An AI visibility audit establishes your baseline: a clear, current picture of how AI engines treat your brand before you change anything. It answers the foundational questions — do the engines mention you, how do they frame you, are they accurate, and are competitors being recommended over you? Without that baseline, any effort to improve is a guess, and you have no way to tell whether it worked.
AI Visibility & AEO · How-to
How to run an AI visibility audit
The short answer
An AI visibility audit is a structured assessment of how AI engines represent, cite, and recommend your brand right now — your baseline. To run one: define a representative set of buyer questions, run them across the major engines, assess how you show up on each dimension (presence, recommendation, accuracy, competitive standing), and turn the gaps into a prioritized plan. It’s the right first step, because you can’t improve what you haven’t measured.
01 · What an audit is
What an audit is
02 · The steps
The steps
A sound audit follows a simple sequence:
- Define your questions — a representative set of the real questions your buyers ask, across their journey. This is the foundation; a weak question set undermines everything after it.
- Run them across engines — ask each question in each major engine your buyers use, and capture the answers.
- Assess the dimensions — for each, note presence, how you’re framed, accuracy, sentiment, and whether you’re recommended versus competitors.
- Identify the gaps — the questions you’re absent from, the engines where you trail, the inaccuracies, the places competitors win the recommendation.
- Prioritize — rank the gaps by impact, so you start with what matters most.
03 · What you’ll find
What you’ll find
Most first audits surface a few predictable things: questions where you’re simply absent, at least one inaccuracy or outdated detail about your brand, an engine where you’re notably weaker than others, and specific questions where a competitor is consistently recommended over you. None of this is cause for alarm — it’s the raw material for improvement. The value of the audit is turning a vague worry into a concrete, named list.
Related
How AI visibility is measured 04 · Turning the audit into a plan
Turning the audit into a plan
An audit that ends in a report is wasted. The point is to convert findings into prioritized action: correct the inaccuracies, target the high-value questions you’re missing with genuinely helpful content, and pursue the credible sources that would earn you citations where competitors currently win. Prioritization matters — the highest-impact, most-winnable gaps (accuracy fixes, whitespace citations) usually come first because they move the needle fastest.
05 · From audit to ongoing program
From audit to ongoing program
A one-time audit is a snapshot, and AI answers move. The real shift is from auditing to monitoring — running the assessment continuously so you can see whether your fixes worked, catch new gaps as they appear, and track your trajectory over time. The audit is how you start; an ongoing program is how you actually improve and hold your position as the engines evolve.
The takeaway
You can’t improve what you haven’t measured.
FAQ
Common questions
A structured assessment of how AI engines represent, cite, and recommend your brand today — your baseline before making changes.
Define a representative set of buyer questions, run them across the major engines, assess how you show up on each dimension, identify the gaps, and prioritize them by impact.
Turn the gaps into a prioritized plan — then move from a one-time audit to continuous monitoring, so you can see whether fixes worked and catch new gaps.
Author
Anil Jwalanna
Co-founder and CTO
Last updated
Start with your baseline
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