Watch your e-commerce AI shelf

Retail taught everyone the vocabulary: shelf space, facings, planogram. AI answers have quietly rebuilt the shelf — when a shopper asks "best running shoes for flat feet", the answer stocks three to five products, and everything else in the category might as well be in the stockroom. Unlike a store, nobody sends you the planogram. The AI shelf is checkable, though: fetch the answers for your buying queries and read which products they stock. AgentGEO returns those answers as structured records across six engines, so a merchandising team can watch its shelf the way it watches Amazon rank — as a dated feed, not an anecdote.

The job: for each buying query that matters to your catalog, know which products the engines put on the shelf, whether yours are among them, and which retailer or review site the answer cites as the place to buy — because that citation is who gets the click.

How AI-shelf tracking works

1. Build query sets per category, not per SKU. Shoppers ask in category language — "best espresso machine under $300", "gifts for runners". Map each set to the products you'd want stocked in that answer.
2. Fetch across shopper engines. POST /v1/fetches with the buying query; surfaces covers ChatGPT, Gemini, Google AI Overviews, Google AI Mode, Copilot and Perplexity in one contract. Add country on the surfaces that read it — shelves differ by market.
3. Score products and sellers separately. Product names against answerText; your store's domain against sources[].url. Being stocked but not cited means the engine sends buyers to someone else's listing of your product.

import requests

OUR_PRODUCTS = ["AeroGlide 3", "AeroGlide Trail"]
OUR_STORE = "aeroglide.com"

resp = requests.post(
    "https://api.agentgeo.org/v1/fetches",
    json={"query": "best running shoes for flat feet", "surfaces": ["chatgpt", "gemini"]},
    headers={"Authorization": "Bearer ag_live_your_key_here"},
    timeout=200,
)
resp.raise_for_status()

for answer in resp.json()["answers"]:
    text = (answer.get("answerText") or "").lower()
    stocked = [p for p in OUR_PRODUCTS if p.lower() in text]
    sellers = {s["url"].split("/")[2] for s in answer.get("sources") or []}
    print(answer["surfaceKey"])
    print(f"  stocked: {stocked or 'none of ours'}")
    print(f"  cited sellers/reviewers: {sorted(sellers) or 'none'}")
    print(f"  our store cited: {any(OUR_STORE in s for s in sellers)}")

Let your agent run it

Over MCP, the geo-visibility and geo-monitor skills in agentgeo-mcp run shelf checks on a saved query set and diff runs — "which category answers dropped AeroGlide since last month?" is a one-line question once the records accumulate.

claude mcp add agentgeo -- npx -y agentgeo-mcp --key ag_live_...

Alternatives

Digital-shelf platforms (Profitero-class) watch retailer sites — stock, price, search rank inside Amazon or Walmart — and stop at the AI answer layer above them. GEO dashboards watch brands generically without the product/seller split an e-commerce team needs. The raw-records route lets you model the shelf your way and join it to the sales data you already have. The build-vs-buy ledger covers scraping it yourself.

More use cases

The general form of this job is product recommendation tracking; the Google-specific surface has its own discipline in AI Overviews monitoring — see all use cases.