Coverage map

Seven areas, two layers, one map

The measured layer runs the grid. The execution layer carries the fixes. All seven areas are worked on both, and every result names the layer it came from.

7 areas2 layers, never blurred20 measured surfaces

One Panorama asks 50 questions of 21 engines, 7 times each — 7,350 cells in all, read across the 20 places an answer can show your brand.

The cost of a narrow map. If a roster never reaches a surface, your absence there and its blind spot read the same from the outside. A content budget then goes to a gap nobody measured. Every surface below is named, and CiteAngle seals each one to the same protocol.

Why the width of a roster decides what a number means

Ask a narrow roster whether your brand appears in AI answers and it will happily tell you no. Absence and a blind spot come back as that same word. A company then rebuilds a content programme around a gap that was really a blind spot. Or it relaxes about a category nobody measured.

This page exists to be checked. The arithmetic behind the widest panel is public — 50 buyer questions, 21 engines, 7 runs each. Hold that against anybody else's roster before you buy either. What no outside party can observe sits in the execution layer. We label it that way instead of reporting it as a measured figure.

The whole map — what we measure, and what we fix

Search, AI answers, local, commerce, media, social and the technical layer underneath. Those are the seven areas where a US brand gets found, and they sit on one map. Anything we can observe runs through the same grid, on the same queries. The rest gets prescriptions bound to the platforms’ own documentation. A result never crosses between the two.

AI answers
Measured

ChatGPT · Perplexity · Claude · Grok · Google AI Mode · Bing Copilot — citation and mention read separately

Execution

Quotable page patterns · third-party evidence carriers · misinformation correction

Voice and personalized screens: covered in the execution layer

Classic search
Measured

Google Search + AI Overviews · Bing Search · Yahoo Search · DuckDuckGo search assist — organic read against the AI read, same queries

Execution

Technical SEO gate patches · structured data

Local
Measured

Local-intent queries in the grid · local entity precision inside answers · Business Profile completeness read

Execution

Profile, review-source and NAP-consistency prescriptions

Map-screen rank: confirmed downstream through traffic you own

Commerce
Measured

Purchase-decision queries read across the AI answer surfaces

Execution

Product feed hygiene · in-store AI readiness

In-store AI answers: covered in the execution layer

Media · PR
Measured

Google News and Bing News surfaces · publisher-class citation breakdown · publisher AI-openness review

Execution

Republication on AI-open outlets · wire design · correction channels

Social · video
Measured

YouTube search and Google short-form in the grid · channel access table built from public crawler policies

Execution

In-app search prescriptions for YouTube, Instagram, TikTok and the app stores

In-app search rank: covered in the execution layer

Technical layer
Measured

AI crawler ledger · rendering-gap checks · citation-to-visit crosswalk

Execution

Freshness, IndexNow and crawler-access alignment

Traffic connection runs on your account permissions

The measured layer runs the full grid — 20 surfaces, listed in the matrix below, seven repeated runs, Wilson 95% confidence intervals. One reading is a point estimate. A range is the part you can act on. Voice, personalized screens and in-app search sit in the execution layer. The methods live on the methodology page; published reports live in the report library.

Stateless measurement, stated in writing. Every paid audit measures through stateless API calls — the state the platforms themselves define as pre-personalization, the same layer as a temporary chat with memory off. Real users’ answers start there and diverge, which is why a before-and-after comparison has to be read at that baseline. Sealed repeated runs accumulate into a time series. Both reads keep the same monthly rhythm — a spot check on the core question set, and a full grid re-run that resets the baseline. What separates the tiers is how much of the fixing we take on, not how often we measure.

Every surface, and which tier measures it

The free AI visibility check is a fast read on the AI engines. Paid audits add the search cross-check and, on Panorama, every source the answer engines pull from.

SurfaceSnapshotCompassPanorama
Six AI engines (ChatGPT, Perplexity, Claude, Gemini + more)✓✓✓
Google AI Overviews–✓✓
Google organic cross-check (+ secondary portal)–✓✓
News · YouTube · Google short-form––✓
SNS presence cards (Instagram / Facebook)––✓
Misinformation scan + executive brief––✓

Scope, deliverables and the decision each tier drives: the diagnostics page.

Measuring is step one of five — the loop closes

01 · Diagnose

Grid measurement

5, 20 or 50 buyer questions — every measurable position, run in full.

RadarCompassPanorama
02 · Prescribe

Execution design

Priorities, target URLs and completion criteria, bound into the report.

Built into every paid audit
03 · Implement

Build and publish

Technical gate patches, core pages and evidence assets — each with a change receipt.

Refit SprintCategory Sprint
04 · Remeasure

Matched delta

Before and after read on the same queries, same conditions — reported as observed change.

Built into both sprints
05 · Time series

Accumulate and watch

Sealed repeated runs, accumulated — and major engine-update windows are avoided on the remeasure.

Competitive SignalGrowth OperationsCategory Leadership

The monthly programs turn step five back into step one. Buy an audit within thirty days of starting a program and it comes off that first payment in full, on any program.

Every rung on this loop carries a published US-dollar list price: see the pricing page.

US regional visibility map

Behind a national average sit state-by-state gaps. As a quote-based add-on, we open a controlled 50-state + DC panel. State metrics stay on their own denominator, read alongside the national baseline.

How the state panel stays honest — location receipts, gates and denominators

Every engine call in the panel fixes explicit location parameters — city, state, country and timezone. Your state selection is sealed with a sha256 receipt, checked fail-closed against the stored hash on every run. A state score publishes only from panels that pass that location-receipt gate.

The selected state becomes hash-bound intake provenance. A dedicated USD regional scope adds location-capable API or exact browser receipts. It also adds contracted coverage and acceptance gates before the panel is frozen. National and state figures are reported on separate tables and separate denominators.

National baseline

Mention, direct citation, search presence and competitive position, read across the national query panel.

50 states + Washington, DC

State and DC scorecards, competitive intensity and priority-state actions under verified location signals.

measuredopportunityconfirmed strength

Scope a regional visibility map

Which of the seven areas is hurting?

Most buyers arrive with exactly one. Usually it is a category question they never appear in, a competitor who always does, or a fact the answers keep getting wrong. Match it to a starting point on the fit page, then read the list price on the pricing page.

Ready to turn US visibility evidence into a market plan? Tell us the US buyer, target state and decision date.

Request a written proposal