About me
“Ranking and visibility are no longer the same thing. I measure the gap – and close it.”
I'm Eugen Ullrich, and I work where buying decisions are made today: inside the answers of ChatGPT, Perplexity, Gemini and Claude. As a GEO & AI Visibility Strategist, I make sure brands get named and cited there – on a foundation of technical SEO since 2016, up to platforms with 20 million pages. I work remotely, for clients across DACH, Europe and the US.
Since 2016, I have worked at the intersection of classic search and AI answer engines. I started in technical and e-commerce SEO. What has shifted since is where the buying decision begins: more and more often inside an AI answer, not in the list of results. Ranking and visibility are no longer the same thing. I measure the gap between a Google position and an AI citation, and I close it.
Outcome, method, foundation
What clients buy from me is AI visibility. I keep its signals separate: whether a brand is mentioned, considered, recommended or supported by a visible source citation when someone asks an AI about the category. The path there is GEO, generative engine optimisation – the method built from extractable answer structures, clean entities, Schema.org and original data that a model likes to cite. Beneath it sits the foundation: technical SEO. What an AI crawler can't reach, render and parse, it can't cite. That is why the years since 2016, spent doing exactly that, still count.
That ranking and citation have come apart is not a claim. Ahrefs analysed roughly 15,000 search queries across ChatGPT, Gemini, Copilot and Perplexity: only about 12 percent of the URLs these chat engines cite rank in Google's top 10. For Google's own AI Overviews, the link is tighter, where about 94 percent of answers cite at least one page from the top 20. So I deliberately say “chat engines,” not “AI” in general, and I measure per engine. Look only at your Google position, and you can be invisible in ChatGPT, Perplexity or Claude without noticing.
What I focus on
My approach is “GEO first, SEO as the foundation,” and it stays evidence-first throughout: measure, prioritise, implement. I capture the separate prompt-level signals for each defined measurement surface, state their denominators and limitations, sort the gaps by impact and effort in a prioritization matrix, and build content and technology so that language models find, understand and cite the right facts about a brand. No public black-box score, no generic best practices, no assumptions without a data basis.
Background
In the years since 2016, I have handled technical and content SEO for more than 100 websites and learned early to tie visibility firmly to revenue. That breadth is the foundation I now build GEO on.
Enterprise & big data
+60 % traffic with better lead quality: for a multilingual platform of roughly 20 million pages with a strict crawl budget, I owned the technical SEO strategy through advanced indexation controls and a clean site architecture. Projects like that are where crawl-budget management, log-file analysis and Python automation prove whether they actually hold up when a large site needs to be fully captured.
E-commerce & internationalization
+50 to 70 % monthly revenue on average across several niche projects: in e-commerce, I have advised on a freelance basis since 2017, from market strategy through technical solutions to operational workflows. In a larger engagement, I owned the SEO and marketplace strategy for entering the German market, including competitor research, category selection against commercial criteria, and legal and regulatory analysis (EU product safety, packaging, VAT/OSS/ProdSG). For premium brands, I have run end-to-end programs combining SEO and Google Ads across multilingual domains, backed by GA4 and Looker Studio dashboards. I also built my own e-commerce brand in the yoga segment up to a self-sustaining operation.
Leadership & provable results
+100,000 € monthly revenue for an industrial client through margin-focused optimisation of high-value product categories. As SEO Team Lead, I moved a five-person team from “busy work” to KPI-driven, ROI-measurable execution and cut non-productive tasks by around 40 percent; in a digital transformation and operations project, I led international teams, defined KPIs, and reduced costs and process bottlenecks by around 30 percent through unified procedures (SOPs). In an earlier role, I managed more than 50 SME clients in parallel – the method grows with complexity.
How I work
I stick to a simple principle: flexible data collection, traceable and deterministic metrics. Every hypothesis traces back to prompt-level data. The real win is not traffic but the probability of being named in the buying process at all. Because if you don't appear in AI answers, the vendor shortlist is decided without you.
The Lab contains runnable tools and workflows. Open methods, data boundaries and research notes live separately under Research.
