Your buyers ask AI before they ask you.
Tailwin is a content engine built for how people actually search now. We find the questions ChatGPT, Perplexity, Gemini and Grok answer without naming you, write the pages that deserve the citation, publish them in your voice, and prove the answer changed.
Free, no account. Runs a real scan plus a page-level readability and citability check on your site.
Answer-engine optimization is the practice of getting a brand named and cited inside AI-generated answers, rather than ranked in a list of blue links. It differs from classic SEO in what it optimizes for: an assistant extracts a passage and attributes it, so the unit of value is a quotable, self-contained passage rather than a keyword-targeted page.
That changes the work in three concrete ways. Pages must be readable without JavaScript, because most AI crawlers do not run it. Answers must open the section rather than close it, because an engine lifts the first passage that resolves the question. And the claim has to carry a specific, checkable figure, because a vague sentence is never the one quoted.
The loop closes in four instrumented steps. Monitoring tools stop after the first one, which is why they can tell you that you are invisible but not whether anything you did about it worked.
We ask ChatGPT, Perplexity, Gemini and Grok the questions your buyers ask, and score every answer for your brand.
An edge tracker logs every GPTBot, ClaudeBot and Perplexity visit: the traffic JavaScript analytics is blind to.
The content engine turns your visibility gaps into humanized articles, published to WordPress, Shopify, Ghost, dev.to, Hashnode or Blogger, and only after your approval.
Watch the loop close: published Tuesday, crawled Thursday, cited in AI answers, score moves.
A monitoring tool reports the symptom. Tailwin reports the cause, fixes it, and re-measures. Here is the same job, drawn against both.
| Job | Monitoring tool | Tailwin |
|---|---|---|
| Tells you if AI names your brand | Yes | Yes, across five engines |
| Explains why you are absent | No | Page-level readability and citability scores |
| Shows which pages AI crawlers read | No | Edge tracker, logged before any script runs |
| Separates answer-loss from training-only blocks | No | Scored separately, by crawler tier |
| Writes the pages that fix it | No | In your measured voice, slop-stripped |
| Publishes them | No | Six CMS targets, hosted pages, or a signed webhook |
| Waits for sign-off first | No | Approve or reject from a link you can forward to the client |
| Proves the answer changed | No | Re-scan tied to the published URL |
| Proves what was published | No | Signed provenance credential per piece, publicly verifiable |
The first time we scored this page with the same engine we sell, it returned 43 out of 100 for extraction, with no structured data and no llms.txt file. We rewrote it against our own rubric and re-ran the scan. That is the entire method: measure, fix the specific thing the measurement named, measure again.
Our scoring engine weighs 5 categories: how directly a section opens with its answer, whether a passage survives being lifted out on its own, structural readability, the density of checkable figures, and whether the page carries anything an assistant could not already generate. We publish the weights because a score you cannot audit is a number, not evidence.
Most AI content is never cited because it says nothing an assistant could not generate itself. Anyone can produce a thousand words; the reason they fail is that they read like machine text and carry no fact worth attributing, so neither a reader nor an engine has a reason to point at them.
We measure your existing writing: sentence length, formality, active voice, reading level, the phrases you actually reuse. Every draft is written to that fingerprint and scored against it before anyone sees it.
No em dashes, no delve, no leverage, no not-just-X-but-Y. A dedicated editing pass plus 14 named checks run on every draft, and each check's pass or fail is shown before anything publishes.
Every piece starts from a question buyers actually asked an assistant, which we watched go unanswered. That is what earns a citation, and it is why the score moves.
The same engine, priced and packaged for who is holding it.
You want to be the name an assistant says when someone asks who to hire. Track the questions your buyers actually ask, see who is being recommended instead, and publish the pages that change it.
Every client gets their own account, their own voice, their own reporting. You get one dashboard across the whole book, and content that sounds like each client rather than like an AI.
White-label the whole platform. Your logo, your colors, your portal domain, your margins. Your clients never see our name, and the scan widget on your site feeds leads straight to you.
Three plans, from $49 a month. One business and domain per account; extra locations start at $10 a month each.
Agencies white-label everything, and your clients never see our name. Full pricing, including the $99 detailed report →
Answer-engine optimization is the practice of getting a brand named and cited inside AI-generated answers, rather than ranked in a list of blue links. It differs from classic SEO in what it optimizes for: an assistant extracts a passage and attributes it, so the unit of value is a quotable, self-contained passage rather than a keyword-targeted page.
Monitoring tools report that a brand is absent from AI answers. Tailwin reports the same thing, then explains the cause at page level, writes the pages that fix it, publishes them, and re-scans to show whether the answer changed. The loop closing is the product; the score is the instrument.
No. GPTBot, ClaudeBot, PerplexityBot and the other 25 crawlers we track do not execute JavaScript, so JavaScript analytics never records the visit. Tailwin logs them at the edge, before any script runs, which is why crawler traffic appears here and nowhere else in a typical reporting stack.
It depends on which crawler. We classify 28 crawlers into 3 tiers: 8 are live answer surfaces such as OAI-SearchBot, PerplexityBot and Claude-SearchBot, and blocking those removes the site from answers people are actively reading. 9 others only feed model training, so blocking them costs no answer-engine visibility and is a legitimate content-rights decision rather than a mistake. Tailwin scores the two groups separately instead of counting every blocked bot as a problem.
Content is judged on whether it is useful and accurate, not on how it was produced. The practical risk is different: generic AI text earns no citation because it says nothing an assistant cannot already generate. We measure your existing writing across 9 dimensions, write every draft to that fingerprint, strip the patterns that read as machine-written, and score the result before anyone sees it.
The loop runs on crawler time, not on a guarantee. A published page has to be discovered, crawled and then reflected in an answer, and Tailwin instruments each of those steps so the delay is visible rather than assumed. We report what the crawlers and engines actually did; we do not promise a ranking or a date.