9 AI engines8 markets, asked nativelyRevenue per prompt

Buyers ask AI first. Find out what it says about you.

Search sent people to a list of links. AI hands them an answer — and one or two brands. AEOMAX measures whether you are in that answer across nine engines, shows you the sentence and the source behind it, hands you the fix as a pull request, and then measures what the fix was worth.

Start free Open the live demo See everything it does

Demo is sample data for a fictional brand•No credit card•First scan in minutes•Every number ships with its sample size and interval

What a scan returns

Visibility

±4 (n=240)

Share of voice

42%

vs 3 rivals

AI revenue

$7.6k

last 30 days

Prompt × engine

Read by a model, not a keyword match

Best note-taking tools

Absent · 4 rivals named

Fix ready

Notion vs Airtable

Ranked #1 · positive

Hold

Cheapest note app

Absent · shopping intent

Fix ready

AI engines tracked

60+

Screens, none marked coming soon

95%

Interval on every headline number

API + MCP

Your stack, not just a dashboard

Coverage

Every answer engine your buyers actually use.

Nine engines, included — not metered one paywall at a time. Ask the same question in eight markets, in prompts written in that market's own language, because the brands named in the US are not the brands named in Germany or Japan. Every engine tile says whether that engine searched the web before answering or answered from memory.

ChatGPTClaudePerplexityGeminiCopilotGoogle AI OverviewsGrokDeepSeekMeta AI

How it works

From invisible to the default answer.

Every screen belongs to one of four stages. You always know which question you are answering, and what to do next.

Stage 1

Diagnose

Why am I invisible?

✓Engine breakdown
✓Site health
✓AI access firewall check
✓AI crawl readability

Stage 2

Find openings

What do I target next?

✓Prompt winnability
✓Prompt discovery
✓Citation gap
✓Knowledge vs retrieval

Stage 3

Compete

How do I pass rivals?

✓Share of voice
✓Battlecards
✓Rival move radar
✓Shopping visibility

Stage 4

Prove it

Did it pay?

✓Per-prompt revenue
✓Impact ledger
✓Lift experiments
✓Client-ready PDF

The difference

Most tools count mentions. We tell you what they are worth.

Accuracy

An LLM reads every answer

Keyword matching cannot tell a recommendation from a brush-off, and calls most answers neutral. A model reads each response: are you genuinely recommended, where do you rank, how are you described — and does the answer state anything about you that is simply wrong?

Hallucinations

Catch what AI gets wrong, and watch it persist

Dead pricing, a feature you retired, a plan that no longer exists. Register what is true, and every contradiction becomes one row in a ledger: which engines repeat it, how many runs, and how many days it has survived since you first saw it.

Revenue

Visibility tied to money

Connect Google Analytics and see AI-referred sessions, conversions and revenue split by engine. Stop defending a vanity score and show which engine actually pays.

Honesty

Every number carries its error bars

A score from 8 answers and one from 800 look identical everywhere else. Here each metric ships with its sample size and a 95% confidence interval, and thin samples are labelled insufficient instead of dressed up as fact.

Provenance

Which sentence came from which page

For a grounded answer, each sentence is matched to the passage on the page it was taken from — and the sentences no cited source supports are listed on their own. You can see what the engine read, not just what it said.

Action

Fixes you can ship, then measure

The plan ships the artefact — llms.txt, Organization and FAQ JSON-LD, robots rules, content briefs — and can open the pull request that puts the file in your repo. Mark it shipped and the ledger reports the 14- and 28-day change against a frozen baseline.

Everything it does

Six things, end to end. Nothing marked “coming soon”.

Measure the answer, take it apart, fix it, earn the citations behind it, prove what it was worth — and run the whole thing for a book of clients.

All live today

Measure

What every engine says, every day

The same buyer questions, asked of every engine on a schedule, read by a model rather than a keyword match.

Visibility score, share of voice, position and sentiment per engine
Grounded-or-from-memory label on every engine, from the stored answer
Blocked pages and CAPTCHAs excluded instead of scored as a zero
Ask a prompt up to 5 times: “mentioned 2 of 3”, with the spread
Model swaps marked on the trend, so a drop is explained
Sample size and a 95% interval on every headline number

Read the answer

Not just whether — what, and from where

The full text of every answer is kept and hashed, then taken apart: what changed, what it came from, what is simply wrong.

Answer change feed: the exact words each engine changed, run over run
Provenance: which sentence came from which cited page, and which nothing supports
Knowledge vs retrieval: does the model know you, or only find you
False-claim ledger: one row per thing AI keeps getting wrong, and for how long
Battlecards: the objections engines actually raise, quoted verbatim
Paid vs earned share where the answer surface carries ads

Act

A fix with an owner, not a chart

Every finding becomes a card someone owns, with the artefact attached and the result measured afterwards.

Work Loop board: owner, due date and a history that survives every rescan
Ship-ready llms.txt, JSON-LD, robots rules and content briefs
Answer Deploy: open a pull request containing exactly that llms.txt — and revert it
Two-way Linear sync: close it in Linear, it closes here
Impact ledger: the 14- and 28-day change against a frozen baseline
Winnability score and lift experiments with a holdout arm

Earn citations

The sources the answer is built on

AI answers quote a handful of sites. These reports name them, tell you who they back, and track your way onto them.

