SEO Intelligence System / Q4 2025
An SEO Brain That Prevents Cannibalization Before You Publish.
Semantic search across every page, product, and keyword so new content finds its slot, not its competition.
// The Problem
The company's content team was publishing blog posts without checking whether new articles competed with existing ones for the same keywords. Content cannibalization was invisible: two articles targeting similar terms would split ranking authority, and neither would reach page one. There was no system for knowing what the site already covered, what it ranked for, or where the gaps were.
// The Build
I built a Python-powered SEO intelligence platform backed by PostgreSQL with the pgvector extension for semantic search. The scraper indexes every page, blog post, and product on the site (150+ items) into vector embeddings.
Before publishing new content, the system checks for keyword overlap and cannibalization risk by computing cosine similarity against the entire existing corpus. It returns interlinking suggestions (related articles that should cross-reference the new piece), gap analysis (topics with search demand but no existing coverage), and a pre-publish validation report.
Google Search Console integration is planned to add real ranking data: identifying keywords with high impressions but low click-through rates, signaling content that ranks but does not convert and needs optimization.
// outcome
Zero content cannibalization on new publishes. Every article gets an interlinking map before it goes live.
40+
Pages indexed
141
Products mapped
0
Cannibalization incidents
1
Semantic engine
// NDA note
This project was completed under NDA. Full narrative with technical detail, trade-offs, and sanitized artifacts available under your own NDA. Contact directly to request the long form.
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