TL;DR: SEO and AEO work better together for startup growth
SEO plus AEO gives you a stronger path to visibility, trust, and leads than treating AI search as a side project.
• The Frenos case shows the pattern clearly: start with buyer-intent SEO, then add AI answer visibility. That helped the company move from near-zero Google visibility to 18.32% in six months in a technical niche, as covered in this Semrush case study.
• Buyers now switch between Google and AI assistants during one research flow. The article cites Semrush research showing 33% start with search and move to AI, while 26% start with AI and move to search. If your brand is missing from one layer, you miss part of the buying journey.
• The practical playbook is simple: pick one revenue-linked topic, build a pillar page, add cluster content and FAQs, include proof and citations, then track both rankings and AI mentions. This AEO guide helps explain why clear, structured, answer-ready content gets picked up more often.
If you want more qualified visibility in 2026, start with one tight topic cluster and build your search and answer presence at the same time.
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A March 2026 Semrush case study on David Haas and Frenos caught my attention for one reason: it shows, with unusual clarity, what many founders still refuse to accept. AI visibility without SEO is flimsy. And old-school SEO without answer visibility is now incomplete. For entrepreneurs, startup founders, freelancers, and small business owners, that is not a theory problem. It is a pipeline problem, a trust problem, and very quickly a revenue problem.
I write this as a European founder who has spent years building companies across deeptech, edtech, IPtech, and AI-assisted startup tooling. I have learned, often the hard way, that small teams do not win by doing more channels badly. They win by building one system that compounds. That is exactly why this Semrush One story matters. It is not about one marketer using one software stack. It is about a repeatable growth method: build search authority first, then layer answer engine visibility on top, and make both reinforce each other.
Here is why this matters now. According to the Semrush article, Frenos moved from near-zero Google visibility in July 2025 to 18.32% by January 2026 in a highly technical niche. On top of that, Semrush research says modern buyers now mix classic search and AI tools during research. About 33% start with search and switch to AI, while 26% do the reverse, and only 8% keep them separate. If your brand is absent from either layer, you are missing part of the buying journey.
What actually happened in the Frenos case?
The article centers on David Haas, a fractional CMO with almost 30 years in marketing, working with growth-stage tech and healthcare firms that often have budget but lack a full in-house SEO or content team. His client, Frenos, an operational technology cybersecurity startup, needed visibility in a niche where trust, technical accuracy, and authority matter a lot more than chasing vanity traffic.
Haas did something many founders skip because they are impatient. He started with the boring layer first. I mean that as praise. He used Semrush Keyword Magic Tool, Semrush keyword overview tools, and Semrush Position Tracking to establish a search baseline around buyer-intent terms with realistic ranking potential. He avoided broad trophy keywords like “OT cybersecurity” where competition was steep and upside was weaker for a smaller player.
Only after that did he add the AI layer using the Semrush AI Visibility Toolkit and the Semrush AI competitor research tool. The initial AI Visibility score for Frenos was 14, which the article describes as typical for a smaller brand. Then came the compounding move: pillar pages, topic clusters, technical FAQs, and structured content mapped to both search intent and AI answer patterns. That combination helped Frenos gain ground in Google and in AI-generated discovery surfaces at the same time.
“SEO accelerates when data informs strategy, and strategy guides content,” Haas said in the article. I agree with the principle, though I would phrase it more bluntly. Content without a system is content debt.
Why does this SEO plus AEO model matter so much in 2026?
Let’s define the terms clearly, because a lot of people are still mixing them up.
- SEO means search engine work aimed at ranking web pages in Google and other search engines for relevant queries.
- AEO, or Answer Engine Optimization, means shaping your content and brand signals so AI systems such as ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews can cite, summarize, or mention you when users ask questions.
- AI visibility refers to how often and how well your brand appears inside these generated answers, summaries, prompt responses, and cited source lists.
This matters because buyer behavior has changed. The Semrush buyer journey research on AI tools and search behavior found that people move between search engines and AI assistants during one research flow. They do not treat them as separate worlds. A founder might Google a category, ask ChatGPT for a shortlist, check Reddit for peer validation, and then return to Google to inspect vendor pages. Your company now needs presence across that full chain.
This also matches what I see in startup education and founder tooling. People do not want ten tabs and twenty experts for basic growth work. They want one operating model. In my own work across Fe/male Switch, CADChain, and AI-assisted founder systems, I have learned that small teams need infrastructure, not motivational fluff. The same rule applies to marketing. If your search, content, and answer visibility work live in separate silos, your team will move slower than your competitors.
