Machine Learning in SEO – Implementation Guides
Implementation guides for utilising machine learning in organic search and broader digital marketing projects, showing different ways that the data can support the execution of the project, or improve the organic search visibility strategy. These are guides are typically a follow-up to the Machine Learning Tutorials and Machine Learning Templates and show you the ‘what now’ section, following the execution of a ML API or the training of the model. What to do next, and how to ensure that the insights and data you have gathered is actually properly utilised and incorporated into your strategy or project.
57 posts
- Implementation guides
How to Build a Search Intent-First Website Architecture
A continuous improvement framework for planning your website architecture around search intent: KPIs, personas, page types, internal linking, and a free notebook…
Lazarina Stoy22 Oct 2026 · 26 min - Implementation guides
The Ultimate Guide to PyTrends: the Google Trends API (with Python code examples)
PyTrends is the unofficial Google Trends API for Python. What you can pull, every method with updated 2026 code, how to handle…
Lazarina Stoy22 Oct 2026 · 22 min - Implementation guides
How to Automatically Generate Meta Descriptions with Python: From BERT to LLMs
Generate meta descriptions in bulk with Python: the original BERT extractive method (fixed and explained), plus embeddings, small open LLMs and LLM…
Lazarina Stoy22 Oct 2026 · 22 min - AI search optimisation
Dark AI traffic: why 70% of your AI influence hides as direct
The AI referral line in your analytics is not small because AI is not sending traffic. It is small because most of…
Beatrice Gamba21 Aug 2026 · 9 min - AI search optimisation
Your audience is no longer only human
The search web asked to be found and read. The agentic web asks to be chosen and acted upon. Those are different…
Beatrice Gamba19 Aug 2026 · 8 min - AI search optimisation
Consistency beats completeness: schema for machines, not results
Most structured-data advice optimises for rich results. Agents do not care about rich results. Let's look at what they read, and the…
Beatrice Gamba17 Aug 2026 · 7 min - AI search optimisation
Trust isn’t a schema field: how machines decide to believe you
You cannot declare that you are credible in markup. A retrieval system infers it from signals it collects about you across the…
Beatrice Gamba14 Aug 2026 · 7 min - AI search optimisation
Your robots.txt might be the silent killer of your AI traffic
Agents do not throw errors when your site defeats them. They just leave, and your analytics record a bored human. Let's look…
Beatrice Gamba12 Aug 2026 · 8 min - AI search optimisation
Vagueness is a disqualifier: the three-property sentence test
A page can rank, read well, and still be useless to an AI answer engine. The reason is usually at the sentence…
Beatrice Gamba5 Aug 2026 · 8 min - AI search optimisation
The chunk, not the page: content an AI can actually extract
The page is read by humans. The chunk is retrieved by machines. And a page that works perfectly for a human reader…
Beatrice Gamba5 Aug 2026 · 8 min - AI search optimisation
Your rank tracker is lying to you about AI search
Your positions can be green while AI never cites you. That is not a coverage gap in your tools. It is a…
Beatrice Gamba3 Aug 2026 · 8 min - Implementation guides
Search-Intent Classification with Machine Learning: The Approaches Explained
Search intent is the thing every SEO optimises for and almost nobody classifies systematically. Here are the three families of ML approaches,…
Lazarina Stoy1 Aug 2026 · 15 min










