Was ist neu bei RankedByAI

The two checks people run most often now live in your terminal. `npx rankedbyai llms-txt yoursite.com` drafts an llms.txt from your sitemap and homepage links; `npx rankedbyai crawlers yoursite.com` audits robots.txt against 11 major AI crawlers and exits non-zero when any is blocked — so a deploy can never silently lock AI out again.

The CLI is open source, has zero runtime dependencies, and runs on Node 18+. Put it in CI, or just run it once to see where you stand.

Growth numbers used to be assembled by hand every Monday. Now a Monday cron collects them automatically — scans, signups, content and uptime probes — into a Growth section in the admin console, with an alert email when something needs attention.

Same data, zero manual work, and a consistent weekly baseline to compare against.

Businesses that scan well wanted a way to show it. There is now an embeddable AI Visibility Score badge — a live SVG served from our API — with an embed dialog on the report page that hands you the snippet.

The badge links back to the underlying report, so the score on someone's site is always the score we actually measured.

AI visibility comes with a pile of new vocabulary — mention rate, grounding, citation gap, prompt sampling. The new /glossary defines 30 of these terms in all six site languages, each with a plain-language explanation and links to where the concept shows up in the product.

Blog posts and reports now link into the glossary instead of re-explaining the same terms in every article.

Buyers comparing AI visibility tools kept asking the same question — how is RankedByAI different from X? The new /alternatives section answers it head-on: eight competitor comparison pages, each in all six site languages, with honest feature tables and pricing context.

Where a competitor is genuinely a better fit, the page says so. Comparison pages only work if they survive being read by someone who knows both products.

RankedByAI now looks like one product everywhere it shows up. We shipped a full logo suite — mark, wordmark, mono and inverse variants — plus social and OG image templates, a PWA manifest with app icons, and a v2 brand book that pins down how all of it is used.

The blog got the most visible upgrade: every article now renders its own dynamic OG image, so shared links carry the article's title instead of a generic banner.

Until now, publishing a blog article meant a code deploy. That coupling is gone: posts moved into a D1-backed CMS served by a worker content API, with an admin editor for writing and publishing.

The frontend renders articles via ISR, so a newly published post appears on the site within minutes — no build, no deploy, no engineer in the loop.

Day-to-day operations used to mean poking at the database directly. We built an internal admin console — overview, customers, scans and content sections — behind a Bearer-token API.

It is deliberately boring: see what is happening, find a customer, inspect a scan, edit content. Boring is what you want at 8am when something needs checking.

The fourth revision of our design system landed together with a sweep through every page. The demo card grid was rebuilt, product screenshots were recaptured at retina quality, and shadow tokens were standardized.

Alongside the new spec we fixed dozens of small alignment issues that had accumulated across the marketing site — the kind of one-pixel debts nobody files a ticket for but everybody notices.

The customer dashboard used to open with charts and leave the interpretation to you. It now leads with triage: what problems were detected this week, and what the concrete next actions are.

Every section closes with a plain-language conclusion, so the dashboard reads like an analyst's summary rather than a wall of metrics.

Our marketing pages talked about AI assistants as if every visitor lived in the US. Now the examples adapt to each of the six site languages — the assistants and search habits referenced match what buyers in that market actually use.

A small copy change, but it removes the constant translation dissonance of reading about a tool nobody around you uses.

The landing page, industry pages, blog, tools, methodology and legal pages were rebuilt in Next.js on the v3 token system: a proper typography scale, the emerald palette, card shadows, and retina product screenshots throughout.

This was the visible half of the migration off the original worker-rendered pages — same content, dramatically more coherent presentation.

rank.zalize.com now serves the migrated pages from a Next.js app through a service-binding proxy. The cutover was designed to be reversible: a KV kill switch flips traffic back to the old renderer instantly if anything misbehaves.

Visitors saw nothing change during the switch — which was exactly the point.

Counting brand mentions is easy to get subtly wrong: 'Joe's Plumbing' and 'Joes Plumbing LLC' are the same business, and a mention buried in a caveat is not the same as a recommendation. Scans now attribute every brand mention at sentence level and normalize entity names with an LLM pass.

The result is that reports can show you the exact sentence where you — or a competitor — appeared, and variant spellings no longer split one brand's count in two.

Scans gained a third channel: live Brave web search results are fetched for each buyer question and fed to the model before it answers. This approximates how retrieval-augmented assistants actually compose recommendations — from the live web, not just training memory.

Reports now split results per channel, so you can see what models believe on their own versus what the live web tells them. The gap between the two is often the most useful finding in the report.

Scans used to run inside a single long HTTP request — workable, until a slow model response pushed it over the edge. They now run as background jobs on a Cloudflare Queue, with the browser polling for progress.

Scans are more reliable, progress is visible question by question, and a flaky connection no longer kills twenty minutes of sampling.

Knowing you were mentioned is half the story; knowing which pages AI answers cite is the other half. Reports now break down the exact URLs cited across all sampled answers, aggregated by domain, with a citation funnel view.

The deep-read insights highlight the pages your competitors win with — the comparison posts, directories and review pages that keep feeding AI answers. That list is effectively your content roadmap.

Question prompts are now auto-generated per business and industry instead of drawn from a fixed pool, and sampling scaled up to 29 question templates per scan.

A cost circuit breaker caps runaway scans, so the larger sample stays economically sane. More questions, better tailored — the mention-rate estimate got noticeably steadier.

Findings are only useful if they tell you what to do. Every detected gap now comes with a concrete page-level fix: which page to create or edit, and what it should say.

Instead of 'your FAQ coverage is weak', the report says 'add a page answering this question — here is the structure'. That difference is what makes people actually fix things.

Scans now audit your own website, not just the AI answers about you. The diagnosis checks the technical and content signals AI assistants rely on — crawler access, structured data, llms.txt, FAQ content and more — and ties each finding to the visibility gaps in your report.

It turns 'AI doesn't mention us' from a mystery into a checklist.

The Monitor and Optimize plans became free for the duration of the open beta — full weekly monitoring, prompt management and email reports, no credit card.

We want trend data and honest feedback more than early revenue. Planned post-beta pricing stays published on the pricing page so nobody is surprised later.

One scan is a snapshot; the interesting question is whether the picture is changing. Every customer now gets a private dashboard — grade, mention rate, trend chart and scan history — fed by weekly scheduled re-scans via cron.

Same questions, every week, no manual work. This is the feature the rest of the product is built around.

AI visibility looks different for a dentist than for a SaaS company, so we shipped programmatic /for/<industry> pages for twelve industries, each with JSON-LD, sitemap entries and cross-links.

Each page speaks to how buyers in that industry actually ask AI assistants for recommendations.

The entire site and all reports were localized into English, Spanish, German, French, Portuguese and Chinese. Language lives in the URL — /de/pricing, /zh/blog — with hreflang wiring throughout.

AI answers themselves stay verbatim in whatever language the model answered in: translating evidence would falsify it.

The first public version went live on Cloudflare Workers: a free AI visibility scan with a permanent report link, A–F grading, competitor extraction from real AI answers, an SEO blog, and Paddle checkout.

Rough in places, but the core promise worked on day one — ask the AI the questions your buyers ask, and show you exactly where you stand.

Shipping a fix and wondering whether it worked is a bad loop. Now you mark a recommended fix as done and re-check it instantly — the audit re-fetches the affected page and re-runs that specific check on the spot.

Trend charts show fix markers at the point each fix shipped, and weekly change summaries confirm what actually improved. Action and outcome, finally on the same chart.