Should AI run your technical SEO review, or just make it easier? Where deterministic checks end and language models earn their place.

One of the easiest traps when adding AI (or an agent) to a process is for it to become:

I’ve been wrestling with this for a few months now. I’d say where I have ended up is trying to go in the opposite direction – at least in certain contexts.

The Chrome extension I’ve been building combines traditional SEO checks, browser data, deterministic analysis, and language models.

I am aware you can effectively do that with MCP servers and agents – it’s almost too easy to audit a site without ever auditing the site itself … or to think you are, at least.

But the goal isn’t to delegate an SEO review to AI outputs; the goal is to make the review easier to perform by an SEO using AI.

That distinction matters, for now at least – it’s been a constant point of tension in my own practice.

This follows on from my previous article, Using Local (AI) Compute To Reduce Reliance On Frontier Models.

Some parts of technical SEO are simple, and will be forever simple – at least if you know what you’re looking for:

We do not need an LLM to discover these things; in fact, most LLMs are likely to write a deterministic check to then reliably capture this data themselves. For a process you are going to repeat time and again, a firm framework rather than a probabilistic token-eating word-predictor IS the better option.

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