Experiment with Google Vertex AI Ranking API: Here's What I Found (Free Script)

For years, we’ve optimized for SERP positions. But what if we could look deeper — to see how semantically relevant a piece of content is according to Google’s own AI models?

I built a custom workflow using:

DataForSEO for SERP + content crawling
Google’s Vertex AI Ranking API to score semantic relevance
My own interpretation layer to map ranking position vs. AI understanding

The result? A practical way to audit what’s ranking, why it’s ranking, and what’s missing.

Read Google’s official blog post here: https://cloud.google.com/blog/products/ai-machine-learning/launching-our-new-state-of-the-art-vertex-ai-ranking-api

google vertex ai ranking api

Case Study: The Query “What is SEO”

Here’s what I found when analyzing that query:

SERP #2 → AI Score: 0.996

Google’s own SEO Starter Guide

Comprehensive
Authoritative
Aligned with user intent

No surprise — it scored nearly perfect.

SERP #5 → AI Score: 0.992

A deeply technical guide buried on page one

High semantic match
Clean formatting
Clear, focused answers

This content deserves to rank higher — the AI knows it, even if the algorithm hasn’t caught up.

SERP #6 → AI Score: 0.145

A top-ranking page with shocking low relevance

Weak content
Likely ranking due to domain authority
Backed by site structure and aggressive internal linking

It ranks high — but not because of content quality. This is where the opportunity lies.

What I Learned from Running 100+ Pages Through AI Scoring

Instead of building “yet another SEO tool,” I used the API to reverse-engineer why bad content sometimes wins, and why great content gets buried.

These were the 3 most important takeaways from my analysis:

1. Weak Content Can Still Rank — If Structure Carries It

I found a page ranking #3 with only 300 words of fluff.

Why did it rank?

47 internal links from the homepage
Clean URL structure
Smart breadcrumbing

Site architecture was doing all the heavy lifting.

2. Google Ranks Passages, Not Pages

When I isolated high-scoring pages, they didn’t rely on length, they relied on precision.

Pages with just 2–3 clear passages (scoring >0.900) consistently outperformed keyword-stuffed, bloated 2,000-word pieces.

“Answer first. Expand second.”

That’s the pattern the AI favors.

3. Internal Linking Drives Semantic Comprehension

Internal linking wasn’t just a crawl signal — it shaped how the AI understood the topic.

What worked:

Contextual links to related content
Descriptive anchor text (not exact-match spam)
Logical topic clusters

What didn’t:

Footer spam
Disconnected silos
Overused keyword anchors

Internal linking = semantic scaffolding.

The Process I Use

Here’s my exact 5-step approach:

Run the analysis — Get Vertex AI scores for top 20 results
Find mismatches — High rank / low score = ranking without merit
Deep dive winners — What makes 0.900+ pages special?
Audit the weak winners — Low-score pages ranking high? Follow the link trails
Execute precisely — Blend quality content with structural SEO

Download the Full Study + Scoring Script

If you’re curious about scoring your own pages or reverse-engineering competitors:

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Connect With Me

I share practical SEO experiments — not recycled advice.
️I publish SEO tools, breakdowns, and findings in public. (Check my GitHub)
I test what others assume.

Follow or connect if you’re into technical SEO backed by data: