AI-native Commerce Audit

AI is transforming the way we search for products, compare offers and make purchasing decisions. If your brand is not properly interpreted by AI systems, it may gradually lose visibility in new discovery and commerce channels.

The question is no longer “Will AI change e-commerce?” but rather whether your brand even exists in this new ecosystem.

The AI-native Commerce Audit allows you to assess your e-commerce platform’s readiness for AI Search, AI-driven discovery and the next generation of shopping experiences.

Until recently, the purchasing process began by typing a search term into a search engine and comparing a few offers. Today, users are increasingly turning to AI tools that help them find products, analyse reviews, compare options and recommend specific brands.

ChatGPT, Perplexity, Gemini and other AI tools are gradually becoming a new gateway to the world of e-commerce. This means that brand visibility no longer depends solely on SEO, performance campaigns or Google rankings.

The following are becoming increasingly important:

• the quality of product data,
• availability and structure of information,
• platform architecture,
• readability of content for AI systems,
• consistency of brand messaging across different channels.

If the AI is unable to interpret your product range correctly, your products may be omitted from recommendations and new shopping paths.

That is why AI Commerce does not begin with the implementation of yet another tool, but with preparing the e-commerce platform, data and shopping experience for a new approach to discovery and purchasing decisions.

The AI-native Commerce audit allows you to assess whether your brand and e-commerce platform are prepared for the changing way in which AI systems search for products, make recommendations and influence purchasing decisions.

We analyse how AI interprets your brand, products, content and data, and identify which elements of your platform may be limiting visibility, recommendations and the shopping experience within an AI-native commerce environment.

The result is a set of recommendations showing:

• where the brand loses visibility,
• which technical and structural barriers prevent robots and AI agents from analysing offers and completing the purchasing process,
• what measures can improve discoverability, the shopping experience and an organisation’s readiness for AI Commerce.

AI does not ‘see’ a brand in the same way as a user. It analyses data, information structures, content and signals from multiple sources simultaneously.

During the audit, we check whether the AI systems:

• fully understand what you have to offer,
• are able to interpret products and categories,
• use relevant information in their recommendations,
• see the brand in the right competitive context.

We also identify factors that limit the visibility of products, discoverability, and the interpretability of content and data.

Modern e-commerce must be prepared not only for users, but also for AI systems that influence discovery, recommendations and purchasing decisions. We analyse the quality of product data, platform architecture, API availability and barriers hindering modern shopping journeys.

We also assess whether the platform is ready for the development of AI-native commerce and integrations with modern ecosystems (such as OpenAI, Microsoft Copilot and the Google ecosystem) and new discovery layers – that is, AI tools and search engines that serve as the customer’s first point of contact with a product even before they visit the shop’s website.

An AI-native commerce audit does not end with the diagnosis. The outcome is a set of recommendations designed to help prioritise tasks, identify areas of risk and prepare your e-commerce business for the development of AI-native commerce.

It’s not just about automated purchases made by AI agents. AI systems are already influencing users’ product discovery, recommendations and purchasing decisions.

AI Commerce is transforming the way products are interpreted, recommended and presented in the digital environment.

SEO remains important, but AI-driven discovery works differently from traditional search engines.

AI models interpret data, content, context and information from multiple sources simultaneously. That is why the following are becoming increasingly important:

• the quality of product data,
• information structure,
• platform architecture,
• consistency of content,
• data accessibility via API.

The audit focuses on assessing the brand’s and platform’s readiness for AI Commerce. The outcome is a set of recommendations, an action plan and the identification of areas requiring change.

In the next stage, we can support the organisation in implementing these recommendations — from optimising data and content, through developing the platform architecture, to implementing AI Search, headless commerce and AI agents.

Not always. In many cases, the greatest impact comes from improving data quality, organising information, optimising architecture, and removing barriers that hinder AI systems’ ability to interpret the data.

The aim of the audit is to identify measures that will genuinely enhance e-commerce readiness for AI-native commerce — without the need for a complete overhaul of the entire ecosystem.

The best time is before any issues arise with visibility, discoverability or a decline in the effectiveness of existing discovery channels.

Organisations that get their data, architecture and shopping experience in order early on will be quicker to build visibility and gain a competitive edge in the AI-driven commerce landscape.

The AI-native Commerce Audit helps identify barriers that limit brand visibility, data interpretability and the effectiveness of modern shopping journeys. We show you how to prepare your architecture, content and shopping experience for the development of AI-driven discovery and a new commerce model.