The D2C brands AI shoppers actually find
ChatGPT, Perplexity, Claude and Google AI Mode are recommending brands today. We’re indexing ~25,000 D2C stores against the seven things shopping agents check before naming a brand. Below: the first 5,575 live, ranked.
5,575
Live on the board25K+rolling out
In the index190.3% largely-ready+
Above the bar100robots.txt disallow
Blocking AI shoppers68/100
Top-10 avgWhen a buyer asks an AI assistant, only a few brands get named.
“best D2C skincare brands for sensitive skin”
For sensitive skin, the brands that come up most consistently across product comparisons are Tower 28, ILIA Beauty, Drunk Elephant, Beautycounter, and Versed. Each has transparent ingredient lists and accessible review aggregations.
“recommend D2C running shoe brands”
Brands cited most often: Tracksmith, On Running, Bandit, and Allbirds Tree Dasher. Tracksmith and Bandit surface most often because both have detailed material/attribute pages plus verified review aggregations.
“best small-batch coffee subscriptions to gift”
Trade Coffee, Atlas Coffee Club, Driftaway, Bean Box, and Counter Culture come up consistently. Trade and Atlas are most often named for breadth; Counter Culture for roast quality.
Cross-vertical leaderboard
All scored brands, sorted by agent-readiness. Click any brand to see its 7-dimension breakdown.
Category leaders
Apparel & Accessories
Top scorers when a shopper asks “best D2C apparel brands”
Beauty & Wellness
Top scorers when a shopper asks “best clean beauty brands”
Food & Drink
Top scorers when a shopper asks “best D2C food brands”
Home & Garden
Top scorers when a shopper asks “best home goods brands”
Pet
Top scorers when a shopper asks “best D2C pet brands”
Outdoor & Sport
Top scorers when a shopper asks “best outdoor brands”
Furniture
Top scorers when a shopper asks “best D2C furniture brands”
Baby & Kids
Top scorers when a shopper asks “best D2C kids brands”
The seven things AI shopping agents check
Each brand is scored 0–100 across seven independent dimensions, weighted by how heavily the major shopping engines actually rely on each signal. Scoring is deterministic — same inputs, same score, no LLM judgement in the loop.
Catalogue completeness
Product count, structured attributes, image coverage. Agents skip catalogues they can't parse.
Pricing clarity
Current price, currency, sale state. Ambiguous pricing kills checkout intent in agent flows.
Reviews schema
AggregateRating + Review in JSON-LD. Reviews are the #1 ranking signal in AI shopping answers.
PDP schema
Product + Offer JSON-LD on every product page. Without it, agents can't extract what they need.
Inventory signal
Real-time availability the agent can trust before recommending — including SKU-level stockouts.
Returns visibility
Returns policy explicit and discoverable. Agents weight returns risk into recommendations.
Agent surface
UCP / ACP / MCP endpoint and feed cadence. The plumbing that lets agents actually transact.
Full methodology and dimension weights are listed on each brand’s profile page. See an example breakdown on the full Agentic-Commerce-Ready Index.
Where to go next
ChatGPT doesn't recommend your store
Run a live diagnostic to see why your brand isn't in the answer.
Agent-readable PDPs
Side-by-side teardowns of the product pages AI shoppers actually parse cleanly.
The 2026 merchant playbook
What agentic commerce is, what it changes, and the steps to get ready.