An AI visibility audit sounds like something you pay an agency six figures for. You don't. With a free checker and a fixed set of questions, you can snapshot how ChatGPT, Gemini, Claude and Perplexity see your brand in about ten minutes — and if you rerun it on a schedule, it actually tells you whether your work is moving the needle.
Here's the workflow we use, written so you can copy it.
Why run an AI visibility audit
Traditional SEO audits focus on Google rankings, crawl errors, and backlink profiles. Those still matter, but they miss an entire channel: the answers AI engines give about your brand. An AI visibility audit reveals how those engines see your brand, whether they trust you enough to recommend you, and where the gaps are that let competitors take your spot.
The audit is the diagnostic that connects your technical SEO health to actual AI recommendation outcomes. Without it, you're optimizing for a shrinking portion of search while ignoring the growing portion where AI engines make recommendations.
For a deeper look at the fundamentals, see our guide on what AI visibility is and how to measure it.
Step 1 — Lock your "truth" fields
Locking those fields is built into our ChatGPT recommendation tracking from the start, so every scan stays comparable.
Before you run anything, write down three things and don't change them week to week: your exact business name, your category, and your city. These become the fixed inputs for every audit. If you change them mid-stream, you can't tell whether a score moved because of your work or because you changed the question.
Consistency in your audit inputs is critical because AI engines evaluate your business across multiple sources. If you change your business name between audits, you're measuring a different entity.
Example truth fields for a Toronto bakery:
- Business name: "Flour & Co. Bakery" (exactly this spelling, no abbreviations)
- Category: "Bakery" (match your GBP primary category)
- City: "Toronto" (not "Toronto, ON" — keep it clean and consistent)
These three fields become the anchor for every query you run. Change one, and your month-over-month comparison becomes meaningless.
Step 2 — Build a short, fixed query set
Grab the AI Visibility Checker and let it generate the buyer-intent prompts from your profile — that covers the "who are you, where are you, are you any good" questions automatically. On top of that, add five or six real questions your customers actually type. Pull them from call logs, email, or your search console. Write them down. Keep the same set each month.
The key discipline: a fixed query set. If you keep changing the questions, you're measuring chaos. Same questions every month, then you can see real movement.
A good query set includes:
- Commercial intent queries — "best [service] in [city]," "[service] near me"
- Brand queries — "[your brand] reviews," "is [your brand] good"
- Comparison queries — "[your brand] vs [competitor]"
- Question queries — "how much does [service] cost," "what to look for in a [provider]"
Example query set for a Toronto bakery:
- "best bakery in Toronto"
- "bakery near me Toronto"
- "Flour and Co Bakery reviews"
- "Flour and Co vs [competitor]"
- "custom wedding cakes Toronto"
- "where to buy sourdough bread downtown Toronto"
Keep this list to 8-12 queries. More than that and the audit takes too long; fewer and you don't get a representative picture.
Our guide on AI visibility audit covers building a comprehensive query set.
Step 3 — Run the checker and record the baseline
Paste your URL and fields, run it, and note three numbers for each engine:
- Did it mention you at all?
- If yes, did it match you on your business name or your domain?
- What did it say — favourable, neutral, or ignored?
Also record the website score, the AI score, and the overall grade, plus the list of failed checks. That snapshot is your baseline. Everything after this is compared to it.
The baseline is essential because AI visibility is relative. A score of 50 might be excellent in a competitive market or poor in a less competitive one. What matters is the trend: is your score improving over time?
What to record in your tracking sheet:
| Metric | Month 1 | Month 2 | Month 3 |
|---|---|---|---|
| Overall score | |||
| Website score | |||
| AI score | |||
| ChatGPT mentions | |||
| Perplexity citations | |||
| Gemini presence | |||
| Failed checks |
Keep this sheet simple. You're not building a database — you're building a trend line. The simplest version is a spreadsheet with one row per metric and one column per month.
Step 4 — Turn the fails into a fix list
The failed checks are your to-do list, in priority order. Don't touch the passes. Your first audit session isn't about fixing everything — it's about identifying the five or six most likely blockers, usually things like missing structured data, inconsistent business info, or thin content pages.
Common fail categories and their fixes:
- Indexability issues — Ensure your pages are crawlable and not blocked by robots.txt or noindex tags
- Missing structured data — Add Organization, LocalBusiness, and FAQPage schema
- Inconsistent business information — Align your NAP across all platforms
- Thin content — Expand pages to provide depth and specificity
- Weak trust signals — Build reviews, case studies, and third-party mentions
The prioritization logic:
Not all fails are equal. Prioritize in this order:
- Entity consistency — If your business name, address, or category differs across sources, fix this first. It's the foundation everything else builds on.
- Structured data — Adding schema is fast and has immediate impact. It's the highest-ROI fix in most audits.
- Indexability — If your pages can't be crawled, nothing else matters. Verify your technical setup.
- Content depth — Pages that are too thin for AI to cite need expansion. This takes longer but compounds.
- Trust signals — Reviews, citations, and third-party mentions take time to build but are essential for long-term visibility.
Our guide on AI trust signals explains the specific signals AI engines evaluate.
Step 5 — Rerun the same check next month
This is the step everyone skips, and it's the one that makes the audit worth anything. At the end of the month, run the exact same query set and compare.
- Did a fail flip to a pass? Good — that change was real.
- Is an engine mentioning you that didn't before? That's attribution working.
- Did the website score climb but the AI score stay flat? That tells you the problem is content and entity clarity, not technical setup.
You're not looking for a vanity number. You're looking for which lever moved, so you know what to pull next.
The monthly audit checklist:
- Run the same query set through the AI Visibility Checker
- Update your tracking sheet with new scores
- Compare fails: which flipped from fail to pass?
- Compare engine-by-engine: which engines now mention you?
- Note any competitor changes in the results
- Update your fix list based on what moved and what didn't
Reading the results
When comparing results over time, focus on these metrics:
- Overall score trend — Is your AI visibility improving?
- Engine-by-engine performance — Which engines are responding to your changes?
- Check-level improvements — Which specific fixes moved the needle?
- Competitor comparison — Are you closing the gap with competitors?
The most actionable approach: track the trend, not the snapshot. AI visibility improves with consistent, focused effort — not with one-time fixes.
What good looks like after 3 months:
- Month 1: Score improves 10-20 points after fixing consistency and adding basic schema
- Month 2: Score improves another 5-15 points as content deepens and third-party signals grow
- Month 3: Score stabilizes or improves another 5-10 points as the compound effect kicks in
If you're not seeing this trajectory, the issue is usually either inconsistent execution (you didn't fix all the fails) or a deeper entity problem that needs strategic attention.
For guidance on entity-specific fixes, see our guide on entity SEO for AI search.
The honest take
A ten-minute audit won't replace a deep dive, but it gives you something most brands don't have: a baseline they can actually act on. Run it once to know where you stand, then run it every month to see if the work is working.
The compound effect of monthly auditing is significant. Each month's improvements build on the last, and the trend data proves ROI. Businesses that audit monthly and act on the results consistently outperform those that audit sporadically or never.
For a deeper dive, see our guide on the AI visibility checker guide.
For a deeper dive, see our guide on quick wins to raise your AI visibility score.
At Launch at Dawn we turn this into a managed monthly process for brands in Montreal and Vancouver. book a free website teardown and we'll run your first audit with you, show you the gaps, and leave you with a list of exactly what to change.