AI Visibility Audit for retail · AI Discovery Intelligence
When a shopper asks ChatGPT, Claude, Gemini, or Perplexity the best product for their need, or the cheapest place to buy it, the engine composes the answer from review sites and marketplaces you do not control - and sometimes gets your price and stock wrong. The shopping journey that used to start on your store starts in a chat window.
28 Labs measures your share of AI recommendations for the queries that drive sales, ties it to your traffic and ROAS, and hands your teams the moves to win it back.
4 engines · intent-scored query universe · every read confidence-tagged
Measured from the outside. No SDK in your rendering path. We do not measure training-data exposure - and we say so.
The queries that move sales
scored
A retailer has tens of thousands of meaningful shopper queries. We score each by intent and track the set that drives sales, not the browsing long tail.
The shift
When a shopper asks an engine what to buy, it composes an answer off your property - you do not know if your store and products came back, or a competitor's.
The product research that used to land on your store now resolves in one answer; if you are not in it, sessions soften and you rebuy them as paid.
We measure your share of AI recommendations across the high-intent shopping queries on all four engines, then tie it to traffic and ROAS.
The board-level risk
A retailer's moat is being the place shoppers go to discover, compare, and buy. Answer engines now sit in front of all three. The exposure is specific, and it is measurable.
"Best {product} to buy" is assembled from review sites and marketplaces - if you are absent there, you are absent in the answer.
Recommendations name a competitor's product ahead of yours, off your property, at the decision moment.
"Cheapest place to buy {product}" gets answered from aggregators - and if your price or stock is stated wrong, you lose the sale before the click.
"Is {store} legit / good returns" gets decided by reviews the engine leans on.
Every figure we put against these is anonymised and confidence-tagged. We show the pattern and the magnitude; we never expose another retailer's numbers to make the point.
The query universe
A retailer's shoppers do not ask four questions. They ask thousands, across a journey. The work is not to track 50,000 random queries. It is to score every query by commercial intent and monitor the high-intent ones that actually drive sales, mapped to where the shopper is in the decision.
Comparison, pricing, and availability queries carry the sales; how-it's-made browsing carries almost none. We weight what you track to where the money is - so the share number you read is the one that matters to the business, not a vanity average across the long tail.
The decision system
Most tools stop at "you were cited less." That is a metric, not a decision. We frame it as market share - the share of high-intent recommendations you hold against the competitor taking them - and we connect that share to the numbers your business already runs on.
How we keep it honest
No one can prove a single AI recommendation caused a single lead. Anyone who claims they can is selling certainty that does not exist. We treat it the way marketing-mix modeling treats a channel: we estimate the contribution from converging signals and we label the confidence of every read, so your analysts can audit any claim back to the evidence behind it.
When a read is directly observed in your own data, we say so. When it is inferred, we say so. When it rests on industry intelligence, we say that too. You never get a confident-sounding number with nothing under it.
Directly observed. The signal is present in your own logs, CDP, or AI-engine output. We saw it happen.
Inferred. Multiple independent signals converge on the same read, but no single source confirms it outright.
Industry intelligence. The read rests on category benchmarks and external patterns, not your own data. Treated as directional.
In your stack
This is not a deck that ages the day it ships. It is a live read wired into the systems you already run, so the share number and its business impact stay current as the engines re-crawl and your competitors move.
The integrated engagement connects to your logs, CDP, and paid feeds, so the AI-share read sits next to the traffic and ROAS data your teams already trust. No SDK in your rendering path.
You get a flag when your share on a high-intent query set moves sharply or a competitor surges, and a monthly read that tracks the trend and ties it to traffic and ROAS. Not once a quarter, on a slide.
HIGH, MED, or LOW on every finding, so the team acting on it knows exactly how much weight it carries before committing engineering or budget against it.
Honest about the tiers. The live read on how much of your traffic and ROAS movement is AI needs your first-party data - your logs, CDP, and paid feeds. That is the integrated, enterprise engagement, not a free snapshot. A standalone audit measures your share of AI recommendations across the four engines from the outside and shows where you are losing ground and to whom. The traffic-and-ROAS loop comes when we wire into your systems.
What winning takes
A retailer wins when it becomes the source the engines trust to answer the category. That is structural. More blog posts will not do it. Being the authoritative, reachable source for the data shoppers actually ask about will.
We diagnose where the engines already trust you, where they trust a competitor instead, and the specific moves that shift that trust toward you. The output is a build plan your teams can act on, not a list of keywords.
Start here
A 60-minute working session. We walk a sample audit, show you how the share read and the traffic-and-ROAS loop would shape up for your category, and scope a live run.
No obligation. We do not measure training-data exposure, and we tell you what we can and cannot see before you commit.