Category: SEO

  • AI Search Citation Mining

    AI Search Citation Mining

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    by

    Dan Petrovic
    in ,

    This is the raw data dump from our citation mining pipeline demo on social media. Entered Entities ✅ AEO (10 prompts) ✅ AI Marketing (10 prompts) ✅ AI Optimization (10 prompts) ✅ AI SEO (10 prompts) ✅ AIO (10 prompts) ✅ Answer Engine Optimization (10 prompts) Mining Parameters Available Prompts: 60GPT-5 Citations: 141Gemini Citations: 400Total…

  • TimesFM-ICF

    TimesFM-ICF

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    by

    Dan Petrovic
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    In-Context Fine-Tuning for Time-Series: The Next Evolution Beyond Prophet and Traditional Forecasting How Google’s TimesFM-ICF achieves fine-tuned model performance without training – and why this changes everything for production forecasting systems If you’re reading this, you’ve likely wrestled with time-series forecasting in production. Perhaps you’ve implemented Facebook Prophet for its interpretable seasonality decomposition, experimented with…

  • RexBERT

    RexBERT

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    by

    Dan Petrovic
    in , ,

    RexBERT is a domain-specialized language model trained on massive volumes of e-commerce text (product titles, descriptions, attributes, reviews, FAQs). Unlike general-purpose transformers, it is optimized to understand the quirks of product data and the way consumers phrase queries. For a technical SEO professional, this means better alignment between how search engines interpret product content and…

  • LLM is a Presentation Layer in AI Search

    LLM is a Presentation Layer in AI Search

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    by

    Dan Petrovic
    in ,

    Classic IR: crawl, index, retrieve, rank remain with search engines. There is a persistent myth that large language models (LLMs) have fundamentally replaced search. In truth, LLMs do not crawl the web, do not maintain indexes, and do not enforce ranking algorithms at internet scale. They operate as presentation and reasoning layers on top of…

  • EmbeddingGemma: The Game-Changing Model Every SEO Professional Needs to Know

    EmbeddingGemma: The Game-Changing Model Every SEO Professional Needs to Know

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    by

    Dan Petrovic
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    Why Google’s Latest Embedding Model Could Reshape Search Understanding In the business of Gen AI search optimization, staying ahead means understanding the underlying technologies that power modern search systems. Today, Google has released EmbeddingGemma, a ground-breaking multilingual embedding model that represents a key piece of the puzzle for anyone serious about understanding how Google processes…

  • Introducing Tree Walker

    Introducing Tree Walker

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    by

    Dan Petrovic
    in ,

    Stop Guessing, Start Optimizing. Introducing Tree Walker for the New Era of AI Search The digital marketing landscape is in the midst of a seismic shift. With the rise of AI-powered search engines and generative experiences, the old rules of SEO are being rewritten. Marketers and content strategists are asking the same urgent question: “How…

  • Does Schema Help With “AI”?

    Does Schema Help With “AI”?

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    Dan Petrovic
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    This test is designed to show whether Open AI’s browsing tool does a better job at supplying their model GPT-5 with grounding context from a page with schema. We took the exact HTML from the original experiment here, stripped off the “experiment” from the title and header and uploaded here and here and then ran…

  • What does an SEO do in 2025?

    What does an SEO do in 2025?

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    by

    Dan Petrovic
    in

    Modern search engines are still fundamentally based on information retrieval, but they’re now powered by two distinct layers of AI augmentation: a strategic Agentic Layer and a user-facing Interpretative Layer. The Agentic Layer The Agentic Layer acts as the engine’s strategic decision-maker. This layer, which involves multiple systems and models, determines how to best fulfill…

  • GPT-5 Made SEO Irreplaceable

    GPT-5 Made SEO Irreplaceable

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    by

    Dan Petrovic
    in ,

    OpenAI’s latest model is trained to be intelligent, not knowledgeable. Wait, what? Yup. You read that right. Here’s an example: Now, you may think this is some pretty esoteric knowledge not broadly relevant to most end users and you’re right. But here’s a tiny, open source model from Google, Gemma 3 4B, just knowing this…

  • Dynamic per-label thresholds for large-scale search query classification with Otsu’s method

    Dynamic per-label thresholds for large-scale search query classification with Otsu’s method

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    by

    Dan Petrovic
    in , ,

    Solving the “Which Score Is Good Enough?” Puzzle The real-world problem Arbitrary label search-query intent classifiers spit out a confidence score per label.On clean demos you set one global cut-off say 0.50 and move on.In production: Manual tuning per label quickly turns into a never-ending whack-a-mole, especially when the taxonomy is customized client-by-client (e.g., SaaS…