← Back to case studies Case Study ยท Product

Revisi

An LLM-powered brand voice auditor that reads a website the way a copy chief would, every page, in a couple of minutes.

python llm fastapi
revisi.ai/report
Revisi project screenshot

Revisi turns a vague worry, "our website doesn't sound like us anymore", into a specific, per-page list of edits. Paste in a URL and it crawls the site, scores every page against brand-voice metrics, and writes prioritised recommendations.

01 - The problem

Auditing brand voice by hand doesn't scale

Content and brand teams can feel when a site has drifted off-voice, but proving it is slow, subjective work. Reading tone page by page does not scale past a handful of URLs, and it rarely ends in a fix anyone can action.

The teams who most need a consistent voice, smaller companies without a dedicated brand editor, are exactly the ones who cannot spare days to read their own website like a copy chief.

02 - What I built

A per-page audit that ends in concrete edits

Revisi crawls the site, scores each page against a set of brand-voice metrics, and turns every gap into a specific rewrite. The output is a per-page report: what is working, what is off, and the edit to make.

03 - Walkthrough

From a pasted URL to a prioritised report

04 - Outcomes

What it delivers

~2 min

from a pasted URL to a full per-page audit

100%

of pages crawled and scored, not a sample

1 line

paste a URL, no install or integration

Live

shipping in production at revisi.ai

05 - What I learned

What I'd carry forward

Voice is a rubric, not a vibe

Explicit, named metrics made the model's recommendations more consistent than open-ended "improve this" prompts.

Prioritisation is the product

The ranked "fix these first" list mattered more to users than the raw scores.

Cache the crawl

Re-auditing after edits should be cheap, with only changed pages re-scored.

Bring your own voice

Teams want to define their own tone rules, so a custom rubric is the next natural layer.

Built with

A small, async Python stack: FastAPI serving the crawl-and-score pipeline, the Anthropic API generating the recommendations layer, and server-rendered reports so a result is a shareable link rather than a session.

python fastapi anthropic api async crawler server-rendered reports

Keep exploring

See the rest of the case studies

← Back to case studies