AEO audit methodology
How Precedia measures AI visibility for law firms.
A useful answer-engine audit has to measure what prospective clients actually see. Precedia runs live legal hiring questions, reads the generated answers, and scores the firm from observable evidence: names, ranks, competitors, and citations.
Core principle
The score is not a model opinion.
The audit does not ask an AI model, "Is this firm optimized?" That would create a vague opinion. Instead, the audit asks the kinds of questions a prospective client might ask when choosing counsel.
If the answer engine names the firm, ranks it highly, and cites credible sources, the firm earns visibility credit. If competitors are named and cited instead, the audit records that gap and turns it into a repair plan.
Process
Six steps from firm name to visibility score.
1. Discover the audit scope
The public form stays simple: firm name and city. Behind the scenes, the audit identifies the likely state, official website, and practice-area context before scoring anything.
2. Generate buyer-intent questions
The audit does not rely on one keyword. It builds a cluster of legal hiring questions across high, medium, and research intent, then weights them by commercial importance.
3. Query live answer engines
The questions are sent to configured live providers with search context. The prompt asks for natural answer-engine behavior, not forced inclusion of the audited firm.
4. Parse named firms and citations
Each answer is parsed for whether the firm appeared, its position, the competitors named instead, and the sources cited by the answer engine.
5. Score observable signals
The score is calculated from measurable signals: intent weight, provider coverage, answer position, and citation quality. It is not a model's subjective opinion.
6. Turn gaps into a fix plan
The output highlights which directories, profiles, review sources, structured data, and content assets are missing or thin compared with named competitors.
Signals measured
What the audit reads in every answer.
- Was the firm named in the answer?
- Where did the firm rank among named firms?
- Was the firm cited or only mentioned?
- Which competitors appeared instead?
- Which sources did the answer engine cite?
- Did the firm's own website appear as a trusted source?
Reliability safeguards
AEO visibility is practice-specific.
A law firm should not receive a vague city-wide score if the audit cannot determine what kind of matter the firm wants to be recommended for. The methodology keeps the visible form simple while treating scope confidence as part of the scoring discipline.
- A city-only score is not treated as reliable if the practice area cannot be inferred.
- The audit separates discovery from visibility scoring so the result is not a black box.
- Provider differences are preserved because visibility can vary between ChatGPT, Perplexity, and other answer engines.
- The scoring model favors observable answer behavior over generic SEO assumptions.
Related AEO pages
Explore the high-intent questions this methodology supports.
Answer Engine Optimization for Law Firms
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AI Search Optimization for Lawyers
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AEO Audit for Law Firms
Run an AEO audit for a law firm to see whether AI answer engines name, rank, and cite the firm for local legal hiring questions.