About SEOcluster.ai

A GSC-based keyword clustering tool built for practitioners

SEOcluster.ai is a founder-built tool designed for people who work with Google Search Console data every day.

It clusters real search queries by intent, identifies keyword cannibalization risks, and translates raw GSC exports into clear target pages and content decisions—without relying on estimated keyword volumes or third-party databases.

The goal is simple: help teams move from "here's the data" to "here's exactly what page to create, optimise, or merge."

Why This Tool Exists

Modern SEO increasingly rewards topic authority, not individual keyword rankings.

Search engines—and AI systems—evaluate whether a site demonstrates depth, clarity, and intent coverage across a subject area, rather than whether it targets isolated phrases.

This shift changes how SEO decisions need to be made:

  • Topic authority requires coordinated page structures, not keyword lists
  • Cannibalization occurs when multiple URLs compete for the same intent
  • Google Search Console reflects real search behaviour, not estimated demand
  • Turning thousands of queries into decisions still requires manual clustering, spreadsheets, and guesswork

SEOcluster.ai was built to remove that friction. It connects directly to Search Console, clusters queries by semantic intent, surfaces overlap and cannibalization, and outputs page-first recommendations with optional AI-assisted briefs—so teams can act with confidence.

What the Tool Does

SEOcluster.ai connects to Google Search Console and:

Clusters queries by intent

Groups queries based on meaning rather than surface-level keywords, revealing how Google interprets search intent across a topic.

Detects cannibalization with click-share analysis

Identifies where multiple URLs split clicks for the same topic, scored by severity using concentration analysis (HHI). Shows exactly which URLs compete and how much traffic each captures — so consolidation decisions are based on data, not guesswork.

Surfaces local intent patterns

Recognises geo-modified queries at scale to support local and multi-location strategies.

Generates target pages

A page-first output showing which pages to create, optimise, merge, or monitor.

Produces AI-assisted content briefs

Optional briefs with suggested structure, headings, FAQs, and schema — optimised for both traditional SEO and AI search engines (GEO). Adapts recommendations based on whether you run a service business, ecommerce store, or publisher site.

Exports client-ready SEO audits

Formatted PDF or DOCX reports showing topic clusters, quick wins, and strategic recommendations based on actual GSC performance data.

The platform is designed for SEO consultants, agencies managing multiple client sites, in-house marketing teams, and local and service-based businesses.

About the Founder

Jimmy Faccioli is a marketing automation and SEO practitioner based in Perth, Western Australia.

He has worked across not-for-profit and commercial environments for over five years, focusing on SEO, analytics, marketing automation, and data-driven website optimisation. His work centres on designing systems that turn raw data into clear decisions—reducing manual effort while improving consistency and confidence.

Jimmy built SEOcluster.ai after repeatedly encountering the same limitation in day-to-day SEO work: Google Search Console provides the most accurate view of search performance, but turning thousands of raw queries into actionable content and site-structure decisions still requires manual clustering, spreadsheets, and subjective judgement.

The project combines applied data analysis, semantic clustering techniques, and automation to help teams move from raw GSC data to clear, defensible SEO decisions.

How SEOcluster.ai Approaches Clustering

SEOcluster.ai applies modern semantic analysis techniques to group Google Search Console queries by intent, not just wording.

Instead of relying on fixed keyword lists or predefined cluster sizes, the system identifies natural topic groupings based on how users actually search. This allows it to:

  • Group queries by meaning rather than phrasing
  • Reveal overlapping intent and cannibalization
  • Detect local and geo-modified patterns
  • Translate query data into page-level actions

AI-assisted generation is used selectively to speed up content planning and briefing, while clustering and prioritisation remain data-driven and transparent.

Ready to try it?

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