller transportation companies that want an enterprise-level GEO/AEO methodology without the pricing of a larger agency. Clients receive founder-level attention, straightforward reporting, and realistic timelines. That makes the agency a particularly good fit for regional carriers, smaller freight brokers, and supply chain firms that still want visibility in AI-generated results.

The trade-off, in our assessment, is industry coverage. Focus Digital deliberately maintains a narrow scope, while its case study portfolio leans toward professional services, manufacturing, and home services. We recommend that transportation clients carefully review industry-specific content for accuracy before publication.

AI Visibility: 4.2
T&L Specialization: 4.3
GEO/AEO Expertise: 4.5
Notable Clients: Bowker Transport
Leadership Experience: 4.3
Average Review Score: 4.8

What We Found in Online Reviews

Focus Digital clients appreciate that the team is “straight with us about what is realistic.” One client said they began to “show up in AI answers within a few months.” We also saw reviewers caution that “replies slow down when they’re busy.”

4. Driven Metrics

Driven Metrics offers what we consider an enterprise-grade GEO/AEO framework at a price that growth-stage companies can manage. Its operating model emphasizes weekly meetings, transparent reporting, and conversion tracking instead of relying solely on raw traffic. As a result, content that fails to earn citations or generate leads can be identified and revised quickly.

For a logistics company seeking disciplined, high-end GEO/AEO execution without a large-agency price tag, we believe that combination is difficult to find elsewhere.

We did identify a couple of considerations. Driven Metrics has respectable transportation and logistics experience, but its client base is still growing. Its depth within a particular freight category or logistics model may therefore be thinner than that of a more established agency. We believe transportation companies will get the best results by investing time upfront to explain their operational models, helping the team create content that accurately reflects how buyers in each niche search.

AI Visibility: 4.4
T&L Specialization: 4.0
GEO/AEO Expertise: 4.4
Notable Clients: AutoStar Transport Express
Leadership Experience: 4.3
Average Review Score: 4.7

What We Found in Online Reviews

One client said, “We got results with no excuses, which was refreshing.” Another appreciated that the team “got timely reporting.” However, we also found comments about the agency’s “more limited transportation experience.”

5. Virayo

We found that Virayo has a strong marketing track record with freight and logistics companies. The agency published a case study with specific traffic and lead figures from its work with Truckstop, one of North America’s largest load boards. It has also delivered results for TruckLabs and the last-mile platform Onfleet.

That experience matters for GEO/AEO because the authority and citation work that helped these clients earn organic rankings can also help them appear when brokers and carriers ask AI tools for recommendations.

In our assessment, Virayo still leans more heavily toward SEO than GEO/AEO, and transportation clients compete for attention alongside a broad B2B SaaS roster. Nevertheless, we consider it a strong choice for logistics and freight technology companies seeking proven search fundamentals supported by a credible and expanding AI layer.

AI Visibility: 3.8
T&L Specialization: 4.5
GEO/AEO Expertise: 3.9
Notable Clients: Truckstop, Onfleet, TruckLabs
Leadership Experience: 3.8
Average Review Score: 4.8

What We Found in Online Reviews

A transportation software client called the Virayo team “super responsive and easy to work with.” We also found a reviewer who said its work “leans more toward SEO than strong AI strategy.”

6. Elevation Marketing

Elevation Marketing has served B2B transportation and logistics clients for more than two decades. Based on our review, that longevity makes the vertical a core part of its practice rather than an adjacent service. The agency operates a dedicated trucking and logistics offering and brings substantial leadership depth. President Scott Miraglia has held COO and CFO positions at a major regional agency and has helped place companies on the Inc. 5000 list five times.

We found, however, that Elevation is still developing its GEO and AEO services. Its established toolkit centers on account-based marketing, demand generation, and integrated B2B campaigns, while its AI practice is newer than those core offerings.

