Geeky Tech’s AI Search Audit Framework
Table of Contents
In Part One of our AI Search Audit Series, we covered what AI Search is, why it matters for B2B marketers, and why a structured audit is the best way to understand your AI visibility.
You now have everything you need to go through our AI Visibility Audit Framework. Think of it as a map of your AI visibility: each section peels back a different layer, from where you’re cited to how extractable your pages are to how you compare to competitors.
You can use this framework in two ways:
- As a diagnostic: run the full audit to get a baseline of your AI visibility
- As a monitoring system: re-run key sections over time to track how your visibility changes as you make improvements
Let’s take a look at the seven dimensions of the audit, what each one measures, and how to interpret the results, starting with Section 1: Your AI Visibility Baseline.
Tip: We highly recommend you read this framework before moving on to the AI Visibility Audit Workbook in Part Three.
Section 1: AI Visibility Baseline
Why is it called an AI visibility baseline? Because it shows where your brand is and isn’t cited across AI search engines and how that compares to competitors.
What it measures:
- Mention rate and citation rate per engine
- AI Share of Voice overall and by platform
- Gaps where competitors are consistently cited but your brand is not
- Stability of your visibility over time
How to assess your baseline:
Basing your entire strategy on a single snapshot is like pinpointing a plane’s coordinates in mid-flight and assuming it’ll still be there an hour later. We recommend performing multiple audits as AI search results vary substantially over time.
To turn your initial audit into an ongoing task of monitoring your results, we recommend re-running it at least monthly and tracking rolling averages across 4–8 observations.
For a statistical approach, run the same prompt tests multiple times over several weeks, then compare results using:
- Jaccard Similarity (how much overlap there is between result sets)
- Rank-Biased Overlap (how much overlap there is, with extra weight on the top positions)
This approach separates real, repeatable visibility from random fluctuations.
What You Should Be Able to Answer About Your AI Visibility Baseline
Section 2: Content Extractability
As we’ve seen from multiple tests, including our own AI citation study, proper page structure is non-negotiable for AI citation. This section of the audit is the most actionable, as it will tell you exactly which pages to fix and how.
What it measures:
- Clarity and directness of answers
- Heading structure and question formatting
- Use of statistics, sources, and summary blocks
- Overall likelihood of a page being cited
Rubric: Extractability Score (0–10)
Add up each page’s extractability score, then benchmark them based on this rubric:
- 8–10: AI-ready, or a high likelihood of citation
- 6–7: Needs minor improvements
- 4–5: Significant structural gaps
- 0–3: Not extractable. AI will skip this page
If over 80% of your website’s key pages score 7.5 or higher, you’re in a great spot.
What You Should Be Able to Answer About Your Content’s AI-Readiness
Section 3: Technical AI Search Audit
If you’re familiar with SEO, then you probably already know that a poor technical setup is a very silly but very common way to miss out on visibility that you otherwise would have had.
So, incorporating technical AI Search in your audit is crucial. This section determines whether AI crawlers can access, parse, and understand your site’s content. At Geeky Tech, this has been standard practice for our clients since before the dawn of AI.
What it measures:
- AI crawler access (robots.txt rules)
- Schema markup coverage and quality
- Content freshness signals
- Core Web Vitals and page performance
What You Should Be Able to Answer About Your Technical AI Search
Section 4: Authority & Trust Signals
The main goal of this section is to assess how your brand is represented across the various web sources that AI search engines use to generate their answers.
What it measures:
- Presence and completeness on key platforms (reviews, directories, “best of” lists, encyclopedic sources)
- Consistency of brand name, description, and contact info
- Strength of external trust signals relative to competitors
Like SEO, what’s said about your brand outside of your site holds a considerable amount of weight. For example, ChatGPT gives 41% weight to “best of” list mentions. Claude draws 68% of citations from traditional databases and directories.
Rubric: Authority Signal Score (0–3 per signal)
Score each signal:
- 3 – Strong: present, complete, accurate, well‑maintained
- 2 – Partial: exists but is incomplete, outdated, or inconsistent
- 1 – Weak: minimal presence, significant gaps
- 0 – Absent: not present at all
Combine scores across seven signals for an overall Authority Score (0–21).
What You Should Be Able to Answer About Your Authority & Trust Signals
Section 5: Platform-Specific Readiness
Each AI engine uses a different citation algorithm, so your brand may be highly visible in one and almost invisible in another. This section scores how well you’re optimised for each major platform and highlights the changes you should be making for the biggest impact per engine.
