AI visibility tools: know where your brand stands, and what to do next

Ask one question across traditional and AI search. Quattr's AI visibility tools show what changed, why it matters, and what to do next, inside the AI client your team already uses.

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Read-only by default Evidence attached

Your AI client illustrative example

Where are we losing high-intent visibility across search and AI answers, and what should we fix first?

Finding Search Non-brand clicks fell 11.8%, a real drop, not a random swing (p = 0.004). Rankings held. AI answers citation rate −2.6 pt on the tracked prompt set. Comparison and pricing demand weakened across both surfaces. Prioritize it.

Next moveRefresh the three comparison pages already earning this demand, then rerun next window.

⚠ Same segment and period. The two measures support this priority, not a combined score or causal claim.

4.9/5on G2 · 65 verified reviews

The answer journey

From question to action, without the dashboard relay race.

Watch one question become an inspectable next move.

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Your AI client Illustrative analysis · acme.example · US · Non-branded · Last 28 days

Ask the question your team already asks.

No dashboard syntax. No exports.

Where are we losing high-intent visibility across search and AI answers, and what should we fix first?

Your business context resolves first.

Your brand, market, segment, period, goals, and vocabulary travel with the question.

"we" acme.example

"high intent" comparison + pricing intents

"where" US · all devices

"when" last 28 days vs prior 28

"competitors" tracked competitor set

Scope locked acme.examplecomparison + pricingUS · all deviceslast 28 vs prior 28tracked competitor setnamed goals

A ready-to-run Agent loads.

Quattr loads the analysis, evidence rules, and output this job requires.

Search vs AI Answers Agent Selected by your question · Agent Plugin loaded · MCP included

Skills set the sequence

1 · Resolve the scopeset-analysis-scope ●

2 · Compare search and AI surfacesai-vs-traditional-gap ●

3 · Separate signal from noisesignificance-referee ●

4 · Rank the pages to act oncontent-decay-refresh ●

Method sets the rules a change must prove it's realcorrelation isn't causationthe claim stops where evidence stops

Output locked findingimplicationnext moveevidence boundary

Only the evidence this question needs is read.

Quattr reads only the connected sources this question needs and attaches freshness to each result. Everything else stays untouched.

Search performance search_performance ● packet returned · GSC as of 2 days ago

Rank & market tracking competitive_trends ● packet returned · observed daily

AI-answer presence ai_visibility_overview ● packet returned · tracked prompts, daily

Search market share market_share_overview ● packet returned · observed daily

Not read GA4 goalsunneeded here Google Adsunneeded here

4 evidence packets · one locked scope · freshness attached

Real, noise, or can't tell, before action.

Quattr runs the statistical test for you, so the team acts on real changes, not random swings.

REAL Non-brand clicks A real drop −11.8% · p = 0.004

NOISEA random swing, not reported as a change

CAN'T TELLNot enough evidence, the claim stops, and says why

⚠ Observational · same segment and period · no causal claim

SEPARATE LANEAI citation rate fell 2.6 pt on the tracked prompt set, reported beside the click verdict, never validated by it.

A decision with its evidence attached.

The next move leads. The evidence rides underneath.

Next moveRefresh the three comparison pages already earning this demand, then rerun next window. WhyComparison and pricing demand weakened across both surfaces. Prioritize it. The evidence SearchNon-brand clicks fell 11.8%, a real drop, not a random swing (p = 0.004). Rankings held. AI answersCitation rate −2.6 pt on the tracked prompt set over the same window. Evidence boundary. Same segment and period. The two measures support this priority, not a combined score or causal claim.

Move the decision into tools that act.

After approval, the Agent can hand the brief to your connected CMS, Slack, or Asana.

Refresh brief, 3 comparison pages · evidence attached

Review → ApproveNot sent · review required One brief. Nothing leaves without approval.

CloudEagle 3×increase in AI Citation Share

113% organic click growth
328 net-new Page 1 queries that previously drove zero clicks
77% of post-intervention traffic from high-intent, consideration-stage searches

CloudEagle customer story ↗

"Previously, everything was flowing from me. I had to give all the keywords, what to do, what topics to use. Now, it's democratized. The writers have become experts with the help of the system."

Joel Platini · Content Manager, CloudEagle

46%more clicks on treated product pages

75% more AI Mode visibility
50% more top-3 ChatGPT citation coverage
10× stronger day-30 clicks on validated new content

Men's Wearhouse customer story ↗ One-pager (PDF)

"Quattr gave us a more systematic way to move from search insight to execution. Because the workflow was built on our own Search Console data, we had more confidence in what to launch, what to optimize, and how to evaluate the outcome."

David Graveline · Sr Product Manager, Growth, Men's Wearhouse

12.8%year-over-year growth in relative search market share

grew while industry-wide clicks declined
algorithm shifts detected within days, against 18+ months of daily baseline tracking

Housing.com customer story ↗

"The SEO function evolved from reporting what happened to explaining why it happened and what to do about it."

Atinder Singh · Senior SEO Manager, Housing.com

See it on your data. Request a Demo Explore Agents →

The Quattr System

The advantage is everything you don't have to build.

