We measure how often your brand appears in AI recommendations, which competitors are being surfaced instead, and what to change to improve visibility in the queries that matter.
Buyers are increasingly asking AI what to buy before they search, browse, or compare. If AI doesn't recognize your brand, it doesn't matter how strong your website, merchandising, or conversion strategy is — buyers may already be choosing competitors before they ever reach your site.
Three recurring factors shape whether a brand gets included in a response:
01
If AI can't quickly place what you are, you're less likely to appear in relevant recommendations.
02
Brands backed by reviews, press, experts, and trusted sources tend to appear more often.
03
Brands that use the words buyers actually use when asking questions are more likely to be included.
A 2–3 week research sprint to understand where your brand appears in AI answers, where it gets replaced, and what to prioritize next.
To understand why competitors are being recommended, where visibility is being lost, and which opportunities appear most likely to strengthen recommendation inclusion.
Established ecommerce brands in competitive categories that want to improve visibility in AI recommendations and better understand why competitors are being chosen.
A clear view of where your brand is missing, which competitors are being surfaced instead, and which priorities matter most.
What's happening in your category and where your brand stands relative to the brands being chosen.
Where you show up, where you don't, and which brands are replacing you in priority queries.
What separates your brand from the brands being included in AI responses more often.
The highest-priority actions across positioning, content, and authority signals.
A working session to review the findings and align on next steps.
The experiment provides a snapshot of current visibility. For teams that want continued insight as AI evolves, monitoring is available as a separate monthly engagement to track recommendation trends, competitor movement, and visibility changes over time.
We publish experiments run on live buyer-style queries across major AI platforms to document which brands get included, which don't, and what patterns keep recurring.
Across 5 ChatGPT queries, one brand appeared in 4 responses while others appeared once or not at all.
The same queries run from two locations returned different brands, showing how context can reshape inclusion.
Across 40 ChatGPT and Claude responses, neither tested brand appeared in 75% of queries.
We run structured buyer-style queries across relevant AI systems, then track what appears, how often, and under which conditions.
Broad, constrained, and comparison-based queries across the AI platforms relevant to your category.
Category framing, buyer-language fit, and third-party validation compared against the brands being chosen.
Inclusion frequency, competitor displacement, and the patterns most associated with stronger visibility.
Across our experiments, the same patterns keep appearing. The PickMeLabs Concept System names the frameworks we use to interpret them.
Whether a brand is likely to be included in an AI recommendation for a specific buying context.
CONCEPT 02Brands that AI systems repeatedly retrieve as default reference points within a category.
CONCEPT 03How constraints like budget, geography, use case, and buyer intent change which brands get recommended.
Start with a conversation. We'll ask a few questions about your category, current visibility, and goals, then tell you whether an experiment makes sense.