Praised is an AI-visibility measurement tool for in-house teams: it tells you whether ChatGPT, Claude, Gemini and the rest recommend you — with confidence intervals — and exactly what to publish next.
And the view in the product that answers each one
Four questions, four views — not four slides you improvise the night before.
Sooner or later someone senior asks whether the number is real. In-house, that moment decides whether AI visibility becomes your win or your liability. So every rate Praised reports is a repeated sample, not a one-shot answer, and carries a confidence interval — when it moves, you can say whether it really moved.
Every figure is also labelled live, modeled or estimated. You'll never put an estimate in front of your VP dressed as a measurement — and when the skeptic on the analytics team asks how the number is produced, the methodology is public, limitations included.
It is written for that skeptic rather than for a buyer. An LLM scores the answers, so the page reports how often that judge is wrong: its agreement with hand-labelled answers as Cohen's κ, its sensitivity and specificity, and the correction those two figures drive on the mention rate. It states how many answers were labelled in each stratum, so the part of a run the estimate does not cover stays visible. And every headline rate carries a 95% interval from a 2,000-resample cluster bootstrap over prompts, not responses — treating repeats of one prompt as independent is the standard mistake in this category, and it makes every rate look more certain than it is. A run with no human labels is reported as not human-validated rather than quietly passing as measured.
Need the stakes for the deck? The documented record is already written: Chegg is what losing the answer looks like, Vercel is what winning it looks like — both sourced line by line.
Your rank tracker still measures the ten blue links. Praised measures the layer above them — the synthesized answer your buyers increasingly act on without ever clicking. Different layer, different measurement; nothing to rip out.
SEO still earns the crawlable, citable pages that engines draw on. What it can't tell you is what the assistant said. Where GEO and SEO actually differ →
Measurement runs from our side by querying the engines directly — nothing to deploy, no tag manager ticket, no waiting on engineering.
Exports, a REST API and an MCP server for the reporting stack you already have — the number travels to the deck without screenshots. You mint the key yourself in Settings on any paid plan: no integrations tier, no sales call, nothing to request. Integrations & API →
A score you can't act on is trivia. Praised's output is work your team can actually ship — sized for an in-house team, not a services engagement.
Findings become tracked items: the page to rewrite answer-first, the wrong claim to correct, the comparison page to build. Each closes only when a re-check passes. How the queue works →
Drafts built from your verified product facts, scored 0–100 for answer-readiness before they ship — so publishing the fix doesn't wait on a content sprint.
Every shipped change measured against its own baseline. When someone asks "did any of this work?", the answer is an attributed number — including the honest null when it didn't. How attribution works →
The catch for an in-house team is rarely the method — it is that nobody has a spare day a week to run it. Agents take the repetitive half: read what the engines said, work out which rival page won and why, draft the answer to the prompt you lost. You choose per run how far one may go, from plan-only to running unattended, and the default asks before every change. Flows put the same sequence on a weekly schedule. Both are included on every plan, including Free.
Start free — the free tier runs a real measurement suite, no card required. When you need more engines and a steady cadence, Pro starts at $99/month.
The free checker shows where you stand in about a minute — no signup, no card, and the methodology is public before you start.