At a glance
Focus areas: Generative Engine Optimization (GEO/LLM), technical SEO and big data, e-commerce and internationalisation.
Tech stack: Peec.ai, Screaming Frog, Sistrix, Ahrefs, Python, SQL, Looker Studio, Shopify, Magento, ChatGPT/Claude.
Stations: AN Digital, ABRAMS World Trade Wiki, LOUIS INTERNET, Würth Group, WDD Dr. Faltz & Partner, Semtrix, 11880.
Selected client brands: Braun Büffel, Jowat, Abus, Caritas Deutschland.
Certifications and education: Certified E-Commerce Consultant, Advanced Search Engine Optimization, E-Commerce Management (sgd).
Contact
Planning a GEO or SEO project and need someone who brings technology and strategy together? Write to me directly: [email protected]. You'll also find me on LinkedIn.
Entity Map
This map names the entities around my work and how they relate, so AI systems find the right facts and cite the right source. Each entry gives a description, its relations to other entities, and one piece of evidence with a source. Last updated July 2026.
Eugen Ullrich — Person
Independent GEO & AI Visibility Strategist, an individual, not an agency. He makes brands visible and citable the moment buyers ask ChatGPT, Perplexity, Gemini, Claude or Google AI Overview for a recommendation.
Defined by: Eugen Ullrich (eullrich.com)
Relations: ROLE → GEO & AI Visibility Strategist; WEBSITE → eullrich.com (DE `/`, EN `/en`); DELIVERS → AI Visibility; METHOD → GEO; FOUNDATION → Technical SEO; MEASURES_WITH → Peec.ai; SERVES → DACH, Europe, US; IS_NOT → agency, software vendor, tool; PROFILE → LinkedIn, Xing
Evidence: “I make your brand visible in AI answers.” — Eugen Ullrich — home, eullrich.com/en, status July 2026.
AI Visibility — Outcome
The outcome clients buy: being cited and recommended in AI answers the moment someone asks an AI for a recommendation in their category.
Defined by: Eugen Ullrich (eullrich.com)
Relations: OUTCOME_OF → GEO; MEASURED_AS → LLM visibility, citation rate; GOAL → inquiries and revenue
Evidence: “Optimization to get cited and recommended in the answers of ChatGPT, Claude, Gemini and Perplexity.” — Eugen Ullrich — home, eullrich.com/en, status July 2026.
GEO (Generative Engine Optimization) — Method
The method that leads to AI Visibility: extractable answer structures, clean entities, Schema.org and original data that a model likes to cite.
Defined by: Eugen Ullrich (eullrich.com)
Relations: LEADS_TO → AI Visibility; BUILDS_ON → Technical SEO; INCLUDES → prompt-level measurement, prioritization matrix; APPLIED_IN → GEO audit
Evidence: “GEO first, SEO as the foundation.” — Eugen Ullrich — home, eullrich.com/en, status July 2026.
Technical SEO & Big Data — Foundation
The foundation under GEO: log-file analysis, crawl-budget control, indexation logic and Python automation on projects with millions of URLs, including a platform of around 20 million pages.
Defined by: Eugen Ullrich (eullrich.com)
Relations: SUPPORTS → GEO; ENABLES → crawlability, citability; USES → Screaming Frog, Python, log-file analysis
Evidence: “Log-file analysis, crawl-budget optimization and Python automation, so that Google and AI crawlers fully capture even large sites.” — Eugen Ullrich — home, eullrich.com/en, status July 2026.
E-Commerce & Internationalization — Service
Shop systems such as Shopify, Magento and Shopware, scaling of category and facet pages, hreflang strategies for multilingual markets.
Defined by: Eugen Ullrich (eullrich.com)
Relations: OFFERED_BY → Eugen Ullrich; USES → Shopify, Magento, Shopware; GOAL → revenue
Evidence: “Complex shop systems (Shopify, Magento, Shopware), scaling category and facet pages, and hreflang strategies for global markets.” — Eugen Ullrich — home, eullrich.com/en, status July 2026.
GEO Audit — Service
A check of AI visibility: prompt-level measurement, a technical review of crawler access and structured data, a gap analysis against competitors, and a prioritized action plan. Based on real engine queries, not generic best practices.
