My career started by manually combing through server log files. I wanted to understand how Googlebot crawls websites. That fascination with the technical side of discoverability? Never faded.
At Kobalt, I translate complex protocols and standards into practical implementations. Websites that are discoverable not just in Google, but also in ChatGPT, Perplexity and Gemini. My approach is always the same: measure first. Then optimize.
I believe the shift toward AI-driven search experiences is the biggest change in online discoverability since the introduction of mobile search. That is a big claim. But I stand by it.
Outside of work, I play complex strategy board games (Terraforming Mars is a favourite), I brew my own beer with the same precision I use to validate structured data, and I spin vinyl while reviewing code.
My best work happens somewhere between ten at night and two in the morning. Post-rock in the background. Homebrewed pale ale within reach. Not for everyone. Works for me.
A Dutch webshop in sustainable products tripled its AI citations in eight weeks. Discover how structured data, product schemas and FAQ content made the difference.
A mid-size B2B company improved its AEO score from 30 to 85 in twelve weeks. Learn which steps they took, which mistakes they made and how you can achieve the same results for your website.
Manually adding Schema.org markup to every page takes hours. Discover how AI tools automate this process, which tools are available and how to ensure the quality of automated structured data.
Vector embeddings form the foundation of how AI models understand, compare and rank content. Learn how this technology works and why it matters for your content strategy.
Search engines and AI models increasingly understand what you mean, not just what you type. Semantic search is changing the rules for content optimization. Learn how to write content that scores on meaning.
Not all content is cited equally by AI models. There are clear patterns in which type of content, structure and tone of voice is preferred. Learn how to analyze these patterns and align your content strategy accordingly.
AI models use knowledge graphs to recognize and distinguish entities. Those who do not explicitly disambiguate risk confusion with namesakes. Learn how to make your brand or person unambiguously identifiable for AI.
Who are your competitors in AI answers and how do you perform relative to them? This article describes a step-by-step framework for competitive analysis focused on AI visibility, including tools, metrics and strategic conclusions.
Which tools do you need to successfully implement and measure AEO? This article compares the best AEO tools of 2026, from citation trackers to Schema.org validators, including pricing and practical experience.
ai.txt is a new proposal that gives website owners a standardized way to communicate instructions to AI systems. From training consent to citation preferences: discover how ai.txt works and why it could become the successor to robots.txt for AI.
TDM headers (Text and Data Mining) give you legally enforceable control over how AI models use your content. Learn how to implement the TDM Reservation Protocol, which licensing models are available and how to protect copyright in the AI era.
Well-known URIs form the discovery layer of the modern web. From robots.txt to ai.txt and from security.txt to agent-card.json: discover which well-known paths are crucial for your AI visibility and how to implement them correctly.
