What data you need to start an AI audit

An AI audit doesn’t start with a “site check”, but with locking down the facts: what exactly you sell, where and to whom, and which pages and sources shape how the brand shows up in AI answers. For an AIO audit to produce practical takeaways, you need inputs on your “brand truth”, content structure, EU demand, and current trust signals. The cleaner the inputs, the fewer distortions you’ll see in generative recommendations.

1) The foundation: “brand truth” and the limits of your promises
The first data pack is a short but disciplined document that captures how the brand should be understood by AI systems. This matters because generative answers “stitch you together” from fragments.

What to prepare:

• 1-2 paragraphs of positioning: what you do, who you do it for, and what problem you solve.
• Clear boundaries: what you do NOT do, where responsibility ends, and which conditions/limitations matter.
• A list of products/services and priorities (what must be surfaced in AI answers first).
• Brand terminology: the correct names for the product, categories, roles, and technologies (no synonym chaos).

Tsoden’s materials emphasise that the starting point is establishing an anchor (“brand truth”) before measuring how AI already interprets the business.

2) A map of digital sources: what AI “reads” about you
AI answers are not formed from your website alone. For a solid audit, you need a list of places where the brand is described and cited.

What you need:

• Core website sections: services/products, About, contacts, delivery/payment/returns, policies and terms.
• Blog/media coverage, guest posts, interviews.
• Company profiles and mentions on external platforms (directories, industry portals, social media).

In Tsoden’s approach, this ties directly to which pages and external sources most strongly shape the “brand picture” in AI answers.

3) Data on content structure and “understanding bottlenecks”
To improve AI visibility, the goal isn’t to “rewrite everything”, but to find the nodes where meaning is hard to extract: overloaded paragraphs, mixed topics, and missing direct answers.

What to prepare (if available):

• A list of key pages that drive enquiries/sales.
• Your current site structure (menus, categories, clusters, internal connectedness).
• Recurring customer questions from support/sales (this is prime raw material for AEO/AIO).
• FAQ blocks and policies: these often end up in generative answers.

Tsoden specifically highlights the value of structure: AI systems can use materials more easily when the logic is clear and key questions are answered directly.

4) Search demand and audience “intent” in the EU
AI search amplifies context: language, region, and query phrasing. For an audit, it’s not just keywords that matter, but real user intent.

What to collect:

• Priority EU countries and languages (where you operate now and where you’re expanding).
• Top scenarios: “choose”, “compare”, “is it right for me”, “total cost of ownership”, “how to set up”, “how to return”.
• A list of competitors/alternatives you’re compared with (this affects how AI builds recommendations).

In Tsoden’s EU scaling FAQ, they stress the need to start with an audit and to standardise structure and localisation.

5) Trust signals and proof: what backs up your claims
In the 2026 landscape of AI search, it’s not enough to “say it”. You need anchors that reduce distortion risk and improve citability – AI trust signals.

Useful inputs:

• Case studies (no exaggeration): what you did, the context, and the constraints.
• Public company facts: legal details, team/expertise, geography.
• Policies and terms (especially for eCommerce and services): SLA/support, returns, warranty terms – anything that affects the decision.

Market-trusted sources (media mentions, partnerships, directories).

6) Analytics access (if possible) to link AI visibility to conversions
An AI audit doesn’t have to start with a “tonne of access”, but if you have it, conclusions become sharper. The key is AI analytics combined with baseline web analytics: which pages actually drive outcomes, and where users drop off.

Optional:

• Google Search Console (queries, pages, CTR).
• Web analytics (GA4/Matomo): conversions, paths, top pages, geography.
• CRM/sales data: recurring objections, reasons for loss, common questions.

How this fits into Tsoden’s process
In practice, Tsoden starts with an AIO audit and an assessment of how models “understand” the brand (AI rating), then moves into structure and data optimisation, and maintains results through AI monitoring – regular accuracy checks and strategy iteration.

To start an AI audit, first lock down your core “brand truth” – positioning, key services, terminology, and constraints – then compile your main site pages, external mentions, and recurring customer questions that shape how the brand appears in AI answers. Add supporting trust materials (case studies, policies, company facts) and, where available, analytics to connect AI visibility with real conversions; from there you can move into a full AIO audit and refine your content strategy.