Cited sources, typed as review / forum / docs / news / owned
Source value index: which kinds of source travel with a mention
Citation gap: sites cited in your category that never name you
Publisher and journalist graph, down to the named byline
Reddit threads and review platforms the answers lean on
Outreach pipeline with the citation delta after you land

Prove it

Visibility, in money

Connect analytics and the score stops being a vanity metric — down to which prompt the revenue came from.

AI-referred sessions, conversions and revenue per engine, by day
Per-prompt revenue, reconciled to the engine's day total
Search Console joined in, including queries AI answers ate
Agent traffic split by bot: training crawl, search index, live agent
Crawl-to-refer: which engine crawls you daily and never sends anyone
HubSpot: AI-sourced pipeline back, visibility score written on the company

Run the business

Agencies, teams and procurement

Every client brand in one place, your branding on the way out, and the answers an enterprise buyer asks for.

Portfolio across every client brand with its 7-day move and open alerts
Pitch a prospect on credits without spending a paid brand slot
White-label dashboards, revocable client links, scheduled branded PDFs
Partner program: provision client workspaces on negotiated limits, one invoice
Per-brand access, seats, invitations and an append-only audit log
SSO and SCIM, SIEM streaming, and a published trust and subprocessor pack

Anything that depends on a third party — Google Analytics, Search Console, HubSpot, Linear, GitHub, Trustpilot, Reddit — says plainly that it is not connected until you connect it. Nothing is faked with sample data.

Trust

Built to be checked, not just believed.

We publish how the numbers are produced — which surface each engine is asked on, whether it retrieved or answered from memory, how many responses sit behind a figure and how wide the interval is. If the sample is too thin to support a claim, the product says so instead of printing a number.

Stated methodology

Wilson intervals for rates, standard error for scores, sample size on every metric.

Access your auditor can read

Roles and per-brand grants, an append-only audit log, SSO and SCIM, and SIEM streaming into your own Splunk or Datadog.

Procurement pack, checked against the code

Subprocessor list, data-flow diagram and retention schedule — with a test that fails the build when the published retention no longer matches what the database actually does. Read it at /trust.

For agencies

Ship it under your name

White-label

Client-ready PDF

Your organisation in the masthead, selectable text, working links.

Portfolio & pitch

Every client on one screen with its 7-day move — and scan a prospect on credits before they sign.

Alerts where you work

Slack, email or any webhook the moment visibility slips.

Scheduled & partner-ready

Weekly PDFs in each client’s inbox, and a partner program that provisions client workspaces on negotiated limits.

Developer API

Put AI visibility into your own stack.

Everything on the screen is also an endpoint, an event or a file in your warehouse — and an MCP server your own AI assistant can read.

✓REST API with scoped aeo_ keys and a versioned OpenAPI contract
✓MCP server — ask Claude or Cursor about your own visibility
✓Signed webhooks with retries; Zapier, Make and n8n triggers
✓Nightly warehouse export to your own S3, for BigQuery or Snowflake
✓Slack app with slash commands, and a Looker Studio connector
✓Your own hostname serving llms.txt and an MCP endpoint to AI crawlers

Example

GET /api/v1/public/brands/:id/scores

{
  "data": {
    "brandName": "Acme Corp",
    "platforms": [
      { "platform": "chatgpt", "avgVisibilityScore": 72 },
      { "platform": "perplexity", "avgVisibilityScore": 84 }
    ]
  }
}

Before you sign up

See the number before you buy anything.

Live demo

Read a whole workspace, filled in

Every screen you would get, populated for a fictional brand: the score with its interval, all six engines, the prompt table, the work loop and the revenue reconciliation. Sample data, clearly labelled, no account.

Open the demo →

Free grade

Grade your domain in a minute

Type a domain. Real engines answer real buyer questions about it, and you get the score, the gaps and the first fixes — no account.

Run a free grade →

Public index

The AI Visibility Index

Which brands AI assistants name in a category, ranked, with the grounded-or-from-memory mode stated per engine and a dated methodology. Brands appear only when their owner opts in.

Browse the index →

Your site

A badge you can embed

Verify your domain with a DNS record or a meta tag, then paste one line to show your live AI Visibility Score on your own site. Switch it off and it stops serving within five minutes.

Included from Starter.

The claim

Every other tool hands you a score. This one hands you the score, the sample it came from, and how sure it is — and if the sample is thin, it says so instead of rounding.

Nine engines, asked the way a buyer asks. A model reads every answer rather than counting keywords. Every figure ships with its interval, and every claim with the sentence and the source page it came from.

How this is measured

Pricing

All nine engines on every plan.

No per-engine add-ons. You pay for brands and prompts; coverage is never the upsell. The usage meter shows exactly how many AI answers you have used against your allowance, before any invoice.

Starter

$49/mo

For solo operators and founders.

✓1 brand
✓30 prompts
✓5 competitors
✓All 9 engines
✓Weekly tracking

Start free

Rather have it done for you?

Everything in Agency, plus the people. We run the playbook the reports point to and prove the result with the same visibility numbers you see in the dashboard.

✓A named AEO specialist who owns your AI visibility
✓Fixes shipped for you: schema, llms.txt, crawler access, content gaps
✓Citation outreach to the sources AI engines already trust
✓Monthly report: what shipped, and what it did to your visibility

Custom / scoped monthly

10–40 specialist hours a month, invoiced separately from your plan.

Request managed service

Find out what AI says about you today.

Add one brand, watch the first answers land in under a minute, and get a ranked list of what to fix — with the artefact attached and the follow-up measured.

Start free Sign in

Not ready? Get a free AI visibility grade in under a minute →