People are now asking AI the questions that used to go to Google. ChatGPT alone is reportedly above 800 million weekly active users, so “recommend me the best X” is becoming a default buying behavior. At the same time, Google is injecting AI answers directly in SERPs, one large study found AI Overviews stabilized around 16% of queries by late 2025. If your brand is not mentioned or, better, cited, you can be ranking and still lose demand.
An AI SEO Audit is the fast way to check that, then fix it. You start with the keywords that make you money, convert them into recommendation prompts, run them across major LLMs, and log 3 things: mention, citation, and how the AI frames you vs competitors. Then you improve what the models actually consume: your source footprint (where they learn about you), your on-page extractability (answers that are easy to lift and quote), and your technical eligibility (indexing, rendering, robots).
Step 1: Select your money keywords
An AI SEO audit is only useful if it’s built on keywords tied to revenue. Skip the “let’s audit everything” approach, you’ll waste time and get noisy results.
Your objective: 50 to 200 keywords that represent what makes money now and what should make money next.
Goal: Define the scope fast
You want keywords that answer:
- What brings leads or sales today
- What buyers search right before choosing
- What AI should mention when people ask for recommendations in your niche
Where to pull keywords: The fastest sources
- Google Search Console: queries from converting pages, last 3 to 6 months
- GA4: landing pages with conversions, map to their main queries
- Paid search: best converting ad groups, pure intent
- Sales calls: exact phrases customers use
- Competitors: 5 to 10 brands customers compare you with
Keep or drop: Simple filter
| Keyword type | Keep when | Drop when | Example |
|---|---|---|---|
| Money intent | Can convert | Pure curiosity | “AI SEO audit service” |
| Problem intent | Clear pain | Too generic | “not cited in ChatGPT” |
| Category intent | Purchase decision | Too broad | “AI SEO tools” |
| Brand intent | Comparison or reviews | Noise | “Brand drama” queries |
Bucket keywords: Avoid overlap
Create buckets so prompts don’t repeat the same test.
- Recommendation: best, top, agency, tool
- Comparison: vs, alternative
- Use case: for SaaS, for ecommerce, for Shopify
- Fix: how to, why not, troubleshoot
Step 2: Turn keywords into prompts that match real AI conversations
Keywords are SEO language. Prompts are buyer language. Your job is to translate each keyword into the question someone would actually ask ChatGPT.
Also: LLM outputs can vary across runs, even with similar inputs, so you need consistent prompt formats to compare results cleanly.
Goal: Build a prompt library that produces stable comparisons
Each prompt should make it easy to log:
- Mention: are you named
- Citation: are you linked or sourced
- Framing: what AI says about you vs competitors
Prompt templates: Use only these 5
| Prompt type | Best for | Template |
|---|---|---|
| Recommendation | Who gets recommended | What are the best [category] for [use case] |
| Comparison | Who wins head to head | Compare [brand A] vs [brand B] for [use case] |
| Alternative | Who replaces a leader | What are the best alternatives to [brand] for [use case] |
| Use case | Niche qualifiers | Best [category] for [industry] with [constraint] |
| Fix | Visibility issues | Why is [brand] not showing up for [intent] |
Prompt rules: Make outputs easy to log
Clear and specific prompts improve output quality and consistency.
- Ask for a ranked list
- Force a format: 5 items, 1 line each
- Add context: country, budget, industry
- Keep 2 to 4 prompts per keyword max
Step 3: Run the prompts on real LLMs and log the output
This is where your AI SEO audit becomes measurable. No vibes, no “I think we’re visible”, just a repeatable test you can rerun next month and compare.
LLM answers change depending on the model, the day, and even the same prompt run twice. OpenAI documents this clearly: chat outputs are non deterministic by default.
Goal: Get a baseline you can compare later
You want to capture 3 signals for every prompt:
- Mention: is your brand named
- Citation: is your brand linked or used as a source
- Framing: how the model describes you vs competitors
Model selection: Use what people actually use
Start with the assistants that dominate usage. StatCounter’s Jan 2026 snapshot shows ChatGPT leads by a lot, followed by Perplexity and Gemini, then Copilot and Claude.
