Seer’s Framework for Human + Machine Design Testing
Six months ago, our Generative Engine Optimization (GEO) team and our Conversion Rate Optimization (CRO) team were presenting on a client call when each realized they had been optimizing the same client page — at the same time — without talking to each other.
The good news was that the client’s page had improved. However, because each team’s project roadmap was built in silos, we had no idea which changes made the difference, or whether any of the recommendations worked against each other. It became clear that while CRO and GEO are both vital for the client’s site health, neither works when the two teams work separately.
That's when we started building a different way of tracking both data sets to get a more holistic view of how both humans and machines were interacting with the site.
Your Website Now Has Two Kinds of Visitors
Not long ago, optimizing a website meant one thing: making it work for the people using it. Clear navigation, compelling copy, and a frictionless path to conversion were the only metrics that mattered.
That is no longer true.
Today, your website is being read, interpreted, and cited by "AI visitors," or scrapers from companies like ChatGPT, Gemini, and Copilot. These systems don't browse as a human does; they parse and synthesize your content to decide if your brand is worth mentioning in a response.
Most websites are optimized for one of these audiences, but almost none are optimized for both. That is the gap our Human + Machine Design Testing is designed to close.
What is Human + Machine Design Testing?
Human + Machine design testing is a collaborative framework between conversion rate optimization (CRO), UX, SEO, and Generative Engine Optimization (GEO). It ensures that AI-optimized content and design changes improve machine visibility without compromising the human user experience or site conversion rate.
Rather than treating UX and AI visibility as separate workstreams, we combine them into a single testing framework. We hypothesize, design, and evaluate website and template changes that target LLM visibility in accordance with UX principles.
This conflict may not be something that is on your radar right now, but for marketing leaders, this risk can become costly. Something could seem like a CRO win, but tanks AI visibility. Or, a seemingly positive GEO change could negatively affect page conversions. When conflicting results of these interactions become visible down the line, the damage is already done.
The Core Philosophy
Good design for humans and clear structure for machines are compatible. When you optimize for both simultaneously, you future-proof your digital ecosystem.
Why This Matters Right Now: The "Dual-Lens" Necessity
GEO can show us which pages are being cited, but it can't tell us if the human experience on those pages is actually working.
SEO and GEO teams are usually optimizing for machine readability, while CRO and UX teams are redesigning pages to reduce human friction. With AI citation content changing as frequently as it does, the brands that will win aren't the ones that do more of one approach or the other — they’re the ones who do both simultaneously and can measure the interaction between them.
Our Human + Machine framework removes that blind spot by evaluating every Human + Machine test through a dual-lens analysis: AI citation impact + user behavior impact.
How Our Approach Works (The Ideal 6-Week Cycle)
We move from baseline to reporting in a structured 6-week sprint. Because the handoffs are where siloed agencies fall apart, the real differentiator isn't just the structure itself, but also how our CRO and SEO/GEO teams collaborate inside of it.
Week 1: Shared Baseline, Separate Lenses
Every engagement opens with a joint kickoff between our GEO, CRO, and UX teams. GEO pulls the AI citation baseline using Scrunch, identifying which pages are being surfaced, for what prompts, and at what frequency. CRO simultaneously audits those same pages for conversion performance using GA4 and heatmap data. Before a single hypothesis is written, both teams can see the full picture of where human performance and machine visibility are misaligned.
Week 2: Parallel Hypotheses, One Wireframe
Each team arrives at the hypothesis session with their prioritized recommendations. For instance, GEO might flag that a product page's lack of structured FAQ content is suppressing citations, while CRO might flag that the same page has a bounce rate driven by a confusing hero module.
Rather than resolving this in separate roadmaps, both hypotheses feed a single wireframe. Any design change that follows is pressure-tested against both objectives before it ever reaches a developer. When there's tension, the tiebreaker is simple: does the change risk measurable harm to either metric? If the structured content GEO needs would clutter the page enough to hurt conversion rate (CVR), we would explore layout alternatives before shipping. The goal is never to sacrifice one audience for the other.
Week 3: One Implementation, Two Tracking Plans
Content and UX changes go live as a single implementation, but with two parallel monitoring setups. CRO tracks session behavior, engagement, and conversion rate. GEO tracks AI mention frequency and citation accuracy in the days & weeks following launch.
Weeks 4 to 6: Analysis and Reporting
We report CRO and GEO results together after analyzing them against an overlay of questions:
- Did citations increase?
- Did CVR hold?
- Did improving machine readability correlate with reduced bounce rate?
If the data is inconclusive, we might layer in qualitative user testing to understand the why. This collaboration model is only possible inside an agency that houses both disciplines.
By implementing this recommended testing approach, we expect both GEO and CRO initiatives to improve. However, each team must measure results carefully to ensure that any improvements that happen are the outcome of the GEO & CRO changes instead of outside variables.
What Triggers This Work?
We recommend Human + Machine Design Testing when we see specific "performance mismatches" between how a page serves human visitors and how it performs with AI systems. These are the four patterns we see most often.
