Berlin, Germany, June 26, 2026 (GLOBE NEWSWIRE) — Peec AI, the AI search analytics platform, today announced the research team it has assembled to answer the question every brand is now asking: why do ChatGPT, Perplexity, and Google’s AI Mode recommend some companies over others – and how can a brand earn its place in those answers?
Former enterprise SEO leaders, an explainable-AI researcher, and the engineers behind some of the market’s earliest tools are reverse-engineering AI search, feeding what they learn straight into a product built to help clients earn visibility there.
As AI answer engines replace the old list of blue links, the rules for which brands get recommended now sit inside models with no published rulebook – and no one outside those companies knows exactly how the choices are made. Peec AI’s response: hire the people who work those rules out by experiment, pairing two decades of enterprise SEO with hands-on reverse-engineering of answer engines and machine-learning research into how models decide.
That research flows straight into the product. Peec AI tracks how brands appear across the major answer engines – measuring their Generative Share of Voice against competitors, sentiment, and the sources each engine cites. Those signals become a prioritized set of recommendations that show teams how to increase their visibility in AI search engines.
Decoding the black box
Much of that work is led by Metehan Yeşilyurt and Tomek Rudzki. Yeşilyurt reverse-engineers the algorithmic behavior of AI search and answer engines without touching the underlying models’ source code. Instead, he runs controlled technical experiments, analyzes API network traffic, and reads client-side configurations to infer how platforms decide which web content to surface and cite.
Yeşilyurt’s findings, which he publishes openly on his own blog, have helped shape the emerging fields of Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) for brands, in-house teams, and marketing agencies. By analyzing ChatGPT’s web interface and network traffic, he decoded how the assistant decides which sources earn an inline citation, surfacing evidence that the behavior reflects a Reciprocal Rank Fusion approach that aggregates and scores content across multiple subqueries. In late 2025 he published a detailed breakdown of Perplexity AI, documenting more than 59 distinct ranking factors and patterns, among them the platform’s reliance on a shortlist of top-tier sources, semantic relevance to the query, content freshness, and rapid user-engagement signals. He has applied the same method to Google Discover and Google’s AI Mode, mapping the multi-stage content pipelines and freshness “buckets” that appear to govern which pages surface.
Tomek Rudzki has been decoding generative search engines since before the field even had a name. Prior to joining Peec AI, Rudzki was a co-founder of ZipTie.dev – the first commercial tool (early 2024) dedicated to monitoring brand visibility in AI search engines. Notably, the tool tracked Google AI Overviews from its earliest days under the “SGE” banner, back when it was restricted to logged-in users within Google Labs. In this role, Tomek was directly responsible for designing and building the module that optimized content for large language models, giving brands a tangible way to influence whether and how AI systems cited their sources. His responsibilities included building an LLM-answer sentiment analyzer and a prompt generator.
That same hands-on understanding now powers Peec’s AI-visibility recommendations – so a brand sees not just how it’s performing across AI answer engines, but the specific change most likely to grow its visibility.
That instinct runs deep. Rudzki’s engineering thesis at Politechnika Opolska was titled Empirical Analysis of the Google Search Engine Algorithm, and for his 2019 computer-science master’s he built software that improved rankings in traditional search engines using SEO crawl data, server logs, and Google Search Console signals. He has spoken at industry conferences including BrightonSEO and SMX.
Built on deep SEO foundations
That experimental work is grounded by people who ran Google organic search at scale long before LLMs entered the picture.
Malte Landwehr brings more than 20 years of enterprise SEO and SaaS product leadership. He previously served as VP of SEO at idealo, one of the world’s largest price-comparison platforms, where he spent five years leading organic growth and doubled Google organic traffic, and as VP of Product at Searchmetrics, a globally recognized enterprise SEO software suite. He left a senior corporate role to help build Peec AI – the product he had concluded the market was missing.
David Konitzny brings a technical-SEO and international-search perspective shaped on both sides of the table – agency and in-house. He started out as a technical SEO at Peak Ace, one of Germany’s most renowned agencies, before becoming Head of Organic Search at Raisin, a German fintech unicorn, where he built multilingual, multi-regional strategies across international markets. When he returned to agency work at KKP, that in-house experience paid off: he already understood the pain points facing insurance and banking clients, and how to solve them. KKP is also where AI search became his focus – and the approach he developed there is the one he brings to Peec: not just optimizing a client’s domain, but educating the whole marketing team and giving larger organizations a clear roadmap to follow. He pairs that technical depth with commercial instinct, and today uses browser developer tools and other hands-on methods to read the signals that reveal how LLM systems behave under the hood.
From research to actionable recommendations
Translating those discoveries into working software falls in large part to Dr. Melissa Fasol, one of the core developers on the Peec AI platform. She earned a PhD in computational neuroscience at the University of Edinburgh, specializing in Explainable AI, signal processing, and machine learning – work focused on exactly the problem AI search presents: making the decisions of complex, opaque systems transparent and trustworthy.
Before Peec AI, she founded Tulia AI a startup that helps brands understand why LLMs recommend certain companies over others – work for which she was shortlisted for the Investec Early Stage Entrepreneur of the Year Award. That approach aligns closely with Peec’s mission, and at Peec she helps turn the team’s collective research into the platform’s Actions module – ranked, opportunity-scored recommendations on what a brand should do next to improve its AI search presence.
“Anyone can measure brand visibility. The hard part is giving recommendations that actually move the needle and can be scaled in a sustainable way,” said Malte Landwehr, Chief Product Officer of Peec AI. “Many tactics that currently work well for short-term gains in AI visibility will stop working in a year. Some even put your whole SEO visibility at risk! To avoid that, you need people with deep SEO experience who are also able to deconstruct how LLMs work.”
About Peec AI
Peec AI is an AI search analytics platform that helps marketing teams and agencies monitor, benchmark, and improve how their brands appear across AI answer engines. With comprehensive coverage of LLM platforms – including ChatGPT, Perplexity, Gemini, Google AI Overviews and AI Mode, Claude. Founded in January 2025 by Marius Meiners, Daniel Drabo, and Tobias Siwonia, Peec AI is backed by Singular, 20VC, and Antler. Learn more at peec.ai
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