Written by: Arjun Karnik, Growth Marketing Specialist

Key Takeaways from Arjun Karnik’s GEO Playbook

  • AI search visibility now depends on technical plumbing, fan-out query alignment, and impression-decay monitoring rather than traditional rankings.
  • Zero-click searches and low AI recommendation coverage create a measurement and retrieval gap that most brands are not tracking.
  • Unblocking AI crawlers, adding schema, and ensuring content exists in initial HTML are the three core technical fixes that unlock downstream visibility.
  • Extracting ChatGPT fan-out queries and rewriting slugs, titles, and H1s in buyer language increases citation likelihood by 161% compared to main-query optimization alone.
  • Book a demo to see how Arjun Karnik runs this diagnostic on AI Growth Agent.

The Zero-Click Reality and the Search Console Scissors

Rankings are no longer the main constraint on growth because the search surface changed. SparkToro’s analysis of Similarweb clickstream data found that 68.01% of U.S. Google searches ended without a click during January–April 2026, up from 60.45% in 2024. AI Overviews now appear in approximately 48% of Google search results as of 2026.

This shift shows up in Search Console as a scissors chart where impressions climb while clicks fall. The content is still being read and used to construct AI answers. It is simply not sending traffic the way it used to. That pattern reflects a measurement failure and a plumbing failure, not a content failure.

An Omni Eclipse study that manually queried ChatGPT across 1,700 businesses found that only 11.9% appeared in AI recommendations, meaning 88.1% were completely absent from AI search discovery. Of the businesses ranking on Google page one, 77% remained invisible in ChatGPT. The diagnostic below is the sequence Arjun Karnik runs in his public test lab via AI Growth Agent to close that gap.

Step 1: Run a Multi-Engine Visibility Audit Before Changing Content

Baseline current visibility before publishing or restructuring anything. The audit answers one factual question: what do the assistants currently say about this business, and to whom are they giving its answer instead?

The recommended logging schema tracks seven dimensions that reveal not just whether you appear, but how reliably and accurately you appear relative to competitors. Log the prompt, engine, run number, whether the brand was mentioned, whether it was cited with a URL, whether the answer was accurate, which competitors were named, and a sentiment note. A 2026 arXiv study titled “Don’t Measure Once” concludes that AI search answers vary across runs, prompts, and time, so visibility should be measured as a rate across repeated samples rather than from single observations. Run each prompt at least three times per platform.

This baseline functions as the control group. Every downstream result is measured against it.

Step 2: Repair Technical Plumbing So AI Crawlers Can Read the Site

Nothing downstream works if the retrieval layer cannot access the site. Fixing technical plumbing comes before any content strategy.

Three issues account for most AI search invisibility. SearchScore’s analysis of over one million website audits found that unblocking AI crawlers in robots.txt, creating an llms.txt file, and adding schema markup are the top three failure points. Across 6,944 websites audited in July 2026, 6.9% block at least one major AI crawler, most without realizing it, and 69.7% have no llms.txt file.

To fix the first issue, explicitly allow four specific user agents. The crawlers to unblock are GPTBot (OpenAI’s search crawler is OAI-SearchBot), ClaudeBot, PerplexityBot, and Google-Extended. A practical crawlability check must verify five layers in sequence: network and edge access through CDN or WAF, robots.txt permission, HTTP behavior returning healthy 200 responses, content delivery and rendering, and parseability of structured information.

Schema provides a structured map that AI systems use to understand page purpose and extract facts. In audits of business websites, 60–70% have either no structured data or significant implementation errors, which means most sites lack this layer. JSON-LD is the preferred format because it separates structured data from visible content and is easier to maintain. The minimum viable implementation covers three page types: Organization schema on the homepage to define the business, Article or BlogPosting schema on content pages to identify the content type and author, and FAQPage schema on answer-format pages to mark question–answer pairs for extraction. Every fact marked up in structured data must be visible to users on the page. Marking up hidden content reduces trust for AI retrieval systems because it creates a mismatch between what humans see and what machines are told.

The single most common silent blocker beyond robots.txt is content that only exists after JavaScript runs. This pattern prevents most AI crawlers from seeing content even if the rendered page looks correct. Core text, statistics, and links must be present in the initial HTML response.

