Lex Agentica audits how your products appear, disappear, or get replaced across AI platforms, languages and buyer contexts. Then we show what needs to change to improve your chances of being selected.
For e-commerce brands that need to understand where AI selection is breaking down before investing further in content, feeds, product data or AI visibility work.
There is no page two. No scrolling through options. No second chance to explain your product. Your brand is either selected, misrepresented, replaced or absent.
The audit shows which of those patterns is happening across the platforms, languages and buying contexts that matter to your category. Then it turns the findings into a practical roadmap your team can act on.
See what the audit diagnoses →AI assistants do not exclude brands at random. They exclude them because the data is hard to read, the trust signals are inconsistent, or the product cannot be matched to the real buyer situation. The audit tells you which of the three is happening, where it is happening, and what to fix first.
We identify what prevents AI systems from extracting your products’ key attributes.
When product attributes are ambiguous, missing or unstructured, AI systems may exclude your brand before comparison begins. Competitors with cleaner data fill the gap.
We surface where claims, reviews and external sources create ambiguity.
When claims conflict across your website, retailer listings and review platforms, AI confidence can drop. The result may be hedged recommendations or quiet exclusion, even when you are visible.
We map where product data lacks the context AI needs to recommend you.
Real buying queries carry context: audience, occasion, budget, outcome and constraint. If your product data does not map to those dimensions, AI cannot place you when it matters.
A complete diagnostic delivered in 2 to 3 weeks. Senior-led. Built for internal teams and trusted partners to execute.
No generic score dump. No template work. The goal is to help your team understand the issue, prioritise action and know which function owns each fix.
What we review
What you receive
After the audit, your team has a documented view of where AI commerce systems are recommending, misrepresenting or excluding your brand. The roadmap identifies one or two near-term changes your team can test within 90 days, alongside deeper structural fixes that require more coordination.
This is one anonymised finding from a wider audit. It is not the full deliverable. In a full engagement, this type of finding sits alongside the visibility scorecard, source map, competitor substitution analysis, product data review and prioritised roadmap.
The buyer query
The brand appeared confidently in the English version of the same query across all three platforms. In German, it disappeared on two of the three. Competitors were selected instead, even when the product fit was not clearly stronger.
AI systems had more usable evidence for competitors: stronger German-language review sources, clearer use-case content and more consistent product attributes across retailer and comparison pages.
The recommendation is not “write more content”. Each fix is translated into an owner, priority and evidence requirement: product data, content, retailer management, source trust or governance. The goal is to show what should change first, who should own it and how to validate whether the change improved AI selection.
For commercial leaders responsible for market visibility, customer acquisition and revenue in AI-mediated buying journeys.
Are AI systems recommending competitors before buyers reach our site?
Understand where product discovery and selection are breaking down across platforms and markets.
Which category queries should we be winning, but are currently going to competitors?
Understand how product positioning, trust signals, content and external sources affect AI recommendations.
What should we prioritise: product data, content, reviews, claims, feeds or governance?
Prioritise changes that improve AI commerce visibility without turning the work into another scattered AI project.
Not a fit for brands without an existing digital presence, or for teams looking for implementation services. We diagnose and advise. Execution is delivered by your internal teams or trusted partners.
AI commerce visibility is still a moving field. That is why the audit combines real query testing, source mapping and documented findings, interpreted with senior judgement.
Real buying queries run across ChatGPT, Gemini and Perplexity, observing how recommendations form and where brands surface or disappear.
We identify which third-party sources shape trust, where your brand appears, and how source context varies across markets and languages.
Findings are documented so your team can understand what was tested, what changed, and why the recommendations matter.
75+ documented tests across multiple AI platforms, languages and EU product categories, with ongoing testing as platforms evolve. The audit applies this methodology to your brand specifically.
See selected findings from our research →The standard audit tests across ChatGPT, Google Gemini and Perplexity Shopping. Each weighs product data, sources and buyer context differently, so cross-platform testing is essential. Other platforms, including Microsoft Copilot, Claude or category-specific AI tools, can be added by request and scoped during the intro call.
The standard audit can be delivered in English and Spanish. Testing can include English, Spanish, German and Italian buyer queries, depending on the market and category. Additional languages can be scoped when there are reliable sources, native phrasing requirements or local market evidence to review.
AI-SEO often focuses on mentions, citations or answer visibility. This audit asks a more commercial question: are your products selected when a buyer asks what to buy? We test whether AI systems can read your product data, trust your claims, understand the buyer context and identify which third-party sources shape the recommendation.
Yes. The audit applies to e-commerce brands selling across languages, platforms and markets globally. Lex Agentica is EU-rooted, so the governance layer is strongest for brands operating in, selling into or preparing for the EU market.
The audit produces a prioritised content opportunity map: a list of content assets that would support AI commerce visibility, tied to the buyer queries each asset supports. Content execution is delivered by your internal teams or trusted content partners. We can advise on briefs and quality criteria as a separate engagement.
Yes. The core audit stays the same. If relevant, we can extend the scope to include EU AI transparency review, additional AI platforms, or category-specific tools. These are scoped after the first conversation so the main offer stays clear.
Yes. AI-driven product discovery is already shaping how some buyers research and compare products. Improving AI commerce visibility now helps prepare the brand for the later shift toward agent-led transactions.
A short intro call to understand your context and confirm scope. A written proposal. Onboarding and access to product data samples. Two to three weeks of analysis and testing. A presentation of findings with your team, plus a written report and prioritised action plan.
Send your brand URL and what you want to understand. We review whether the audit is the right fit before recommending next steps.
Senior-led from the first message. We respond within 24 hours, Monday to Friday, CET, with next steps and a proposed time to talk if the audit is relevant.