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BERT Semantic Interlinker

Use cases

Building topic clusters with contextual internal links Finding orphan pages that need more internal links Improving site architecture based on content relationships Scaling internal linking for large e-commerce sites

Uses Sentence Transformer ML models to analyse page content and find semantically related pages that should link to each other.

Goes beyond URL patterns to understand content meaning, surfacing linking opportunities you'd never find manually.

Award-winning Streamlit App of the Month.

Streamlit App Award Winner Crawl Data

Platform

Browser-based (no installation required)

Input

Screaming Frog crawl CSV or URL/content CSV

Output

CSV with related page pairs and similarity scores

Launch App

Features

  • Sentence Transformer ML embeddings
  • Semantic similarity scoring
  • Bulk page analysis at scale
  • Configurable similarity thresholds
  • Export recommendations to CSV

How to use

  1. 1 Export your crawl data from Screaming Frog or prepare a CSV with URLs
  2. 2 Upload the file to the Streamlit app
  3. 3 Select which columns contain your URLs and content
  4. 4 Set similarity threshold and run the analysis
  5. 5 Download the results with linking recommendations

Want me to run this for you?

I offer this as a managed service. You get the insights without touching the tool.

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Monthly retainers or one-off projects. No lengthy reports that sit in a drawer.

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