Other 2026 sources reinforce the same shift. Semrush’s guide to answer engine optimization states that AI systems do not just list links. They retrieve, evaluate, and generate answers. ABI Research reports a 93% increase in AI referral traffic over ten months and says it appeared in 765 AI Overview search results according to Semrush data. seoClarity’s 2026 article on SEO and AEO strategies makes the point even more sharply: content now needs to function as structured knowledge fragments, not just as web pages.
How did David Haas build the system step by step?
The Haas workflow is useful because it is simple enough for founders to copy. Here is the sequence in plain English.
- Set the SEO baseline. Identify buyer-intent queries with realistic ranking potential. Skip ego keywords.
- Track search positions. Measure visibility against direct competitors, not fantasy competitors.
- Measure AI visibility. Check how the brand appears in AI-generated answers and which prompts competitors win.
- Build topical authority. Create pillar pages and connected cluster content around narrow, high-value themes.
- Run a feedback loop. Review keyword movement, prompt visibility, mentions, citations, and gaps. Then publish the next content based on evidence.
That may sound obvious. It is not. Most early-stage companies do one of these things, then stop. Or worse, they publish random blog posts and call it a content strategy. Founders love novelty. Search rewards consistency. AI answer systems reward clarity, topical depth, brand mentions, and source trust. The winners are often the teams that look less glamorous from the inside.
1. Establish a search baseline before chasing AI mentions
Haas began with classic search research using Semrush tools. This is the right move because AI answer systems still rely, directly or indirectly, on the open web, published pages, entity signals, third-party mentions, and search-derived relevance patterns. If your site lacks topical depth, AI tools have little reason to trust you.
Founders often want to skip this because AEO sounds newer. That is a mistake. AEO is not a replacement for SEO. It is a second layer sitting on top of it. You need indexed pages, clear topic coverage, internal linking, consistent terminology, and real proof. Without that, your AI mentions can be sporadic and fragile.
2. Measure AI visibility as a separate signal
After the search baseline came AI visibility measurement. This matters because ranking on page one and getting cited inside AI answers are related, but not identical. A page can rank well and still fail to get cited if it is vague, fluffy, unstructured, or lacks direct answers. A smaller brand can also appear in AI answers if it has sharply written, specific, source-backed content around a narrow topic.
This is where tools matter. According to Profound’s 2026 comparison of Profound and Semrush for AEO, Semrush includes prompt research and topic-level AI data built from real clickstream behavior and Google keyword data for AI Overviews. You can debate product philosophy, but the market signal is clear: prompt tracking, brand mentions inside LLMs, citations, and AI share-of-voice are now standard marketing metrics.
3. Build topical authority with pillar pages and clusters
This is my favorite part of the Frenos story because it reflects how knowledge actually compounds. Haas created pillar pages around themes such as OT penetration testing and industrial cyber testing, then added supporting pages around use cases, sub-questions, technical scenarios, and FAQs. These pages linked to one another and referenced credible sources.
That structure does two things at once:
- It helps search engines understand the site’s topical coverage.
- It gives AI systems clean, answer-ready source material that can be cited or summarized.
This pattern fits a rule I use in founder education: people do not need more inspiration, they need infrastructure. Content works the same way. One great article is nice. A well-mapped cluster is an asset.
4. Keep refining keywords, prompts, and content angles
The article shows Haas repeatedly checking Semrush tools to see which topics were gaining traction, where gaps remained, and what the next content move should be. This loop matters because both search results and AI answer surfaces shift fast. Competitors publish. Models update. Forums rise. Google changes result layouts. New prompts appear.
For founders, this means one thing: stop treating content as a campaign and start treating it as a product system. If you are building a startup, you already know that products improve through feedback. Your content and visibility stack should work the same way.
What can founders and small teams learn from this right now?
Here is the practical lesson. If you have limited time, limited headcount, and no in-house SEO department, you do not need a giant publishing machine. You need a disciplined sequence.
- Pick a narrow commercial topic where your buyers already have pain and intent.
- Map the questions buyers ask in search, in sales calls, on Reddit, in communities, and inside AI assistants.
- Create one pillar page that explains the topic with precision.
- Add supporting articles that answer adjacent questions in direct, source-backed language.
- Track rankings and AI mentions each month.
