SEOGEO Audit Kit

Get found on Google. Get quoted by AI. SEO and GEO (AI search) audits for programmatic sites, reported per page type.

Try it live → · Sample report · How it works

A programmatic site has thousands of pages but only a handful of templates. SEOGEO Audit Kit finds the templates behind a sitemap, checks a few representative pages from each, and tells you what to fix — with how many pages each fix covers. A 1.3M-page site costs about $0.20 of Claude per audit, against ~$14,900 to send every page through an LLM.

Report overview	Where to start

Status: live and open source. Full audits run end to end, including Claude checks, AI visibility, crawler logs and evals in CI. Search Console, Bing and AI referral traffic are coming soon. See FEATURES.md.

What it does

sitemap.xml ──► sample URLs ──► detect templates ──► crawl samples ──► checks ──► ranked fixes
                 (fair across     (URL patterns +      (robots.txt,     (SEO, AI
                  every file)      page structure)      rate-limited)    readiness)

Stage	How
Sitemap ingest	Follows sitemap indexes and gzip. Big sites split sitemaps by type (listings__001…180), so URLs are sampled across every file family and weighted back up to estimate pages per template.
Template detection	Signal 1: URL patterns (/buildings/{slug}, {id}, {uuid}), with facet siblings merged. Signal 2: page structure — shallow tag paths plus a heading skeleton ("Reviews of 25 Main St" → "reviews of") — compared with MinHash and clustered with HDBSCAN. URL patterns that hide different layouts are split; siblings sharing a layout are merged.
Crawl	Respects robots.txt and Crawl-delay, paces requests per host, identifies as SEOGEOAuditKitBot.
Technical SEO	Status, sitemap redirects, noindex in sitemap, canonicals, titles and descriptions, headings, thin content, boilerplate share, image alt, lang, hreflang, lastmod freshness, response time.
AI readiness (GEO)	AI-crawler policy (retrieval vs training bots), llms.txt structure, JSON-LD present / parses / has a main entity / required properties / no placeholder values, dateModified freshness, quotable verdict sentence, facts up front.
Scoring	Each finding is measured on samples and scaled to the template's estimated pages; scores are weighted by template size.

The 13 modules

Audit	Connect	Track
Technical SEO · Core Web Vitals · AI readiness · What AI sees (with vs without JavaScript) · Fix generators	Google Search Console · Bing Webmaster · AI referral traffic	AI visibility (citations, share of voice) · AI crawler logs · Monitoring and alerts · Question gaps · GitHub Action and MCP server

The hosted demo runs on Claude and free APIs. Tracking citations in ChatGPT, Perplexity, Gemini and Copilot is built in, using your own API keys when self-hosted.

Architecture

flowchart LR
  UI[Next.js UI] -- POST /audits --> API[FastAPI]
  API -- SSE stage events --> UI
  API --> P[Pipeline]
  P --> F[safe_fetch: SSRF guard, IP pinning]
  F --> W[(Target site)]
  P --> ML[Template detection: URL patterns + MinHash/HDBSCAN]
  P --> C[Checks: SEO, AI readiness]
  P -. next .-> CL[Claude: fact extraction, judge, fixes]

• Safe by default: every user-supplied URL goes through one fetcher that blocks private, loopback and cloud-metadata addresses, re-checks each redirect, and connects to the exact IP it vetted (so DNS rebinding can't slip through). Responses and XML are size-capped; XML is parsed with defusedxml.
• Costs visible: rules and ML run first for free; Claude only reads sample pages. Every stage reports its time and cost.

Run it locally

Requirements: Python 3.12+ with uv, Node 20+ with pnpm.

# API — http://localhost:8001
cd api
uv sync
cp .env.example .env
uv run uvicorn app.main:app --reload --port 8001

# UI — http://localhost:3001
cd web
pnpm install
pnpm dev --port 3001

Or run everything (API, website, Postgres) with Docker:

cp api/.env.example api/.env   # add ANTHROPIC_API_KEY to enable the Claude checks
docker compose up --build      # then open http://localhost:3001

Run an audit from the command line and stream its stages:

ID=$(curl -s -X POST localhost:8001/audits -H 'Content-Type: application/json' \
  -d '{"url":"https://example.com"}' | python3 -c 'import json,sys;print(json.load(sys.stdin)["id"])')
curl -N localhost:8001/audits/$ID/events

Smart sampling and limits

A template's problems repeat on every page built from it, so the audit doesn't need every page. It reads a fair sample of sitemap files across every family, keeps a spread of URLs from each, checks about 5 representative pages per template, and scales the counts back up. The report shows exactly how pages were counted, per sitemap family. On a 1.3M-page site the estimate was within 1% of a full count (1,356,128 vs 1,345,411), and the audit took about 2.5 minutes.

Count every page (on any report, or POST /audits/{id}/count) reads every sitemap file and replaces the estimates with exact counts. It only counts URLs, so the findings don't change and memory stays flat even for millions of pages.

The hosted demo keeps the defaults below so audits stay fast and free. When self-hosting, raise them in api/.env:

This is the beginning of the README. Read the rest at the original. Read the full README ↗