AI Website Automation Lab

What we are exploring

Building and maintaining a high-quality website involves a lot of repetitive, structured work - drafting copy, shaping layouts, generating metadata, keeping content consistent - and this lab asks how much of that AI can safely accelerate. We prototype tooling that drafts on-brand copy, proposes page structure and produces structured data, then study where the time savings are real and where automation quietly introduces errors that a human would not.

The interesting tension is between speed and accuracy. AI can produce a plausible page in seconds, but plausible is not the same as correct, compliant or accessible. So our research deliberately builds in friction at the points that matter: factual claims, regulated wording, semantic structure and performance. The work is experimental, and we expect the balance between automation and human editing to keep shifting as we learn.

Why a human review gate stays mandatory

In every prototype, AI drafts and assembles but never publishes on its own. A human editor reviews accuracy, tone and compliance before anything goes live, and the tooling is designed to make that review easy rather than to bypass it. This matters especially for claims: generated copy can drift into overstatement, invented detail or wording that misrepresents what an organisation actually does, and only a person can reliably catch that.

We treat the review gate as a feature, not a bottleneck to be optimised away. Our experiments include checks that discourage unsupported claims and surface where the model was uncertain, so the editor’s attention goes to the riskiest parts first. The honest position is that AI here is a drafting and assembly aid; editorial responsibility - and accountability for what is published - stays with the human author.

• AI proposes copy, structure and metadata; it never publishes unattended.
• Editors check factual accuracy, tone and regulated wording before go-live.
• Checks discourage unsupported or exaggerated claims in generated copy.
• Model uncertainty is surfaced so review effort targets the riskiest parts.
• Final editorial responsibility and accountability remain with the author.

Keeping performance and accessibility first-class

A fast, accessible website is not an afterthought to be bolted on once content is generated, so our research treats performance, accessibility and clean markup as constraints that automation must respect from the start. We explore how generated pages can be assembled with semantic structure, explicit dimensions and lean output, rather than the bloated markup that naive generation tends to produce.

This is exploratory engineering, and we are careful not to overstate it. A prototype that assembles a performant page in testing is evidence that the approach is promising, not a guarantee that every generated site would hit a given score in the wild. Where automation cannot reliably meet the bar, we keep that step manual, because a slightly slower build is preferable to shipping something that quietly degrades the experience.