The Invisible Hero: Strong CVR, Low AI Visibility
Your page converts well, but when you run an AI audit, the page is absent from responses to the exact prompts your users leverage during research. The problem is that the page was built for scanners, not parsers. Large visual modules, minimal structured text, and no explicit product definition are all invisible to an AI platform trying to extract a citable summary.
So what? The fix is often targeted: a structured FAQ block, an explicit value proposition in the first third of the page (where nearly half of all LLM citations originate), and schema markup that helps AI systems understand the page's entity. Citations improve without touching the conversion-driving elements.
The Competitor Gap: Present in Search, Missing from AI
Your brand ranks well organically, but a competitor consistently surfaces when users ask AI assistants for category recommendations. This is increasingly common because AI systems and traditional search engines draw from different source pools. Ranking well doesn't guarantee citation.
So what? This trigger calls for a content and structure audit focused on the specific prompts driving competitor visibility, with CRO validating that any changes made to improve machine readability don't introduce friction for the human visitors already converting.
The Accuracy Issue: AI Is Getting Your Brand Wrong
This is one of the more urgent triggers, and one that clients often don't discover until a sales conversation surfaces it. AI is citing your brand, but the information is inaccurate, outdated, or incomplete.
So what? By creating authoritative, well-structured pages on your site, you can significantly improve the accuracy of what gets surfaced. CRO's role here is ensuring that the correction doesn't come at the cost of page clarity for human visitors.
Strategic Importance: Critical Pages Being Ignored
Some pages are foundational to your customer journey regardless of their current traffic or conversion volume. A key solution page, a comparison page, or an ROI calculator that sales teams rely on in late-stage deals may not be generating AI citations at all.
So what? The dual-lens approach helps us build the case for investment: improving machine readability on a high-value page often improves its clarity and structure for human visitors as well.
Grounded in Real User Behavior, Not Assumptions
This testing doesn't happen in a vacuum. It is fueled by our AI Search User Testing studies.
If our research shows that users are prompting AI with specific questions about your service or solution, we use those real-world prompts to inform the Human + Machine Design hypotheses.
What we're watching for:
- What exact prompts are your users typing into ChatGPT or Gemini during the research phase?
- When AI responds, are users satisfied? Do they follow up? Do they click through?
- Where does AI's response contradict, omit, or misrepresent your brand?
Those real-world prompts become the test inputs for our GEO hypothesis work. We optimize for the questions your actual users are asking. When user testing reveals a gap, that finding feeds directly into the design hypothesis for the next sprint. The CRO and GEO teams jointly determine how to surface that information in a way that's both machine-readable and human-friendly.
What This Looks Like in Practice
We're currently working with a brand in the home improvement space whose primary product page is one of their most-cited pages in LLM responses. However, our GEO team's prompt analysis revealed a gap. When users ask AI assistants to compare products in this category, LLMs consistently surface attributes like slip-resistance, fade-resistance, and realistic material aesthetics as key decision factors. However, those attributes were underrepresented on the page, meaning the brand was losing ground in competitive comparison queries despite having a strong citation baseline overall.
Rather than treating this as a content problem alone, our CRO and GEO teams approached it together. GEO identified the specific benefit language LLMs use when evaluating this product category. CRO assessed how introducing additional benefit callouts would affect the page's existing conversion elements, ensuring the additions would engage human visitors as effectively as they signal value to AI systems.
We're in the midst of tracking performance at 30, 60, and 90 days across both dimensions: increases in AI citation frequency for the newly added benefit themes, and conversion rate movement on key actions like lead forms and dealer locator engagement.
A good outcome would be increased AI citation frequency without a negative impact on conversion rates. A better outcome would be increased AI citation frequency with a positive impact on conversion rates.
The Takeaway
AI platforms are no longer just tools. They are a permanent and growing segment of your audience, one that most websites are not built to serve.
The brands that will maintain visibility in both traditional search and AI-generated responses are the ones that stop treating CRO, UX, and GEO as separate workstreams. Human + Machine Design Testing is how we close that gap: one implementation, two tracking plans, and a shared hypothesis that neither team could build alone.
What To Do This Month
If you're a CRO, SEO, or UX practitioner, here are three concrete starting points:
- Run an AI citation audit on your top 5 converting pages. Use a tool like Scrunch or manual prompt testing in ChatGPT, Claude, and Gemini. Note which pages are cited, which are absent, and what language AI uses to describe your brand.
- Flag one page where CVR and AI visibility are misaligned. A page that converts well but doesn't appear in AI responses, or vice versa, is your first Human + Machine test candidate.
- Bring your CRO and SEO/GEO counterparts into the same room for one hypothesis session. If your teams have never co-written a test brief, that's the gap. Start there before you touch a wireframe.
Most organizations are still measuring SEO/GEO, CRO, and UX in isolation, which means they’re missing how those decisions interact in the new AI-driven environment. Without a unified approach, you risk improving one metric while quietly eroding another that matters just as much. In a world that is increasingly designed for humans and AI, siloed optimization isn’t efficient; it’s dangerous.
If you're not sure where your site stands in either dimension, that's exactly where we start. Reach out to Seer to run a dual-lens audit on your highest-priority pages.