What it measures:
- Visibility and citation patterns per engine
- Alignment with each platform’s known ranking and citation signals
- Gaps that are specific to one engine (e.g., schema for AI Overviews, third‑party authority for ChatGPT, reference sources for Claude)
Rubric: Platform Readiness Score (0–5 per engine)
For each major engine, assign:
- 5: Fully optimised
- 3–4: Partially ready, clear gaps
- 0–2: Not visible or fundamentally misaligned
AI Overviews appear directly in Google Search and now show for over 30% of queries. They’re the most SEO‑correlated AI surface, but they apply different citation rules than organic ranking.
Key facts:
- Content length is not a ranking factor. Structure and quality matter more.
- Because AI Overviews link back only 13% of the time, the main value is brand visibility, not direct traffic.
- Results are unstable. The same query can return different citations on different days, so single snapshots can be misleading.
Gemini uses Google’s search index but applies its own rules for what to cite. Among standalone AI assistants, its results are the closest to traditional Google Search rankings.
ChatGPT is powered by Bing’s index and is heavily influenced by third‑party signals rather than on‑page tweaks alone.
Key facts:
- Strong Bing SEO still helps (high alignment with Bing’s top results).
- In our own AI Citation study, we observed more overlap with Google’s top ten search results than Bing’s. Best to optimise for both.
- Wikipedia appears in nearly half of ChatGPT’s top‑10 source citations.
- Brands with complete review profiles have significantly higher citation rates.
- Fresh, original research is heavily favoured.
Perplexity has one of the most documented retrieval pipelines and is strong in research and technical queries.
Key facts:
- 92.78% of cited pages had fewer than 50 referring domains
- Person schema has 2.3 times higher citation rates
- Reddit appears in 46.7% of Perplexity’s top-10 cited-domain share
- Has the highest citation-to-URL ratio, i.e., it links to sources more consistently than other engines
Claude is increasingly used for research, technical, and professional queries and favours established, verifiable sources. What will separate your Claude optimisation strategy from other AI search engines is that it will mainly focus on ensuring your brand is accurately represented in sources Claude already trusts.
Key facts:
- Claude’s Citations API grounds responses in source documents, links claims to supporting passages, and has been shown to reduce source hallucinations and formatting issues from 10% to near-zero for some users.
- On‑site content optimisation alone has limited impact compared with authority signals.
Copilot uses Bing’s index (like ChatGPT) and is integrated into Microsoft 365, Edge, and Windows, giving it significant enterprise reach.
What matters most:
- Very similar to ChatGPT: List mentions, reviews, awards, and Bing SEO.
- Enterprise context may surface different content (e.g., LinkedIn data for B2B queries).
What You Should Be Able to Answer About Each AI Platform
Section 6: Competitive Benchmark
Competitive benchmarking is a crucial aspect of your AI Search audit, as it shows how you stack up against your top 3–5 competitors across all AI Search dimensions.
What it measures:
- Relative AI visibility and citation rates
- Content extractability and technical AI Search posture vs. competitors
- Authority and trust signal gaps
- Platform‑by‑platform readiness compared to rivals
How to assess:
Run the same metrics you’ve already audited for yourself, but side‑by‑side with competitors:
- Visibility and citation rates
- Extractability score distributions
- Technical AI Search pass/warning/fail summaries
- Authority scores and signal matrices
- Platform readiness scores
What You Should Be Able to Answer About Your Brand vs Competitors
Section 7: AI Search Action Plan
You’ve now measured your AI visibility baseline, checked how extractable and technically accessible your site is, assessed your authority signals, compared yourself to competitors, and scored your readiness per platform.
Section 7 brings all of that together into a single, prioritised action plan. It’s the point where the audit stops being a diagnostic and starts driving real changes to your content, technical setup, and third‑party presence.
What it produces:
- A prioritised list of actions (P0: quick wins, P1: content optimisation, P2: authority building)
- Clear owners for each piece of work (dev, content, PR, demand gen, etc.)
- Expected impact per action, tied back to specific audit findings
- A simple progress‑tracking approach (done/in progress/blocked, with re‑audit checkpoints)
How to Structure the Plan
Group actions by priority:
- P0: Quick wins (next 2 weeks)
- e.g., unblock AI crawlers, add critical schema, fix obvious extractability issues on top pages
- P1: Content optimisation (next 1–2 months)
- e.g., restructure priority pages, add statistics and summaries, expand FAQ/HowTo content
- P2: Authority building (next quarter+)
- e.g., pursue “best of” lists, strengthen review profiles, create original research, and improve encyclopedic presence
What You Should Be Able to Answer by the End of Your Action Plan
What’s Next: The AI Search Audit Workbook
You now have a complete framework for auditing your AI visibility across all seven dimensions and turning your findings into a prioritised plan.
In Part Three, we’ll share a DIY AI Search Audit Workbook that gives you everything you need to run the audit yourself.
