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Every tool counts mentions. Then what?

Praised is the GEO platform that answers what comes after the rate every tool reports — whether it moved, what moved it, and what to publish next.

After the number

The four questions a rate cannot answer

  • Is this change real, or inside the noise?
  • Which page or source caused it?
  • What should we publish next?
  • Did last month’s work measurably do anything?

Answering these is the whole product. Counting is the easy half.

Trackers tell you the score. Praised moves it.

Category norms as of August 2026. Tools evolve — treat this as a checklist for evaluating any of them, including us.

What you getVisibility trackersPraised
Mention, citation and recommendation tracking across the major enginesYesYes
Every engine on every plan — no surface withheld from the cheapest tierRare — how many engines you get is a common tier leverYes — all six, on Free and up. Credits are the only limit
Confidence intervals on every number — so you can tell movement from noiseRareYes, on every headline rate
A prioritised fix queue — findings become tracked workUsually notYes, and it closes only on a passing re-check
Agents that do the work — not just report on itIncreasingly commonYes — 40 tools on your own measurement, three autonomy modes you pick per run
Every automated action priced first and traced afterRareYes — an estimate before the credit is spent, and a proposal is never logged as a completed action
Content production — verified facts into GEO-scored, publishable pagesUsually notYes, scored 0–100 before it ships
Corrections — drafted requests for wrong facts on pages you do not ownUsually notYes, verified by re-reading the page
Experiments — baseline, change, treatment, attributed liftUsually notYes, with the null results reported too
A published score formula — the arithmetic behind the headline number, so you can recompute itRareYes — 0.4 share of voice + 0.4 mention rate + 0.2 recommendation
A self-serve API and MCP server — a key you create yourself, not a sales conversationCommonly enterprise-only, and often behind a support requestYes — REST API and a 15-tool MCP server on every paid plan, key minted in Settings
Published methodology, including its limits and the calibration behind themVaries — usually a description of the method, rarely its error ratePublic, no account required — Cohen's κ, judge sensitivity and specificity, labels counted per stratum, and a limitations section

Evaluating a specific tool? Hold it to the six-point measurement bar we publish and meet — or ask us and you will get a straight answer, including where we lose.

Numbers built to survive scrutiny

The reason to care about the statistics is not elegance. It is that someone senior will eventually ask whether the number is real, and you need an answer better than "the dashboard says so".

6
answer surfaces on one schedule — ChatGPT, Google AI Overviews, Gemini, Claude, Perplexity and Grok. Queried directly, never inferred from rankings, and none of them locked to a plan.
each prompt asked repeatedly, per engine. One answer is an anecdote; a sample is a measurement.
95%
confidence interval on every headline number — a change is flagged only when it clears the noise.
2,000
bootstrap resamples behind every interval, clustered by prompt rather than pooled.

The honesty architecture

This is the part we would keep if we had to drop everything else. In a category this new, the easiest thing to sell is a confident number, and the most valuable thing to own is a number you can defend.

  • Every result is labelled live, modeled or estimated — in the product and in our marketing
  • Gaps are reported as gaps — an engine we could not reach says so instead of being averaged away
  • Null results are published — an experiment that found nothing says it found nothing
  • No invented statistics — if a number is ours it came from a run; if it is not, it is cited and dated
  • The method is public, limitations section included
  • We audit ourselves — praised.co is graded by our own scorer on every change, and a page below 85 does not ship

What we will not do

Stated plainly, because the category is full of quiet over-promises:

  • Claim we can delete something from a model's memory
  • Guarantee a position in an AI answer
  • Attribute revenue to a mention we cannot trace
  • Show you an estimated number styled as a measured one
  • Write to your website without a connection you configured
  • Let an agent publish to your live site, or report an action it only proposed

If any of these is a requirement, we are not the right tool, and you should be suspicious of whoever says yes.

When someone asks "did any of this work?", you open Experiments.

Every shipped change measured against its own baseline. The answer is a number with a confidence interval — not a hunch, and not a screenshot.

Fair questions

How do you compare to a specific tool?
We describe categories rather than name rivals, because a dated claim about a competitor is out of date within a quarter and unfair by the time anyone reads it. Hold any tool you are evaluating — including this one — to the six-point measurement bar we publish, and ask for a straight answer where it falls short.
Why does the confidence interval matter so much to you?
Because without one you cannot tell a real improvement from the natural variance of a stochastic system. A tool that reports mention rate moved from 41% to 47% with no interval has told you nothing about whether anything happened, and teams have reorganised quarters around exactly that kind of noise.
What are you deliberately not good at?
We do not do social listening, PR distribution, or classic rank tracking, and we do not claim to edit what a model remembers. We are narrow on purpose: measure AI answers rigorously, produce the changes they imply, and prove whether those changes worked.
What do you deliberately not put behind a higher tier?
Engines, the API, the MCP server, agents and flows. All six answer surfaces run on every plan including Free, and the REST API and the 15-tool MCP server ship on every paid plan — the key is minted in Settings → API keys in about a minute, with no sales call and no support ticket. What the plans differ on is credits and how many products you track. Category norms as of August 2026 run the other way on all of these, so it is worth asking any tool you evaluate which of them it gates.
Can I see the methodology before buying?
Yes, all of it, without an account — including the limitations section. It publishes the parts most methodologies leave out: the judge's measured error rate against hand-labelled answers (Cohen's κ, sensitivity, specificity), the Rogan–Gladen correction those figures drive, how many answers were labelled in each stratum so you can see what the estimate does not cover, and 95% intervals from a 2,000-resample cluster bootstrap over prompts rather than responses. If a measurement vendor will not show you how the number is produced before you pay, that is the answer to your evaluation.

Judge it on your own data.

The free tier runs a real suite across every engine. No card, and the methodology is public before you start.