SEO
- A ranked link on a results page
- Position for a keyword
- Crawlable pages, internal links, keywords where they belong
- Rankings and organic sessions, usually monthly
- Clear writing, real authority, a site crawlers can read
Conversational search demand: the question replaced the keyword
Someone with a problem describes it to ChatGPT or Perplexity in full sentences, the way they'd describe it to a colleague. They get back one answer with a few companies named inside it. Those companies get the visit, the shortlist spot, and the pipeline. There's no page two.
- Citations in answers: A buyer asks a question, the engine composes an answer and names its sources. If your page carried the cleanest passage, you are the link they click, already matched to you by their own question.
- Recommendations in comparisons: Buyers ask engines to compare vendors and draft shortlists. Being recommended puts you in the deal before you knew the deal existed; being absent means a shortlist you never hear about.
- AI agents reading your site for a buyer: More buyers send an agent to visit your site, read it and report back. A site an agent can parse turns that visit into an accurate summary of what you do. A site it cannot parse turns into a shrug.
SEO vs AEO: how the two tactics differ
AEO
- A citation inside the answer, with your company named as the source
- Share of voice across the questions your buyers ask
- Structure machines can read: schema, llms.txt, pages shaped like answers
- Citations and share of voice, tracked live against named competitors
- The same inputs, aimed at a different target
01
Is AEO just SEO with a new name?
Partly, and it costs nothing to admit it. The inputs overlap almost entirely: a crawlable site, real authority, writing that answers the question it raises. If you did SEO well, you already built a solid foundation. What moved is the target. A results page has room for a long list and sends real traffic to more than the top result. An AI answer names a few sources and gives everyone else silence. Some of the work is genuinely new: schema and llms.txt written for AI crawlers, pages built so a lifted passage still makes sense on its own, and research into the questions buyers ask assistants.
02
Visibility is now measured inside the answer
Conversational search demand behaves differently from keyword demand. A buyer no longer types two words and scans ten links. They ask which platforms let a marketing team publish pages without engineers, and what the tradeoffs are. The question carries context, constraints, and intent, so the answer can be specific, and often final. Many of these questions never reach a results page at all. Which means visibility has a new unit. It's no longer position for a keyword. It's how often the engines cite you or recommend you across the questions your buyers ask. If you only watch rankings, this whole layer of demand is invisible to you while a competitor takes it.
03
What are AI answer engines
ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, and Copilot: software that reads the web and composes one answer instead of returning a list of links. They read your pages, your structure and schema, and what other credible sources say about you. When they answer a question, they pick a handful of sources to cite or recommend: passages that answer the question cleanly, facts that stay consistent across the web, companies with real evidence of doing what they claim. No engine publishes its rules, and the rules keep moving. What holds across all of them is simple: they quote what they can read and trust, and they skip what they can't parse.
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Who we are and why teams like yours work with us
We exist for B2B teams under pressure to turn an AI roadmap into visible product usage, expansion, and a safer story in front of the C‑suite and investors. If design isn’t moving revenue, adoption, or retention, it’s decoration. We design to avoid that. Since 2015, we’ve shipped 600+ products and earned 120+ awards for work on complex, data-heavy tools: fintech platforms, AI copilots, decision engines, and vertical SaaS. Our work has helped clients turn “we have AI features” into “our customers actually use and pay for them.”
We started designing AI products in 2017, long before “AI-native” became a buzzword. With 30+ AI products shipped, we focus on the hard part most teams struggle with: making complex intelligence feel simple, trustworthy, and obviously valuable in a demo, a POC, or a QBR. We’re a 40+ person team of UX strategists, product designers, and analysts who treat design as a business function.
What sets us apart from a typical agency or a single in-house hire is pattern recognition at scale. We’ve seen what works – and what quietly kills adoption – across hundreds of AI and data-heavy products. That lets us spot failure modes early, bring proven interaction patterns to your team, and reduce the risk that your next AI release is another unused toggle in a settings menu.
We start with research not because it’s “best practice,” but because designing without understanding your users, your market, and your revenue model is just guessing with nicer pixels. From there, we collaborate with your product, AI, and design leaders to define where AI should show up, how it should behave, and how to make it obvious, safe, and monetizable.
If you’re a Head of AI, Product, or an AI-native founder who needs AI capabilities to be seen, understood, and used now, not someday, we’re built to be that partner.
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How our AI search optimization service looks like
01
Free AI visibility check
Measures the starting line: which engines cite you today, who wins the answers in your category, and where the gaps are.
02
Keyword and AEO discovery
Maps the questions your buyers ask, clusters them by intent, and makes the platform call: what your site needs before content can compound.
03
The agentic build
Gives citations a site to stand on: machine-readable, fast to publish on, run by the marketing team.
04
Continuous demand generation
Keeps citations accruing month after month: question discovery, steady publishing, technical upkeep, and a dashboard showing share of voice any day you open it.
Frequently asked questions
How we work, scale, and deliver measurable outcomes.
How do we get ChatGPT and Perplexity to send us qualified traffic?
Through the three doors above: a citation in an answer, a recommendation in a comparison, or an agent reading your site on a buyer's behalf. All three reward the same preparation — a site machines can parse, pages written so a passage survives being lifted out, and evidence that backs what you claim. There's no separate trick for each engine.
How fast does inbound traffic from AI show up?
Partly, and the check tells you exactly how far. Schema, llms.txt, and structural fixes retrofit onto most platforms, and strategy work doesn't care where your site lives. The ceiling is publishing speed: citations follow pages, and if every page takes weeks to publish, the cadence dies. That's the point where the build conversation starts, and not before.
How is it measured?
Share of voice: across the questions your buyers ask, how often you get cited by AI engines versus named competitors, tracked live instead of reported monthly. Alongside it, referral traffic from the engines and what it converts into, so the graph connects to pipeline.
Does this cannibalize our SEO?
No. The inputs overlap, so work earning citations tends to help rankings too. What changes is where demand shows up: a growing share of buying questions gets answered inside the engines and never reaches a results page. Measuring only rankings doesn't lose you that demand. It hides who's winning it.
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