AI & Searchstarter

Title/Slug Relevance Scorer

Score existing pages against ChatGPTs fanout queries

What it does

Title/Slug Relevance Scorer is the fast, URL-based counterpart to the Fanout Query Extractor. Instead of starting from a blank prompt, you point the tool at an existing page. We fetch the <title> tag and slug via our SSRF-safe fetch-url function, simulate fanout queries via Perplexity Sonar Pro + Gemini in parallel, and compute cosine similarity between each field (title + slug) and each fanout query. Output: composite score (0-100), per-field score, coverage breakdown, and concrete AI-generated rewrite suggestions for both title and slug. Designed for SEO managers who want to benchmark and optimize ten pages in an hour.

The Problem

You have hundreds of indexed pages — but no fast way to know which titles and slugs are misaligned with AI fanout queries.

• Title and slug are the primary citation signals for AI models (Ahrefs study)
• Manual audit per page takes 20+ minutes — not scalable
• Existing SEO tools score against keywords, not against AI sub-questions
• You don't know which pages to prioritize for rewriting

The Solution

A 30-second audit per page: composite score + per-field score + concrete rewrite suggestions.

• Auto-fetch title and slug from your URL via SSRF-safe fetch-url
• Reuses the full fanout pipeline (Perplexity + Gemini + embeddings)
• Composite score weights title (40%) + slug (30%) + coverage (30%) — calibrated to Ahrefs
• Concrete AI-generated rewrite suggestions for both title and slug with rationale
• Coverage matrix per fanout query: which the title covers, which the slug covers, where gaps are

Features

• Composite score 0-100: One number that tells you how well the page matches AI fanout queries. >65 = strong, 45-65 = OK, <45 = needs work.
• AI rewrite suggestions: Concrete suggestions for both title and slug with rationale — not generic advice, but ready-to-paste alternatives.
• Coverage matrix per query: See exactly which fanout queries your title covers, which your slug covers, and where you have gaps.
• SSRF-safe fetch: Our fetch-url Edge Function only fetches public HTTP(S) URLs and blocks internal addresses — safe to use on any URL.

How it works

1. Paste the URL: Any public page — your own or a competitor's. We fetch the title and slug automatically.
2. Set the seed keyword: The keyword you want the page to rank for in AI search.
3. We simulate the fanout: Same pipeline as Fanout Query Extractor — Perplexity Sonar Pro + Gemini in parallel + embeddings.
4. Read the score and rewrites: Composite score 0-100, per-field score, coverage matrix, and concrete rewrite suggestions for title and slug.

FAQ

How is the composite score calculated?

Title 40% + slug 30% + coverage 30%. Title and slug scores are average cosine similarity to top fanout queries. Coverage is the share of fanout queries with similarity ≥0.65 (well covered). Weighting is calibrated to the Ahrefs study where title and slug had the strongest individual citation correlation, while coverage breadth determines overall AI visibility.

What if the title tag is missing?

We fall back to scoring slug only. The composite then weighs slug 60% + coverage 40%. The tool still gives a meaningful score, but you should fix the missing title tag — it's the strongest single SEO signal for both Google and AI models.

Why fetch the URL instead of asking me to paste the title?

Two reasons: speed (one click vs three) and accuracy (auto-fetched title is always what Google and AI models actually see). Saves you 30 seconds per page and prevents copy-paste errors. We use a SSRF-safe fetch that only allows public HTTP(S) URLs.

How many credits does it cost?

15 credits per page: 1× Perplexity Sonar Pro + 1× Gemini + 1× Gemini Lite for rewrite suggestions + up to 17 embeddings (title + slug + 15 queries). For multi-page audits, use the Bulk Scorer (12 credits per URL — 20% bulk discount).

Try Title/Slug Relevance Scorer free