Defined by: Eugen Ullrich (eullrich.com)
Relations: PART_OF → GEO; PRODUCES → prioritization matrix; ASSESSES → LLM visibility
Evidence: “Sort gaps by impact and effort in a prioritization matrix. No generic best practices.” — Eugen Ullrich — home, eullrich.com/en, status July 2026.
Prompt-level measurement — Method
Capturing visibility per prompt, separated by engine. The basis for every prioritization; every hypothesis must trace back to this data.
Defined by: Eugen Ullrich (eullrich.com)
Relations: PART_OF → GEO; COLLECTED_VIA → Peec.ai; FEEDS → prioritization matrix
Evidence: “Capture visibility at the prompt level: which brand shows up in which AI answer, and which does not.” — Eugen Ullrich — home, eullrich.com/en, status July 2026.
Prioritization matrix — Framework
Sorts the visibility gaps found by impact and effort, setting the order of implementation.
Defined by: Eugen Ullrich (eullrich.com)
Relations: FED_BY → prompt-level measurement; PRIORITIZES → gaps; PART_OF → GEO audit
Evidence: “Sort gaps by impact and effort in a prioritization matrix. No generic best practices.” — Eugen Ullrich — home, eullrich.com/en, status July 2026.
Citation rate — Metric
Share of retrieved source domains that make it into at least one AI answer. From the own portfolio, on average 76.8 percent for Google AI Overview, 47.7 percent for Perplexity, 40.9 percent for ChatGPT. (Source article in German.)
Defined by: Eugen Ullrich (eullrich.com)
Relations: MEASURES → LLM visibility; COLLECTED_VIA → Peec.ai; FROM → Retrieval-to-Citation study
Evidence: „Gemittelt über die Projekte liegt sie bei 76,8 Prozent für Google AI Overview, 47,7 Prozent für Perplexity und 40,9 Prozent für ChatGPT.“ — Warum Sichtbarkeit in einer KI-Engine wenig über die nächste verrät (German), eullrich.com/blog/retrieval-zitat-funnel/, 19 July 2026.
Engine overlap — Finding
How strongly the cited domain pools of two AI engines overlap. In the own portfolio only 0.12 to 0.21 (Jaccard index) – visibility in one engine says little about the next. (Source article in German.)
Defined by: Eugen Ullrich (eullrich.com)
Relations: FROM → Retrieval-to-Citation study; JUSTIFIES → per-engine measurement
Evidence: „Der Wert bleibt in jedem Projekt zwischen 0,12 und gut 0,21.“ — Warum Sichtbarkeit in einer KI-Engine wenig über die nächste verrät (German), eullrich.com/blog/retrieval-zitat-funnel/, 19 July 2026.
Decoupling of ranking and citation — Concept
Google position and AI citation have come apart: only about 12 percent of URLs cited by chat engines rank in Google's top 10 (Ahrefs). For Google's own AI Overviews the link is tight (about 94 percent, seoClarity).
Defined by: Eugen Ullrich (eullrich.com), based on Ahrefs and seoClarity
Relations: EVIDENCED_BY → Ahrefs, seoClarity; LIMITED_TO → chat engines; NOT_FOR → Google AI Overview
Evidence: about “12 percent of the URLs these chat engines cite” rank in Google's top 10 — Ahrefs, AI Search Overlap, ahrefs.com/blog/ai-search-overlap/, retrieved 3 July 2026.
Peec.ai — Tool
A platform for measuring visibility at the prompt level. A tool Eugen Ullrich uses – explicitly not a product of his.
Defined by: Eugen Ullrich (eullrich.com)
Relations: USED_BY → Eugen Ullrich; MEASURES → citation rate, LLM visibility; IS_NOT → a product by Eugen Ullrich
Evidence: “Peec.ai · Screaming Frog · Sistrix · Ahrefs · Python · SQL · Looker Studio · Shopify · Magento · ChatGPT / Claude” (tech stack) — Eugen Ullrich — home, eullrich.com/en, status July 2026.