Recommended test set:
| LLM | Why it matters in an AI SEO audit |
|---|---|
| ChatGPT | Highest usage share, sets the “default” narrative |
| Google Gemini | Connected to Google ecosystem, strong for discovery prompts |
| Perplexity | Citation heavy outputs, great for source footprint checks |
| Microsoft Copilot | Common in enterprise workflows |
| Claude or Grok | Claude is common in B2B, Grok is rising fast in the US |
Pick 5 and stick to them for the whole audit. Consistency beats perfection.
Test rules: Keep conditions stable
Small changes create fake insights. Use a strict setup:
- New chat for each prompt
- Same language and same country context
- Same browsing mode setting across runs
- Run each prompt 2 times if results look unstable
- Save proof: screenshot or copy the raw output into your log
Awilix can run your AI SEO Audit end to end, then turn the findings into a prioritized roadmap that increases rankings, LLM mentions, and citations. Book a call with us now
Step 4: Diagnose the gap: mentions vs citations vs narrative
Raw LLM answers are messy. This step turns them into clean diagnosis.
Also quick reality check: AI visibility is volatile. One 2026 tracking study reports only 30% of brands stayed visible from one answer to the next, and only 20% stayed present across five consecutive runs. That’s why you diagnose patterns, not single screenshots.
Goal: Convert outputs into 3 clear buckets
For each prompt cluster, you want one of these outcomes:
- Not mentioned
- Mentioned not cited
- Cited but weak narrative
Once you have the bucket, the fixes become obvious.
Deliverable: Gap map table
This is the table that creates your roadmap in 10 minutes.
| Prompt cluster | Outcome | What it usually means | What to check next |
|---|---|---|---|
| Recommendation | Not mentioned | You are not considered a default option | Source footprint, entity clarity, coverage |
| Recommendation | Mentioned not cited | You exist, but AI does not trust your site as a source | Proof signals, extractability, authoritative pages |
| Comparison | Cited but weak narrative | You are used, but framed poorly | On page positioning, claim proof, specificity |
Bucket 1: Not mentioned: You are not in the model’s shortlist
This is rarely a “page tweak” problem.
Check these levers first:
- Source footprint: do trusted sites mention you at all
- Entity clarity: is your category and positioning consistent across your site
- Coverage: do you have pages that match the prompt intent cleanly
Bucket 2: Mentioned not cited: You are known but not trusted as a source
This is the most common pain.
Typical causes:
- Your pages are hard to quote: no clear answer blocks, weak structure
- Your claims have no proof: no data, no sources, no specifics
- The model relies on other domains that already summarize your niche
Bucket 3: Cited but weak narrative: You are visible but framed wrong
This is the sneaky one. You “win” citations but lose preference.
Fixes usually live here:
- Make your positioning explicit in plain sentences
- Add concrete differentiators: constraints, use cases, pricing logic, outcomes
- Remove vague marketing language that AI rewrites into bland generic text
Mini experiment: The quickest way to spot your real problem
Pick 10 prompts where competitors show up.
If you are:
- Missing everywhere: you need presence across trusted sources
- Mentioned but never linked: you need citation ready pages and proof
- Linked but described poorly: you need tighter positioning and on page clarity
That’s the whole point of an AI SEO audit: fewer guesses, more diagnosis, faster roadmap.
Step 5: Build the roadmap: 4 levers that move AI visibility
Now you have the gap map. This step turns it into actions you can ship.
Think of it like an AI SEO audit scoreboard: you fix what blocks visibility first, then what improves LLM mentions and citations at scale.
Lever A: Source footprint: where LLMs already pull answers from
If you want citations, you need to win the places AI already trusts. Perplexity is the easiest example because it shows citations by design.
What to do
- List the domains that show up most often in citations for your prompt clusters
- Tag them by type: media, directories, forums, review sites, industry blogs
- Pick the 10 that appear repeatedly, those are your target surfaces
Quick wins
- Create 1 page that those sources can link to cleanly: “What we do” + proof + FAQs
- Pitch 3 to 5 niche publications that already rank for your category
Deliverable
- “Source targets” list: Domain, prompt cluster, why it matters, action to get featured
Lever B: On page extractability: can your site be quoted in one clean block
This is the “make it easy to cite” lever. Not prettier content, just more usable content.
