Agentic commerce readiness audit
The way customers find products is shifting. A growing share of purchase journeys now starts inside an AI assistant rather than a search box or a marketplace, and agentic commerce is the name for the layer where those assistants compare, recommend and increasingly complete the purchase on the buyer’s behalf.
What the agentic commerce readiness audit covers
We analyse how AI systems currently perceive your brand, your catalogue and your individual products, then identify what stops those products being surfaced. The audit covers product feed quality, structured data implementation, product attributes and naming, content depth on category and product pages, technical accessibility for AI crawlers, and the consistency of price, stock and specification data across every place it appears.
How your catalogue compares
We also run the same checks against the competitors that assistants currently recommend in your category. That comparison is usually the most useful part of the report: it shows whether you are missing because of a technical gap, a data gap, or simply because nobody outside your own site describes your products in a way a model can quote.
The output
You receive a prioritised action plan rather than a list of observations, ordered by the effort each fix takes against the visibility it returns. Most of the work sits in feed and markup rather than in redesign. Google’s product structured data reference defines the fields that both classic rich results and AI shopping surfaces read.
What comes next
An agentic commerce audit is a starting point, not a project on its own. Stores typically follow it with website optimization for AI services to implement the fixes, LLM visibility monitoring to track whether recommendations actually shift, and ecommerce optimization to convert the traffic once it arrives.