Quattr starts with your search intelligence, business context, expert methods, and evidence rules already working together. Run a ready-to-run Agent, then extend it with your own tools.

Start with an agent builderassemble the search system yourself

connect datamodel contextdefine verbsencode expertiseset evidence rulesdesign outputsmaintain it

You still haven't answered the question.

Start with Quattrthe system already works

Platformdata + business context

MCPpermission-aware analytical verbs

Agent Plugin packages the connection orchestrates Skills + Method

peer inputs, Skills + Method → Agent Plugin Skillsthe expert sequence Methodevidence & decision rules

Ready-to-run Agentsrepeatable search work

+ your additionsextend a working system

01 · DO THE WORK Agents + Skills Repeatable jobs. Expert sequence. begins directly below ↓ 02 · TRUST THE WORK Platform + Method Connected intelligence. Bounded claims. Jump to chapter ↓ 03 · USE IT WHERE THE TEAM WORKS Agent Plugin + MCP Packaged experience or direct connection. Jump to chapter ↓

01 · DO THE WORK

Agents run the job. Skills define the standard.

Choose the search job. The Agent carries its scope, cadence, and output. Skills supply the expert sequence and guardrails, so every rerun stays comparable.

Agent, the repeatable job Illustrative example Search vs AI Answers Agent acme.exampleUS · non-brand organiccomparison + pricing28 days vs priorcadence: every Monday Output contract: finding · why it matters · next move · boundary

Skills, the expert steps inside it Illustrative Agent composition

1 Resolve the scopeset-analysis-scope

2 Compare search and AI surfacesai-vs-traditional-gap

3 Separate signal from noisesignificance-referee

4 Rank the pages to act oncontent-decay-refresh

Method requires every claimed change to clear its evidence and significance rules.

RUN 01 · ACTION REQUIRED Comparison and pricing demand weakened across traditional and AI search. Refresh the three affected pages. Evidence and limits →

RUN 02 · NEXT WINDOW Same question · scope · Skills. Changed result: clicks −3.4% (not significant); recovery not confirmed. Rerun next window.

Repeatable jobs AI visibilitySearch market shareCompetitor intelligenceAnomaliesSearch to revenueExecutive reporting Explore ready-to-run Agents → Inspect the Skills inside this Agent →

02 · TRUST THE WORK

Platform

Bring your connected search signals into one decision.

Quattr joins your performance and outcome data with rankings, competitors, search features, and AI answers, carrying context, permissions, freshness, and provenance.

See the Platform in a demo →

FIRST-PARTY, YOURSSearch Console · BigQueryGA4 & Adobe goalsGoogle Ads

QUATTR-OBSERVEDRankingsSearch market shareSERP featuresAI answers

Planes converge only where the question requiresOnly needed source families are read.

SOURCE LEDGER GSC, as of 2 days ago Ranks, observed daily AI answers, tracked prompts, daily Missing or stale data lowers confidence.

ReachableCan crawlers and AI bots fetch you at all?

RetrievedDo you enter the consideration set?

ReferencedAre you the source answers are built from? ◀ the limiting rung, the claim stops here

RepresentedDoes AI describe you the way you would?

RewardedDoes it turn into traffic, conversions, revenue?

The claim rises only as high as its measurement; here, the evidence stops at Referenced.

Lever on the limiting rungRefresh the three pages already earning this demand and close their comparison-evidence gaps.

Method

Know what the evidence supports, and what to do next.

The Quattr Method bounds each claim, shows the limiting rung, and points to the lever most likely to move it.

Read the Quattr Method →

03 · USE IT WHERE THE TEAM WORKS

Agent Plugin

Install ready-to-run Agents in your AI client.

The Agent Plugin packages Quattr MCP, then uses Skills and Method to orchestrate its analytical capabilities into ready-to-run Agents. Connect through MCP directly when you want the capabilities without the packaged Agent experience.

Explore the Plugin →

01Platformdata + context

02MCP verbsgoverned, permission-aware

03Plugin orchestrationusing Skills + Method

04Ready-to-run Agentsdecision-ready results

05Your AI clientwhere the team works

One Agent Plugin install. Worked examples and updates included.

01Choose client

02Authenticate

03Resolve tenant & context

04Verify read-only

05Ask the first question

Agent Plugin: Claude · ChatGPT · Codex · verified standard clients Direct MCP: verified MCP-capable clients Read-only analytical scope

MCP

Connect a supported AI client directly to Quattr's search intelligence.

Quattr MCP exposes permission-aware analytical capabilities over your data and business context, without the packaged Agent experience.

Explore MCP →

The operating standards are built in.

Read-only scope"quattr:read" only, analysis can never change your configuration.
OAuth 2.1 + PKCEYou never paste keys or passwords into the AI client.
Permissions inheritedConnecting grants no additional access beyond your account.
Organization-isolatedEnforced at the data layer on every query; each conversation bound to one organization.
Audit-loggedTool calls are audit-logged server-side.

Review security details ↗ The evidence standard: Quattr Method →

Turn your next AI search question into a decision.

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