Australian AI Search Visibility Index framework

Direct answer

The proposed Australian AI Search Visibility Index measures whether a brand is mentioned, cited or recommended for a fixed set of commercial questions, whether the stated facts are accurate and which public sources support the answer. Each observation retains the platform, market, date and retrieval conditions so later runs can be compared honestly.

How to use it

Publish the rules before publishing the rankings

The framework fixes the market, category, prompt set, run conditions and classification rules before collection begins. It records answer evidence rather than asking a model to grade itself. Visibility states, factual accuracy and source coverage remain separate throughout reporting.

• 3

  visibility states

  Mentioned, cited and recommended are reported separately.

• 1

  frozen prompt set

  Category, problem, comparison and brand-verification questions are defined before collection.

• Every run

  keeps evidence

  Answer, source URL, platform, market, date and retrieval conditions.

01

Define the market and the observation unit

An index needs a declared category, Australian market boundary, eligible brand set and collection window. A brand's inclusion criteria should be fixed before the answers are observed.

The observation unit is one answer to one frozen question under recorded conditions. The answer—not a later summary produced by another model—is the evidence.

• Category and eligible brand rule.
• Australian market and language.
• Platform and model or product surface.
• Logged-in or anonymous state where observable.
• Web retrieval state where observable.
• Collection date and time.
• Exact question and complete answer.

02

Build questions around real commercial decisions

The prompt set should cover category discovery, problems, comparisons, use cases, eligibility, risk and brand verification. It should not be rewritten after collection to make a preferred company look stronger.

Branded questions test factual accuracy and reputation. Non-branded questions test category discoverability and recommendation. They answer different questions and should not be merged.

Use the search market opportunity map to ground the prompt universe in the decisions customers actually make.

03

Keep mentions, citations and recommendations separate

A mention means the brand appears in the answer. A citation means a visible source link or attribution points to the brand or a page about it. A recommendation means the answer positively selects or shortlists the brand for the stated need.

One answer can contain more than one state. A citation can support a negative statement. A mention can be factually wrong. Reporting the states separately keeps those differences visible.

04

Score factual accuracy independently

Check material claims about the organisation, service, product, people, location, eligibility, price and policy against an inspectable public source. Record accurate, inaccurate, unsupported and unavailable rather than forcing every statement into true or false.

The AI Source Layer is the commercial repair path when official facts are missing, contradictory or too difficult to verify.

05

Preserve answer variance instead of hiding it

Generated answers can change between runs. The framework therefore retains repeated observations and reports the distribution rather than presenting one answer as a permanent rank.

A later edition must use the same eligibility, questions and classification rules or label the break clearly. Method changes can improve the index, but they destroy a clean time comparison unless both versions are retained.

06

Report the useful metrics before any composite score

The first report should show prompt coverage, mention rate, citation rate, recommendation rate, factual-error rate, source-domain distribution and the number of repeated observations. A composite index can be added only when its weights and missing-data treatment are public.

No visibility metric proves revenue. Where referral visits, qualified enquiries or sales can be observed, report them separately and preserve the attribution limit.

07

Turn each gap into a source action

An incorrect official fact becomes a source correction. A missing category answer becomes a page or entity question. A competitor citation becomes a source-quality comparison. An unsupported recommendation becomes a proof and corroboration question. The index is useful only when it identifies the next verifiable change.

Use the AI visibility measurement guide to connect observations to a practical reporting cadence.

08

What this framework does not claim

It does not claim permanent visibility, complete coverage of every private or personalised answer, statistical confidence that has not been calculated, platform preference, a causal ranking factor or a revenue result.

It also does not publish an Australian brand leaderboard yet. Any future edition must publish its eligible set, collection window, sample, definitions, weights and limitations beside the results.

Use it now

Prepare a baseline that can be repeated

1. Declare the category, market and eligible brand rule.
2. Freeze the commercial and branded question sets.
3. Record every answer and retrieval condition.
4. Classify mentions, citations, recommendations and accuracy separately.
5. Retain repeated observations and report variance.
6. Publish definitions, missing-data rules and limits before a score.
7. Route every gap into a specific source or measurement action.