What to check
- Does the page answer the question fast, then expand
- Can a model lift a definition, steps, or comparison without rewriting everything
- Are lists and tables doing real work, not decoration
Quick wins
- Add 3 short answer blocks: definition, best practices, checklist
- Add 1 comparison table if the query is recommendation based
Deliverable
- “Extractability fixes” table: URL, section to add, format, expected impact
Lever C: Technical eligibility: can systems access the content
Google is explicit: there are no special tricks for AI Overviews, fundamentals still decide eligibility.
What to check
- Indexed and canonicalized correctly
- Not blocked by robots rules you actually care about
- Main content visible without weird rendering issues
Quick wins
- Fix indexing blockers first, everything else is wasted effort
- Validate structured data and keep it aligned with visible content and policies
Deliverable
- “Eligibility blockers” list ranked: blocker, affected URLs, fix, owner
Lever D: Entity and authority: do you deserve to be recommended
This is where you stop being “a random option” and become “a default pick”.
What to do
- Make your positioning unmissable in plain sentences
- Close content gaps around your highest value prompt clusters
- Add internal links that connect your best proof pages to your money pages
Quick wins
- Add a 5 sentence “entity block” on key pages: who you help, what you do, what you are best at
- Publish 2 supporting pages that answer buyer sub questions, then link them into the main page
Deliverable
- “Authority plan” table: topic gap, target page, new asset, internal links to add
Step 6: Execute fixes: 14 day sprint plan
A roadmap is useless until it ships. The easiest way to execute an AI SEO audit is to run it like a sprint: short, focused, measurable. Scrum defines a Sprint as one month or less, most teams operate faster than that because feedback loops matter.
Goal: Ship changes that improve mention and citation rate
Your sprint needs one rule: every task must change an output you can retest.
Good sprint outputs:
- Fewer eligibility blockers
- More citation ready pages
- Better source coverage on target domains
- Better narrative when the model compares you to competitors
Sprint setup: What goes in the backlog
Keep the backlog small. If it cannot be done in 14 days, it is not a sprint item.
| Backlog item | Lever | Impact | Effort | Definition of done |
|---|---|---|---|---|
| Fix indexation blocker on key page | Eligibility | High | Low | Page indexed and canonicalized correctly |
| Add answer blocks to 5 money pages | Extractability | High | Medium | Clear TLDR plus steps plus table live |
| Publish 2 support pages for 1 cluster | Authority | Medium | Medium | Pages live plus internal links added |
| Get featured on 3 recurring cited domains | Source footprint | High | High | Live mention plus link or profile live |
Prioritization: Use impact vs effort
Do not debate priorities for hours. Use a simple framework, value vs effort is one of the most common because it forces tradeoffs fast.
| Quadrant | What you do |
|---|---|
| High impact: Low effort | Do first, same week |
| High impact: High effort | Plan, assign owner, ship partial if possible |
| Low impact: Low effort | Do only if time left |
| Low impact: High effort | Kill it or park it |
Want the fastest path to more LLM mentions and citations: book a call and Awilix will run your AI SEO Audit, then ship the fixes with you in a 14 day sprint.
FAQ on AI SEO Audits
How many prompts do I need for a reliable AI SEO Audit
For most brands, 60 to 120 prompts is enough to spot patterns. If you cannot explain your results in 5 minutes, you tested too much, not too smart.
Should I test with browsing enabled
Use both.
- Browsing off: tests model memory and training signals
- Browsing on: tests real time retrieval and citations
Keep them separated in your log or you will mix two different realities.
How do I avoid fake citations from LLMs
Treat citations like leads, verify them.
- Open the URL
- Check the page actually mentions you
- Confirm the claim appears on the page
- If it is wrong, mark it as hallucinated and ignore it
What is llms.txt and when it helps
llms.txt is a proposed file to help LLMs pull the right pages at inference time, basically a curated map of your best content. It is not an official standard and adoption varies.
Best use cases:
- Documentation heavy sites
- API products
- Large blogs where models need a shortcut to “the right pages”
Why do some models mention me but never cite my site
Because the model trusts a third party summary more than your pages.
Quick fix that often works: publish a “facts and positioning” page that is easy to quote:
- 5 plain sentences on what you do and who it is for
- proof points with sources
- a short FAQ with crisp answers