Retrieval-to-Citation study — Own data source
Analysis of the own project portfolio: eight anonymized B2B projects from 18 June to 17 July 2026, more than 13,000 retrieved source domains, around 17,000 domain-engine observations via Peec.ai. (Source article in German.)
Defined by: Eugen Ullrich (eullrich.com)
Relations: PUBLISHED_BY → Eugen Ullrich; YIELDS → citation rate, engine overlap; COLLECTED_VIA → Peec.ai
Evidence: „Acht anonymisierte B2B-Projekte, ein Monat vom 18. Juni bis 17. Juli 2026 ... Zusammen mehr als 13.000 abgerufene Quelldomains ... verteilt auf rund 17.000 Domain-Engine-Beobachtungen.“ — Warum Sichtbarkeit in einer KI-Engine wenig über die nächste verrät (German), eullrich.com/blog/retrieval-zitat-funnel/, 19 July 2026.
Productive MCP Connector — Tool
A self-built connector that makes Productive.io data queryable via MCP directly in Claude or another LLM. Saves around $230 in tariff cost per month across ten employees. (Lab page in German.)
Defined by: Eugen Ullrich (eullrich.com)
Relations: BUILT_BY → Eugen Ullrich; USES → MCP; DOCUMENTED_IN → eullrich.com/lab
Evidence: „Der Connector macht diese Daten per MCP direkt in Claude oder einem anderen LLM abfragbar ... Bei zehn Mitarbeitern sind das 230 $, jeden Monat.“ — Lab — Eugen Ullrich (German), eullrich.com/lab/, status July 2026.
GEO Content Engineering (Agent Skill) — Tool (open source)
The GEO method as an executable agent skill: chunk, embed, retrieve, ground, cite. Published under CC BY 4.0. (Lab page in German.)
Defined by: Eugen Ullrich (eullrich.com)
Relations: BUILT_BY → Eugen Ullrich; IMPLEMENTS → GEO; LICENSE → CC BY 4.0
Evidence: „Der Skill macht aus einem generalistischen KI-Agenten einen Spezialisten für GEO-Content.“ — Lab — Eugen Ullrich (German), eullrich.com/lab/, status July 2026.
GEO Share of Voice Monitor — Tool (n8n)
An n8n workflow that measures share of voice, mention rate and citation rate in AI answers without a SaaS subscription, using reproducible regex detection instead of an LLM judge. (Lab page in German.)
Defined by: Eugen Ullrich (eullrich.com)
Relations: BUILT_BY → Eugen Ullrich; MEASURES → share of voice, citation rate
Evidence: „Share of Voice, Mention Rate und Citation Rate in KI-Antworten, gemessen ohne SaaS-Abo ... Die Erkennung läuft per Regex ... damit jede Zahl reproduzierbar bleibt.“ — Lab — Eugen Ullrich (German), eullrich.com/lab/, status July 2026.
FAQ Factory — Tool (n8n)
An n8n workflow that drafts FAQ blocks from third-party page text while hardening against prompt injection using Simon Willison's dual-LLM pattern. (Lab page in German.)
Defined by: Eugen Ullrich (eullrich.com)
Relations: BUILT_BY → Eugen Ullrich; USES → dual-LLM pattern (Simon Willison)
Evidence: „Der Workflow trennt beides nach Simon Willisons Dual-LLM-Pattern: Das steuernde Modell arbeitet ausschließlich auf Enums, Zählern und IDs und bekommt den Seitentext nie zu sehen.“ — Lab — Eugen Ullrich (German), eullrich.com/lab/, status July 2026.
Tracked AI engines — Objects of measurement
The answer engines where visibility is measured and built: ChatGPT, Perplexity, Gemini, Claude and Google AI Overview. Measured per engine, separately.
Defined by: Eugen Ullrich (eullrich.com)
Relations: MEASURED_BY → Eugen Ullrich; DISTINGUISHED_BY → chat engines vs. Google AI Overview
Evidence: “Optimization to get cited and recommended in the answers of ChatGPT, Claude, Gemini and Perplexity.” — Eugen Ullrich — home, eullrich.com/en, status July 2026.