AI-Powered SEO Audit Engine

Turns a manual SEO QA checklist into a repeatable pipeline that crawls live pages, compares them to the brief with an LLM, and ships actionable fixes.

Step by step, in pictures

See the automation in action — these are demonstration captures of the working project, so you know exactly what you'd get.

1. 1

   The workflow

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   Sitemap discovery → crawl → LLM comparison against the brief.

2. 2

   The trigger form

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   A simple form kicks off the audit — you enter the site and its SEO brief targets, and the engine takes over.

3. 3

   The overall score

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   The engine grades each page against the brief and returns a clear compliance score — so you see exactly where the site stands.

How it works

The problem

An agency manually checked whether published pages matched the SEO brief — titles, meta descriptions, H1s, target keywords — across many client sites with wildly different setups. It was slow, inconsistent, and quietly produced wrong results when a spreadsheet column was reordered.

The approach

1. 1

   A crawler discovers sitemaps robustly across heterogeneous setups — pretty permalinks, query-string sitemaps, and www / non-www DNS variants — so both staging and production sites are covered.

2. 2

   Brief data is read by column header, never by position, and fails loudly if a required column is missing — so silent column drift never yields wrong-but-plausible output.

3. 3

   An LLM compares each live page against its brief and returns structured, prioritized recommendations.

4. 4

   Production-grade reliability: retry-with-backoff for rate-limited APIs, an Opus→Sonnet model fallback, and an in-workflow error sub-flow that emails failures instantly.

Highlights

• A manual QA checklist becomes a repeatable, auditable pipeline.
• Robust to messy real-world inputs: staging sites, reordered columns, DNS quirks.
• Resilient to provider outages and per-minute quota limits.

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