Paste one page to measure its AI recommendation readiness. Get evidence, competitor context, and the next change to make and verify.

Our mission
Today, check your website! measures whether one exact page can be accessed, understood, quoted, and trusted. It turns that evidence into a prioritized change you can make and verify. We are building toward AI Recommendation Intelligence: learning which changes are most likely to improve how generative systems represent and recommend a business.
Measure. Prioritize. Change. Verify. Learn.
What the check measures, what it does not, and what you get next.
Not yet. The current product measures page-level recommendation readiness, not live mentions across AI answers. It checks the signals that affect whether a page can be accessed, understood, quoted, and trusted, then ranks what to change next.
check your website! analyzes one exact page at a time. It measures access, page identity, quotable answers, proof, structured labels, links, mobile speed, and content structure.
You'll see a recommendation-readiness score, the evidence behind each issue, competitor benchmarks when supplied, and the one change that matters most to make next. After publishing, you can verify the same signal again.
Proof is specific evidence placed close to the claim it supports. Examples include numbers, dates, customer examples, screenshots, source links, testimonials, certifications, case studies, or clear before-and-after results.
A self-contained answer block is a short section that answers one question without needing surrounding context. It usually starts with a clear heading or question, then gives a direct 1-3 sentence answer.
No. Paste one public page and check your website! analyzes that exact URL. Add more pages any time; each gets its own score and prioritized changes. If the page blocks automated access or important assets, the report can identify that access problem.
It is our direction for connecting measurement to action: diagnose why a page is hard to use, recommend the most valuable change, verify the result, and learn which interventions work. Prediction and cross-model monitoring are research and product-development goals, not features of the current free check.