Cannibalization Intelligence Engine – Structural Similarity & Risk Modeling Platform_

Project Overview

Content cannibalization is rarely visible through keyword overlap alone.

Pages may target similar intent structures without sharing exact queries — leading to internal competition, diluted authority, and ranking instability.

Traditional SEO tools measure keyword positions and traffic. They do not model structural semantic similarity between pages.

The Cannibalization Intelligence Engine was built to solve that gap.

It ingests exported page-level data, models semantic similarity using TF-IDF vectorization, computes deterministic risk scores, and visualizes structural conflict patterns through clustering and interactive dashboards.

This is not a keyword overlap tool. It is a structural similarity intelligence engine.

What It Does

The system ingests:

• Page-level exported data (e.g., Google Search Console CSV)
• URL structures and associated queries
• Page-level traffic and impression data

Then computes:

• Global Risk Index (0–100)
• Page-Level Risk Scores
• Pairwise Similarity Matrix
• Conflict Severity Scores
• Cluster Density Modeling
• Structural Uniqueness Index
• Risk Distribution Visualization
• Actionable Consolidation Recommendations

Every metric is derived from deterministic backend modeling — the frontend only renders computed results.

No synthetic demo metrics. No client-side scoring logic.

Core Capabilities

• Semantic Similarity Modeling

  Generates TF-IDF page vectors and computes cosine similarity between all page pairs to detect structural overlap beyond keyword matching.

• Deterministic Risk Scoring Engine

  Computes cannibalization risk using weighted similarity and structural overlap signals.

• Conflict Severity Modeling

  Identifies high-risk page pairs and ranks them based on conflict intensity.

• Intent Clustering Engine

  Groups structurally similar pages into clusters using similarity thresholds.

• Cluster Density Calculation

  Measures intra-cluster similarity averages to quantify structural cohesion.

• Structural Uniqueness Scoring

  Calculates inverse similarity signals to determine how unique each page is within the site structure.

• Interactive Risk Filtering

  Allows threshold-based filtering of page conflicts using real computed risk values.

• Behavioral Visualization Dashboard

  Displays risk distribution gradient bars, pairwise similarity heatmap, conflict matrix with severity coloring, cluster density indicators, and recommendation panel.

• Local-First Architecture

  Runs entirely on FastAPI + React with no external AI APIs. All modeling logic is implemented server-side.

The Challenge

Modern content strategies generate:

• Location-based landing pages
• Service-specific pages
• Commercial intent variants
• Slightly modified structural templates

These often create structural semantic overlap without obvious keyword duplication.

Traditional SEO tooling cannot detect:

• Structural similarity conflicts
• Intent cannibalization clusters
• High-density semantic redundancy

There was no lightweight, self-hosted system to model cannibalization using deterministic semantic similarity scoring.

The Solution

Built a full-stack structural intelligence engine composed of:

Backend:

• FastAPI scoring API
• TF-IDF vectorization engine
• Cosine similarity matrix modeling
• Deterministic conflict risk scoring
• Cluster density computation
• Page-level uniqueness scoring
• Structured JSON intelligence output

Frontend:

• React dashboard interface
• Tailwind dark-mode UI
• Risk gradient visualization
• Similarity heatmap rendering
• Conflict matrix severity coloring
• Interactive risk threshold filtering
• Cluster density display
• Recommendation panel

The system enforces strict backend authority — similarity and risk scores cannot be manipulated client-side.

Why It Matters

As content scales, structural similarity becomes increasingly difficult to manage.

Websites need to understand:

• Which pages structurally compete
• Where semantic overlap exists
• How dense intent clusters form
• Which pages should be merged or differentiated
• How structural risk evolves as content grows

This engine provides a measurable, deterministic framework for detecting structural cannibalization patterns. It shifts cannibalization analysis from keyword-based observation to semantic modeling.

Future Expansion

• Google Search Console API integration
• Historical cannibalization tracking
• Merge impact simulation modeling
• Internal linking optimization layer
• Structural graph visualization
• Entity overlap detection modeling
• SaaS-ready multi-tenant architecture
• API-based intelligence endpoints
• Version comparison between analysis runs