- Refresh pages when new objections, terms, or prompts appear.
I would add one more rule from my own founder playbook. Default to systems that small teams can actually maintain. A lot of startup marketing advice is written for companies with writers, SEO specialists, analysts, PR teams, and paid distribution. Most founders do not have that setup. They need workflows that work with one marketer, one founder, or one fractional CMO.
What does Semrush One add that separate tools often miss?
The value in the Frenos example is not just the tools themselves, but the fact that they sit in one operating environment. Semrush One brings together classic search work, AI visibility tracking, prompt research, and competitor monitoring. For larger companies, that may just mean convenience. For smaller firms, it can change execution speed.
That matters because fragmented workflows create hidden costs:
- You lose time moving between tools.
- You compare mismatched datasets.
- You miss relationships between ranking data and AI mention data.
- You publish content based on guesswork instead of evidence.
- You struggle to explain progress to founders, clients, or investors.
A 2026 Semrush study on AI search and SEO adoption reported that only 22% of marketers had fully connected AI search and SEO. That number should worry founders. It means many teams are still operating with split visibility models while buyer behavior has already changed.
At the same time, not every company needs the same stack. Amadora’s 2026 comparison of GEO and AEO tools breaks the market into tracking, content execution, authority research, and local visibility tools. That is useful. Still, the Frenos story makes a good case for choosing fewer tools with tighter workflow logic, especially when your team is small.
How can a founder copy this approach without a big budget?
Let’s break it down into a founder-friendly version. If you sell software, services, consulting, or B2B tools, you can adapt the same system in six steps.
- Choose one revenue-relevant topic cluster. Pick something your buyers actively research before purchase.
- Define entities clearly. If you sell cybersecurity for industrial systems, use terms such as operational technology, OT security, industrial control systems, penetration testing, compliance, and threat detection in the correct context.
- Create one authoritative pillar page. Make it clear, structured, and directly useful.
- Publish supporting FAQs and use-case pages. Answer narrow questions with direct language.
- Add external proof. Cite customer cases, original data, third-party studies, expert commentary, and community mentions.
- Measure both search and answer visibility. Track rankings, prompt mentions, AI citations, and traffic quality.
If you are a freelancer or consultant, the same structure works at a smaller scale. Build one cluster around your service category, one around your client pain point, and one around your method. Then monitor which pages earn not just traffic, but qualified calls and mention-rich AI visibility.
A practical content structure founders can use
- Pillar page: “What is OT cybersecurity risk assessment?”
- Cluster article: “OT penetration testing vs vulnerability assessment”
- Cluster article: “How industrial manufacturers prepare for a cybersecurity audit”
- FAQ page: “How much does OT cybersecurity testing cost?”
- Proof page: case study, methodology, or checklist with named steps
This format works because it mirrors how humans and AI systems look for information. One broad page gives context. Supporting pages answer narrower questions. Proof pages show trust signals.
Which mistakes do most teams make when trying SEO and AEO together?
This is where I want to be a little provocative. Many teams are not failing because AI search is mysterious. They are failing because their content habits are lazy. Here are the most common mistakes I see.
- Chasing AI visibility before earning topical trust. You cannot fake authority for long.
- Publishing vague content. AI systems prefer clear, answer-ready language.
- Ignoring buyer intent. Traffic without commercial relevance is a distraction.
- Skipping competitor analysis. If rivals dominate prompt mentions, you need to know why.
- Writing for algorithms, not humans. Fluffy keyword stuffing fails in both search and AI answers.
- Forgetting external validation. Reddit threads, reviews, citations, podcasts, and industry mentions matter.
- Not refreshing old content. Stale pages decay in rankings and in AI usefulness.
O8’s 2026 AEO playbook and rygr’s 2026 article on why AEO matters for marketing planning both point to the same pattern: answer visibility depends on much more than web pages. It also depends on structured data, PR, reviews, publisher mentions, and community signals. That means founders should treat AI discoverability as a brand-wide trust issue, not just a content issue.
What is my founder take on why this framework works?
I think this approach works because it matches how small companies should think about competition. Startups do not win by copying the surface behavior of big brands. They win by building compact systems that compound faster. As a parallel entrepreneur, I have seen this across product design, startup education, and market positioning. One well-structured workflow beats scattered effort almost every time.