Step 3: Pull Fan-Out Queries from ChatGPT and Align Core Page Labels

Each buyer prompt triggers a cluster of hidden retrieval queries, not a single lookup. The answer is assembled from what those sub-queries return. Perplexity averages 2.24 sub-queries per prompt and ChatGPT 3.51, with Google AI Mode showing variable fan-out typically between 3 and 10 depending on query complexity.

Arjun’s method focuses on the machine’s questions instead of the visible prompt. Extract fan-out queries directly from ChatGPT rather than inferring them from keyword tools, because the target is the internal retrieval language. Pages ranking for fan-out queries are 161% more likely to be cited in Google’s AI Overviews than pages ranking only for the main query, per Surfer SEO research.

In a documented test on Arjun’s own site using AI Growth Agent, pages rewritten to match extracted ChatGPT fan-out queries earned citations while control pages did not. The rewrite targets slug, title tag, H1, and H2s. All four must use the language the machine is actually retrieving against, not practitioner jargon.

Step 4: Relabel Pages in Buyer Language and Track Citation Lift

Buyer language at the page-label level removes friction at the exact moment the machine matches a question to an answer. A page titled “What is GEO” on Arjun’s site was relabelled to “How to Get Your Business Recommended by AI Search,” with the slug, title, H1, and H2s all realigned to buyer questions. Citations followed within weeks of that specific change.

Adding statistics increases AI citation visibility by around 31–33% and adding quotations by around 41–43%, according to the Princeton GEO study. Buyer-language alignment and answer-first formatting function as structural requirements for the retrieval layer, not stylistic preferences.

An Ahrefs study of 75,000 brands found brand web mentions correlate at 0.664 with ChatGPT citation likelihood, compared to only 0.218 for backlinks. In this channel, authority comes from topical coverage and structured specificity rather than accumulated domain authority.

Step 5: Use Impression-Decay Tripwires to Trigger Self-Healing Content

AI-driven visibility decays quickly, so sites need automation that reacts before positions disappear. In Arjun’s own tests, pages dropped 78% to 99% in two months without updates. That decay remains invisible unless the site is instrumented for it, and by the time it appears in a monthly report the position is usually gone.

Independent freshness data points in the same direction. Brands whose content ages past roughly 90 days without updates see measurable drops in AI citation frequency. Content freshness accounts for 40% of Perplexity’s ranking signal, and pages under 30 days old receive 3.2× more citations than older content.

The fix is an impression-decay tripwire wired to Search Console signals that auto-queues an update when a page starts falling. In AI Growth Agent, this runs passively. The tripwires fire and updates queue without anyone auditing a spreadsheet. The system executes several autonomous actions per day via AI Growth Agent, combining new articles with updates to existing ones. The result is self-healing content that repairs itself on a loop instead of waiting for a quarterly audit.

See the tripwire system in action. Book a demo with Arjun Karnik.

Step 6: Replace Rankings with Citation and Share-of-Answer Metrics

Measurement must follow the buyer, not the legacy interface. Rankings measure a surface the buyer increasingly skips. The correct metrics are citation rate, share of voice in AI answers, and AI referrer behavior in analytics.

Citation rate is the share of test queries where an AI engine cites the domain. Calculate it as cited responses divided by total responses tested, measured per platform against a fixed query panel. Share of voice measures how frequently the brand is included relative to competitors across the same prompt set. ChatGPT mentions brands 3.2× more often than it provides clickable citations, so track both linked citations and unlinked brand mentions separately.

In GA4, create a custom channel group that matches referral sources including chatgpt.com, perplexity.ai, gemini.google.com, and claude.ai. This step prevents AI referral traffic from being misattributed as direct. AI search visitors convert up to 23 times better than organic search visitors, per Ahrefs research from June 2025. Whatever is measured is a floor, not a ceiling, because buyers often copy an answer and type a brand name directly into the browser, which lands as direct traffic and never gets attributed.

Step 7: Run Defensive GEO to Correct Wrong Model Answers

Defensive GEO protects against inaccurate or hostile answers that already exist in models. The visibility audit in Step 1 surfaces not only gaps but also inaccuracies. A wrong AI answer hurts more than no answer, so Defensive GEO corrects the record before growth work compounds on a false foundation.

