KatvTech.com
We run Google ranking experiments and publish the results.
KatvTech is an independent SEO research lab. We test ranking factors, AEO citation tactics, and algorithm impacts on real websites. We publish the data. No sponsored opinions. No affiliate-driven recommendations. Just results.
Who we are
KatvTech exists because most SEO advice is based on opinion, not evidence. Our team runs structured experiments across multiple websites, controlling for variables and measuring outcomes over 30, 60, and 90-day periods.
We cover three areas: ranking experiments (does tactic X actually move rankings?), algorithm analysis (what did the latest Google update actually change?), and AEO research (what makes content get cited in AI Overviews and ChatGPT?).
Whether you’re building your first niche website or managing a content portfolio, our experiments give you data to make decisions — not guesswork to act on.
Ranking Experiments
Algorithm Analysis
AEO Research
our mission
KatvTech covers the full ecosystem of Google ranking in 2026, with particular depth in three interconnected areas.
Ranking Experiments
We test specific tactics on live websites — schema markup, heading structures, internal linking patterns, content length, and update frequency — measuring impact on Google Search Console impressions, clicks, and position over defined time periods. Each experiment controls for as many variables as possible and states its limitations clearly.
Algorithm Analysis
When Google releases a core update, spam update, or any confirmed ranking change, we analyse the pattern of winners and losers across publicly available ranking data. We cross-reference Google’s official guidance with observed outcomes to identify what actually changed — not what the SEO community assumes changed.
AEO Research
Answer Engine Optimisation is the emerging discipline of structuring content to be cited by AI systems — Google’s AI Overviews, ChatGPT, Perplexity, and others. We test specific structural and technical factors that influence whether a page gets cited in AI-generated answers, publishing citation rates and the conditions that appear to trigger them.
Our work sits at the intersection of content quality, technical implementation, and AI-driven search where the three forces reshaping how content earns visibility in 2026 and beyond.
Posts from our AEO specialist – Sana Morikofte
An Ecommerce SEO Case Study: What We Tested and Found
Most ecommerce SEO advice comes as a general principle: improve your product descriptions, fix your category structure, add reviews. None of that tells you whether the specific change is worth the engineering time on your specific site. This ecommerce seo case study…
SEO Test Ideas and Cases Worth Running on Your Site
The hardest part of experimentation is rarely the methodology, it is deciding what to test first. A good running list of seo test ideas and cases saves you from staring at a blank page every time you have bandwidth to run something new, and it means your next test is…
How to Run SEO Experiments Without Wasting Months
Deciding to test something is the easy part. Most SEO experiments fail not because the idea was bad, but because the setup was sloppy: too few pages, no real control group, or a measurement window closed the moment the first promising number appeared. If you want a…
about us
KatvTech grew out of a frustration shared by a small group of engineers and developers. We were building websites, reading the same SEO advice as everyone else, and getting the same inconclusive results. Nobody in the industry was testing anything properly. So we started doing it ourselves.
Today KatvTech publishes controlled ranking experiments, algorithm analysis, and AEO research across three content categories. Our team brings backgrounds in software engineering, data analysis, and technical SEO. We work across multiple domains and niches, which gives us a broader testing environment than most independent researchers have access to.
Sana Morikofte – AEO specialist
Sana joined the KatvTech research team in early 2025, bringing a background in computational linguistics and a particular interest in how AI systems parse and extract information from web content. Before focusing on AEO research, she spent three years analysing content performance patterns across e-commerce and publishing sites. At KatvTech she leads all experiments related to AI Overview citation rates, FAQPage schema impact, and question-based content structuring. She has personally tracked over 500 AI citation events across multiple niches and content types, making her one of the more data-grounded voices in a field that is still largely driven by speculation.
Marcus Veltrino – SEO Research Lead
Marcus heads KatvTech’s ranking experiments programme. With a background in software engineering and over a decade spent analysing search ranking behaviour, he designs the controlled testing frameworks that underpin every experiment on this site. His focus is on isolating single variables and measuring outcomes with the rigour that most SEO research lacks.
David Brauksworth – Technical SEO Analyst
David leads KatvTech’s technical SEO research, with a focus on Core Web Vitals, site architecture, and crawlability. His background in web performance engineering means he approaches ranking factors from the infrastructure level up. He runs all experiments measuring the relationship between technical site health and search visibility outcomes.
Algorithm Analysis
Schema Markup for SEO and AEO: The Complete Implementation Guide for 2026
Schema markup is one of the most underused tools in both SEO and AEO strategies. Despite being around since 2011 and supported by every major search engine, the majority of websites still do not implement it correctly — or at all. In 2026, that gap has become a…
Google’s E-E-A-T Framework: How to Build Trust Signals on a New Website in 2026
For most of its existence, Google’s E-E-A-T framework was treated as a concern for health and finance sites. If you were writing about medical symptoms or investment advice, you needed credentials, named authors, and verifiable expertise. If you were writing about…
Google March 2026 Core Update: What Actually Changed and Who It Hit
When Google confirmed the March 2026 core update on March 4th, the initial reaction across the SEO community was the usual mix of panic, speculation, and contradictory advice. Tracking tools registered volatility levels higher than any previously recorded update….
AEO Research
Does llms.txt Actually Work? What the Data Says in 2026
If you have spent any time in SEO communities over the past year, you have seen the llms.txt pitch: add one small file to your website and AI systems like ChatGPT and Google’s AI Overviews will finally understand your content. Some influencer posts go further and…
Internal Linking Strategy for SEO: How to Build Topical Authority From Within Your Site
Internal linking is one of the few SEO levers that is entirely within your control. You do not need to earn it from other websites, negotiate for it, or wait for algorithmic changes to reward it. Every internal link on your site is a deliberate choice — a signal you…
People Also Ask: What It Is and How to Appear in PAA Results
People Also Ask boxes have been part of Google’s search results since 2015, which makes them old by search feature standards. What has changed is their relevance. A feature that was once treated as a minor SEO bonus is now directly connected to the AEO research being…
katvtech.com
We’d love to hear from you—reach out with your questions, ideas, collaborations, or feedback.
FAQs
How does KatvTech run its experiments?
We select a single variable to test, set up control and test conditions on live websites, measure outcomes through Google Search Console over a defined period, and publish the raw data alongside our interpretation.
Are your experiments reproducible?
We describe methodology in enough detail to allow replication. Every experiment states its limitations, including sample size, niche, domain age, and time period.
How often do you publish new experiments?
We publish 2–3 new pieces per month with a mix of new experiments, experiment updates, and algorithm analysis.
Do your experiments apply to all niches?
No, single experiment applies universally. We note the niche type and domain characteristics for each test. Results in competitive niches may differ from results in low-competition ones.
Can I suggest an experiment topic?
Of course, your input is helpful! Just use the contact page. We prioritise suggestions that have clear hypotheses and are practically relevant to content site builders.