In the age of conversational shopping, product discovery is no longer happening on search bars or category pages. It’s happening in chat windows. When a shopper asks ChatGPT for “the best running shoes for high arches,” an unseen engine selects, describes, and orders products based on its own understanding of relevance. This emerging space, what Profound calls the “invisible commerce layer”, is quickly becoming the new front door to retail. Yet for brands, it remains a black box.
Profound, a one-year-old startup focused on AI visibility, has built its business around decoding that opacity. Its latest release, Shopping Analysis, promises a window into how AI assistants surface and rank products, an analytics tool designed for a future where recommendation engines write the store layout.
Turning Prompts into Product Placement
The launch marks a pivotal moment in digital retail strategy. Until recently, brands optimized for search engines and retail media, vying for sponsored slots and keyword rankings. Now, optimization moves inside the dialogue itself. Each prompt—“best moisturizer for dry skin,” “top noise-canceling headphones”—is a potential shelf, where inclusion and ranking depend not on ad spend, but on how well a product is described, structured, and trusted by the model.
Shopping Analysis tracks how those decisions are made. It maps when a product appears, how it’s positioned, and which attributes the AI highlights or ignores. It can even monitor shifts over time, showing marketers whether tweaks to product data or content push them up the list or out of view entirely.
A New Kind of Shelf Space
For decades, visibility has been a matter of endcaps and eye-level placement. In the algorithmic marketplace, the new premium real estate is a sentence inside an AI-generated answer. Profound’s tool allows retailers to audit that space, revealing not only their own presence but also competitors’. It’s the first step toward what some are calling *”*answer engine optimization”—the discipline of ensuring that AI assistants can read, interpret, and recommend products correctly.
Unlike paid placements, these recommendations are built from structured data, availability, and credibility. That means the technical hygiene of a brand’s product feed now shapes sales outcomes. Clean metadata, accurate specs, and fresh pricing aren’t just good practice, but actually what determines whether an AI considers a product worth mentioning.
The Stakes of Machine Visibility
The implications extend far beyond analytics dashboards. As shopping journeys compress from discovery to decision within a single conversation, missing from the AI’s shortlist could mean losing the sale entirely. The challenge is not just being findable but being explainable, ensuring that what the machine says about your product matches reality.
Errors or omissions can misrepresent value propositions, misstate features, or rank outdated SKUs. Profound’s analytics offer a safeguard, helping brands monitor how assistants portray them, catch hallucinated claims, and adjust their data inputs before reputational drift sets in.
The Future of Influence in AI Shopping
Behind every AI answer sits a hierarchy of trust built from data quality and brand authority, and Shopping Analysis exposes this logic. In doing so, it reframes how marketers think about influence. The persuasive copy and glossy visuals that once defined product marketing now compete with the clarity and completeness of machine-readable facts.
In the long run, brands that invest in structured storytelling, combining technical precision with emotional context, will thrive in this invisible marketplace. In that sense, Profound’s new tool is an early operating system for the next stage of commerce, where shelf space lives inside a paragraph and the algorithm decides what shoppers believe.