Companies primarily focused on maximizing citation volume may find a better fit elsewhere. For a transportation company that wants an experienced, full-service B2B partner with genuine freight knowledge, however, we believe Elevation remains a compelling option.

AI Visibility: 3.2
T&L Specialization: 4.7
GEO/AEO Expertise: 3.5
Notable Clients: Chasewater Industries, Caterpillar, GE
Leadership Experience: 4.5
Average Review Score: 4.3

What We Found in Online Reviews

Clients say the agency “actually get how B2B buyers think.” A few reviewers felt it was “pricier than the smaller shops we looked at,” although most emphasized that “nothing felt cookie-cutter.”

Source

Inspired by this post on First Page Sage Blog.

July 10, 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
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
How I Turn AEO Data Into Action With Profound Projects

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

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

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

Inspired by this post on Try Profound Blog.

July 3, 2026
Profound Agent Templates: Launch AI Workflows Faster

With Profound’s Agent Template Marketplace, I can start from pre-built AI agent workflows instead of building every process from scratch.

It gives me ready-to-clone templates designed for marketing, SEO, and AEO teams, so I can move from idea to live workflow in minutes.

For me, the biggest advantage is speed: I can choose a proven workflow, clone it, customize it for my team, and start using AI agents faster with less setup.

Inspired by this post on Try Profound Blog.

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

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

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

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

Page-level audits

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

1. Brand voice consistency

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

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

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

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

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

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

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

2. Coverage comparison

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

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

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

3. Freshness audit

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

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

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

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

4. AEO and AI retrievability

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

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

Library-level audits

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

5. Performance triage

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

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

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

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

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

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

6. Topical gap analysis

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

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

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

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

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

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

I do not try to audit everything at once

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

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

Inspired by this post on Search Engine Land.
July 3, 2026
How I Analyze Query Fanouts in Profound for AEO Wins

I use Query Fanouts in Profound to understand how Answer Engines turn a prompt into the search queries that shape AI-generated answers.

In this guide, I walk through Profound’s new Query Fanouts page step by step, focusing on how prompts are interpreted, which queries carry the most weight, and how those queries influence visibility inside AI answers.

For AEO teams, this view makes the optimization process clearer. I can see where an answer engine is looking for supporting information, identify the queries that matter most, and spot the strongest opportunities to improve content, authority, and brand visibility.

By expanding my analysis beyond the original prompt, I get a more practical view of the full search pathway behind an AI response. That makes it easier to prioritize the work that can actually improve performance in answer engines.

Inspired by this post on Try Profound Blog.

July 3, 2026
Profound HIPAA Compliance Unlocks Healthcare AEO

I’m excited to share that Profound has successfully completed an independent HIPAA compliance assessment conducted by Sensiba LLP.

For me, this is an important step forward for healthcare, pharmaceutical, and life sciences organizations that want to adopt Answer Engine Optimization without compromising regulatory requirements.

With this assessment complete, I can now support organizations in using AEO more confidently while maintaining the compliance standards that matter most in regulated healthcare environments.

Inspired by this post on Try Profound Blog.

July 3, 2026
Unlocking AI Search: Insights from the AEO Periodic Table V4

I’m thrilled to share the latest from Goodie’s research—the fourth edition of the AEO Periodic Table. This comprehensive guide explores the 14 factors crucial for boosting brand visibility in AI-driven searches. We’ve sifted through an astounding 1.13 million prompts using platforms like ChatGPT, Claude, Perplexity, Grok, Gemini, and Google AI Mode.

What’s new in V4? For starters, we’ve introduced explicit weights and two groundbreaking factors: Search & Fan-Out Rank and Originality & Information Gain. These additions bring fresh insights into the complex world of AI search.

A key takeaway that’s highly noteworthy is that off-site earned and social citations account for a whopping 22% of the total citation leverage. That’s even more influence than any single on-page content factor can muster!

Inspired by this post on HiGoodie Blog.

June 23, 2026