LaunchRanked

Landing page grader

Enter your landing page. We read it like a first-time visitor and check the questions they need answered in the first screen, with a fix for each miss.

Free, no sign-up. AI reads the text of the page (not the design) and answers nine questions a first-time visitor has.

What a first-time visitor needs to know

Someone who lands on your page from a search, a launch post or a directory listing has never heard of you. Before they scroll, they want quick answers to a few questions: what is this, is it for me, what do I get, why this one, and what do I do next. If the first screen doesn’t answer them, most people leave without telling you why. The grader asks those questions of your page’s text, one by one.

The nine checks, with examples

The examples below are made up to show the difference. They aren’t real products.

  • Says what the product is. The headline or the line under it names the kind of product and the job it does. Weak: “The future of work, reimagined.” Better: “Invoicing software that chases late payments for you.”
  • Says who it’s for. A role, industry or situation the right visitor recognises. Weak: “For teams of all sizes.” Better: “For freelance designers who bill clients monthly.”
  • States a concrete outcome. What changes for the customer, not what the software contains. Weak: “Powerful automations and smart reminders.” Better: “Get paid on time without writing awkward follow-up emails.”
  • Says why it’s different. What you do that the usual alternative doesn’t, including a spreadsheet or doing it by hand. Weak: “The best invoicing tool.” Better: “Unlike accounting suites, it only does one thing: follows up until the invoice is paid.”
  • One clear call to action. One main next step, with the strongest button. Weak: “Start trial”, “Book a demo”, “Join the community” and “Download the app” side by side. Better: one “Start free trial” button, repeated lower down, with a quieter text link for demos.
  • Shows social proof. Evidence that other people use it: a short quote with a real name, customer logos you have permission to show, ratings from a review site, or a usage figure you can back up. Weak: “Loved by users.” Better: a named quote from a customer that says what changed for them.
  • Handles a main objection. The worry that stops people clicking: price, risk or effort. Weak: nothing near the button. Better: “Free for 14 days, no credit card, set up in one sitting”, if that’s true for your product.
  • Specific headline. A headline that could only belong to your product. A useful test: could a competitor, or a company in a different industry, paste it onto their homepage unchanged? “Work smarter, not harder” fails. “Chase unpaid invoices without the awkward emails” passes.
  • Overall clarity. A five-step score from “confusing” to “very clear” for the headline, subheadings and opening text read together, as a stranger would read them.

How the grade works

We fetch your page once, the way a crawler would, and pull out a compact summary of what a visitor sees first. Jev 1.13, TypeSafe’s decision model, answers all nine questions about that summary in a single call, each with a probability. The grade out of 100 is a weighted average of those probabilities. “Says what it is” and overall clarity count the most, and “handles an objection” the least, because a page can work without it while a page that doesn’t say what it is rarely does.

For every check that isn’t a likely yes, Claude Haiku 5.5 writes a short fix based only on the text of your page. It’s a starting point for your own edit, not copy to paste in blind.

What the grader can’t tell you

  • How your page looks. Layout, contrast, images and video are invisible to it. A product screenshot can answer “what is it” for a human even when the text doesn’t.
  • How your real visitors behave. The probabilities are a model’s reading of your copy, not conversion data. Watch what visitors do, and ask a few people who fit your audience to explain the page back to you.
  • Whether it ranks. For titles, descriptions and indexing, use the meta tag checker or the SEO checker.

After you fix the page

Test a few headline options in the headline analyzer, then run the grader again. When the page is ready for visitors, launch it free on LaunchRanked to get a permanent product page and a followed link with our badge.

Questions

What does the grader actually read?

The HTML your server returns for the URL: the title, meta description, H1 and H2 headings, the first 150 or so words of visible text, the buttons and links that look like calls to action, and text signals such as testimonials, logos, prices and free trial terms. It skips navigation, footers, hidden elements and cookie banners. It doesn't run JavaScript or look at your design, images or colours.

Why does my page get a low grade when it looks fine in a browser?

If your page builds its text with JavaScript after it loads, our fetch sees an almost empty page, and we say so under the results. The grader also only judges words: a screenshot that explains the product perfectly counts for nothing if the text around it doesn't. Open the What we read section to see exactly what the AI was given.

What do the percentages mean?

Each check is a yes or no question, and the model answers with how likely the answer is yes. At 65% or more we show likely yes, under 35% likely no, and anything in between unclear. Overall clarity is a score on a five-step scale, shown out of 100. The grade is a weighted average of all nine, so a borderline answer pulls it down a little rather than flipping it from pass to fail.

Can I trust the suggested fixes?

Treat them as a first draft. The fixes are written from your page's own text, and the model is told not to invent facts. Where a fix needs something your page doesn't say, such as who it's for or how many customers you have, it leaves a placeholder in square brackets. We also replace any number that isn't on your page with [number]. Fill in the real details yourself.

Which AI models does it use, and do you store my page?

The nine checks are answered by Jev 1.13, TypeSafe's decision model, in a single call through OpenRouter. The fixes are written by Claude Haiku 5.5, from Anthropic. We log the cost of each call, not the page or its text.

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