Brand sentiment in AI answers is unstable and flips approximately 6.7 times more often than whether a brand is mentioned at all. Sentiment monitoring functions as a risk control, not a vanity exercise. Defensive GEO runs in parallel with growth work and is revisited on a cycle, because model answers change as training data and retrieval indexes update.

The governing principle is simple: once a model has a settled answer for a category, that answer becomes sticky. By 2026, brand visibility in search depends less on page position in ranked results and more on whether a brand is cited within AI-generated responses. Early citations become tomorrow’s record. Correcting the record now costs less than correcting it after incumbency hardens.

Frequently Asked Questions

How long until citations appear after the checklist is complete?

Coverage and impressions typically appear within weeks of publishing structured, buyer-language-aligned content. Citations in ChatGPT, Perplexity, and Google AI Overviews generally follow within one to three months. Compounding, where topical authority accumulates and citation rates rise across a broader query set, usually begins after month three.

On Arjun’s own site, new articles reached thousands of monthly Google impressions within weeks. The GEO subfolder went from zero to the only source of new impressions on the domain in 60 days. These figures come from his own Search Console data and do not guarantee any specific outcome for another site.

How do I measure success without traditional rankings?

Success in GEO uses a three-layer measurement stack. The first layer is citation rate per platform. Run a fixed set of 20 to 30 prompts across ChatGPT, Google AI Overviews, Perplexity, and Gemini monthly, and record how often the brand appears as a cited source.

The second layer is share of voice in AI answers. Calculate how often the brand is mentioned relative to competitors across the same prompt set. The third layer is AI referrer behavior in GA4. Segment traffic from chatgpt.com, perplexity.ai, gemini.google.com, and claude.ai as a distinct channel and track conversion rate separately from organic. Google Search Console remains the instrument for impressions and decay curves.

The honest caveat remains that buyers frequently copy an AI answer and type a brand name directly into the browser. Measured impact therefore understates real impact, and every figure should be treated as a floor.

Is this just SEO with a new name?

GEO and traditional SEO target different systems, so the mechanics diverge. Traditional SEO focuses on rankings on a human-readable list, where authority comes from backlinks and domain authority, and the query being optimized against is the one the buyer typed.

Generative engine optimization focuses on citation inside a machine-generated answer. Authority comes from topical coverage and structured specificity, and the queries being optimized against are the hidden fan-out queries the buyer never sees. The success metric is share of answer, not rank position. The winning content format is the one the retrieval layer can parse, attribute, and quote, not the one that earns the most backlinks.

What happens if I skip the technical plumbing step?

Skipping technical plumbing means every downstream investment is spent on content the machine cannot access. If AI crawlers are blocked in robots.txt, if core content only exists after JavaScript renders, or if schema is absent, the retrieval layer cannot read the site regardless of content quality.

The schema error rate mentioned earlier, where 60–70% of sites have missing or broken structured data, illustrates why this step cannot be skipped. Roughly 7% of sites also block at least one major AI crawler without realizing it. When plumbing is ignored, the fan-out query mapping, buyer-language rewrites, and freshness loop all operate on a site that remains invisible to the systems being targeted. Technical plumbing is fixed first because nothing else can work without it.

Conclusion: Run the Seven-Step GEO Checklist on Your Own Data

The seven steps above match the exact sequence Arjun Karnik runs in his public test lab via AI Growth Agent. The method is self-verifying: ask an AI assistant about generative engine optimization and see who gets cited. The same system being documented is what produces the visibility, so the receipts function as both proof and method.

The window for outsized gains is open now and resembles the early SEO era. Eighty percent of LLM citations do not rank in Google’s top 100 for the original query, which means the path into AI answers does not run through rankings. It runs through plumbing, mapping, and freshness, executed at machine cadence, measured in citations, and corrected in real time when the answers go wrong.

The checklist is self-contained. Run it in order. Measure citations before and after each step. Publish the misses alongside the wins, because specific, dated, first-person, verifiable content is exactly what the retrieval layer rewards.

Book a demo to see how AI Growth Agent runs this diagnostic at machine cadence on your site.