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
GPTBot: What It Is and How to Control Its Access
Every AI SEO strategy assumes a site is actually reachable, and GPTBot is the specific crawler determining whether that assumption holds true for OpenAI’s systems. Understanding what it does, and how to deliberately allow or block it, is a prerequisite most content…
ChatGPT SEO: How to Get Cited Instead of Ignored
ChatGPT SEO gets treated as a mystery box by a lot of the industry, when in practice its sourcing behavior is more documented than most people realize, just genuinely different from what classic SEO trains you to expect. How ChatGPT Actually Sources Information…
Conversational Search Optimization: The Basics
Someone types “best running shoes” into Google. The same person asks an AI assistant “I have flat feet and run about 20 miles a week, what shoes should I actually get, and are they worth the extra cost over a cheaper pair.” Conversational search optimization exists…















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
How to Know Which Update Hit Your Site, Reliably
Every unconfirmed dip in the SEO world gets blamed on “an update,” often incorrectly. Knowing how to know which update hit your site, specifically, rather than assuming the most recently discussed one is responsible, is a diagnostic skill worth building deliberately….
Why Did My Traffic Drop After Core Update? A Checklist
The rollout finishes, you check your dashboard, and the graph is pointing the wrong way. Before panicking or making sweeping changes, work through this checklist in order, since “why did my traffic drop after core update” deserves a real diagnosis, not a guess. “Why…
Google API Leak Findings Summary: What Actually Leaked
In May 2024, over 2,500 pages of internal Google API documentation, detailing more than 14,000 potential ranking attributes, became public through what appears to have been an accidental exposure rather than a hack or whistleblower disclosure. This google api leak…
Site Reputation Abuse: Google’s Parasite SEO Crackdown
For years, a well-known SEO tactic involved buying placement on a large, trusted publication’s coupon or review section, letting that domain’s existing authority carry thin third-party content straight to the top of search results. Site reputation abuse is Google’s…
Scaled Content Abuse: Google’s Official Policy Explained
Scaled content abuse gets misread constantly as “Google penalizes AI content,” and that misreading causes a lot of unnecessary panic and equally unnecessary confidence. Google’s actual policy, introduced in the March 2024 spam update, is more specific than that, and…
Algorithm Update Recovery: A Realistic Timeline and Plan
Confirm the Timeline Before Doing Anything Before making a single change, verify a core update actually rolled out during your traffic drop by checking Google’s Search Status Dashboard against your own analytics timing. Core updates typically take one to four weeks to…
Manual Action: What It Is and How to Actually Recover
A manual action is when a member of Google’s Search Quality team, a real person, has personally reviewed a site and applied a penalty for violating Google’s guidelines. Unlike an algorithmic penalty, this is not automatic, and it always comes with a paper trail: a…
Algorithmic Penalty: How It Differs From a Manual
Your traffic drops, you check Search Console, and the Manual Actions report says “no issues detected.” That is not good news, it just means you are dealing with an algorithmic penalty instead of a human-reviewed one, and the two require completely different recovery…
Navboost: Google’s Click-Based Re-Ranking System Explained
For over a decade, Google publicly denied that clicks directly influenced search rankings. Then, during the 2023 US v. Google antitrust trial, Google’s own VP of Search testified under oath that a system called navboost is “one of the most important ranking signals”…
Search Quality Rater: What They Do and Why It Matters
Every few months, someone in an SEO forum claims that a search quality rater personally downgraded their site, and someone else corrects them: raters cannot touch your rankings directly. Both halves of that exchange miss the more useful point. A search quality rater…












AEO Research
AI Overviews Optimization: What Actually Earns Citations
AI Overviews now appear on a meaningfully large and growing share of Google searches, and getting cited inside one requires a different playbook than climbing the traditional ten blue links. AI overviews optimization is close cousin to classic SEO, sharing much of its…
How to Track Brand Mentions in ChatGPT Reliably
You cannot improve what you cannot measure, and knowing how to track brand mentions in ChatGPT reliably is genuinely harder than checking a classic ranking position. Here is how to track brand mentions in ChatGPT with the methods currently available, and an honest…
LLM Rank Tracking Tools: What’s Actually Available Now
Classic SEO has Search Console and two decades of rank tracking infrastructure behind it. Measuring visibility inside AI-generated answers has none of that maturity yet, which is exactly why the current crop of llm rank tracking tools matters and why none of them…















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.


