There is also a linguistic point here, and I care about that a lot because my background includes linguistics and education. AI systems reward semantic clarity. If your company uses sloppy terms, inconsistent naming, generic claims, and unclear definitions, your visibility suffers. If your pages define entities properly, answer real questions, and connect related concepts in a coherent way, machines can parse you more easily and humans trust you more quickly.
That is why I like this Semrush One case. It is not just about traffic. It is about language, structure, intent, and trust living inside one growth system. For founders, that is the right mental model.
What should founders do next if they want the same result?
Next steps are simple, even if the work takes discipline.
- Audit your current Google visibility for buyer-intent terms.
- Check whether your brand appears in AI answers for commercial prompts in your niche.
- Choose one topic cluster tied to revenue, not vanity traffic.
- Build a pillar page and three to five supporting pages.
- Add proof, citations, third-party mentions, and clearer definitions.
- Track search positions, AI mentions, and lead quality monthly.
If you are early-stage, do not wait for a full marketing department. Start with one founder, one specialist, or one fractional CMO who understands both search and answer visibility. If you are later-stage, stop treating AI search as a side experiment. Your buyers already moved on.
The big takeaway from the Frenos story is simple: SEO and AEO work best when they are layered in sequence, measured together, and fed by clear topical authority. That is how smaller brands can punch above their weight in 2026. And if you are building in a crowded category, you should feel a bit of FOMO right now, because your competitors are not waiting for perfect certainty.
I would bookmark the Semrush case study on David Haas and Frenos, study the Semrush AEO guide for 2026, and compare it with the outside perspective in ABI Research’s article on increasing AI search visibility and seoClarity’s enterprise view on SEO and AEO. Then build your own version, with less noise and more structure.
FAQ
Why should founders build SEO before chasing AI visibility?
A strong SEO base gives AI systems trustworthy, crawlable, and topically clear source material. Without that, AI mentions are inconsistent and hard to sustain. Start with rankings, structure, and internal links, then add answer visibility. Explore SEO for startups in 2026 and review David Haas’s Semrush One workflow.
What does SEO plus AEO actually mean for a small business?
It means optimizing both for search engine rankings and for AI-generated answers in tools like ChatGPT, Gemini, and Google AI Overviews. Small teams should treat them as one growth system. See AI SEO for startups and compare it with this AEO guide for 2026.
How did Frenos improve visibility so quickly in a technical niche?
The company focused on buyer-intent keywords, skipped broad vanity terms, built pillar pages, and added cluster content plus FAQs. That created compounding topical authority instead of random blog output. Read the startup SEO playbook and study the Frenos case study.
What content format works best for SEO and AEO together?
A pillar page supported by tightly linked cluster articles, FAQs, and proof pages works best. This structure helps search engines understand topic depth and gives AI tools answer-ready content blocks. Use this AI SEO for startups guide and review seoClarity’s knowledge-fragment model.
How can a startup measure AI visibility in 2026?
Track prompt mentions, citations, share of voice, and whether your brand appears in AI-generated comparisons or summaries. These metrics complement rankings rather than replace them. Learn startup analytics basics here and check Semrush One’s SEO and AEO dashboard overview.
What keywords should founders target first?
Start with narrow, high-intent, commercially relevant terms your buyers actually use before purchase. Avoid trophy keywords with heavy competition and weak conversion value. See Google Search Console for startups and validate the approach with ABI Research on AEO question selection.
Can a freelancer or fractional CMO use this same framework?
Yes. The approach works especially well for lean teams because it relies on sequencing, measurement, and repeatable content systems rather than large headcount. One specialist can run it if the workflow is disciplined. Review the bootstrapping startup playbook and see the AI tools fractional CMOs actually use.
What mistakes hurt SEO and AEO results the most?
Common problems include vague content, weak definitions, poor structure, no external proof, and treating content like a one-off campaign. Refreshing older pages is also essential. Read AI automations for startups and compare with O8’s complete AEO playbook.
Why does using one platform like Semrush One matter for small teams?
A unified system reduces context switching, keeps ranking and AI visibility data aligned, and makes prioritization easier. Small teams benefit more from workflow speed than from tool sprawl. Explore Google Analytics for startups and check this Semrush One review for AI visibility tracking.
What should a founder do first to copy this growth strategy?
Pick one revenue-linked topic cluster, build one authoritative pillar page, publish three to five supporting pages, and track both rankings and AI mentions monthly. Keep refining based on evidence. Start with SEO for startups and watch this Semrush tutorial for mastering SEO and AEO.
