AI search · DTC brands, marketplaces, retail tech
AI search for e-commerce. From "best running shoes 2026" to "your brand" — close the buy-cycle.
Buyers used to read 5 Amazon reviews before buying. Now they ask ChatGPT "best running shoes for plantar fasciitis under $150" and get one sentence back. If your brand is not in that recommendation, the buyer never sees you. The opportunity is large — most DTC brands have great product pages and zero AEO schema.
Why e-commerce is an AI-search opportunity
Where it breaks down.
E-commerce is the highest-velocity AI-search category. Buyers ask comparison questions every day. AI engines refresh recommendations weekly. Where it breaks down: most DTC brands have Shopify or WooCommerce defaults with Product schema only — no Review schema, no Person schema for the founder, no comparison content, no third-party authority signals beyond Trustpilot. Hermes shows where you sit on the per-engine recommendation graph; ContentOS ships the comparison content; Website Agentic Optimization adds the missing schema layers.
Move 01
Product schema + review aggregation
Every product gets full Product schema with Review aggregation from verified sources (Trustpilot, Google Maps, Sortlist). AI engines pull rating + count from JSON-LD, not body text.
Move 02
Comparison content
ContentOS pipeline ships category-comparison guides ("best X under $Y") with structured comparison tables. AI engines cite these for the high-volume comparison queries that drive buy intent.
Move 03
Founder + brand story Person/Organization
Founder Person node with knowsAbout (your category) + sameAs (LinkedIn, X, founder story). Organization with foundingDate + awards + press mentions. AI engines weight authored-brand stories over commodity stores.
90 days
What ships, week by week.
A typical e-commerce engagement, start to finish.
- Week 1. Hermes baseline: per-engine recommendation graph for your top 10 product-category comparison queries ("best X for Y under $Z"), ranked against the 5 closest DTC competitors.
- Week 2–3. Schema rollout: full Product + Review + AggregateRating from Trustpilot/Google/Sortlist, Person node for the founder, Organization with foundingDate + awards + press mentions.
- Week 4–7. ContentOS ships 6 comparison guides ("best X under $Y", "X vs Y vs Z") with structured tables. Each guide a citation candidate for the high-volume comparison queries.
- Week 8–10. Authority graph: founder press placements, podcast guesting, third-party reviewer outreach (independent reviewers your category trusts).
- Week 11–12. Re-scan + report. See which comparison queries now name your brand, what buy-cycle conversion lifts followed, what P2 gaps need a second round.
Scope clarity
What you do not pay for.
Honest about the boundaries. AI search is one slice of your funnel; other slices stay with your team.
- No paid Meta or Google Shopping ads. AI search is earned recommendation, not bought clicks. Paid stays with your performance team.
- No Amazon listing optimization. Amazon is a closed ecosystem and a separate competence. We focus on the open web + AI engines.
- No fulfillment or returns work — that stays with your ops. We hand the citation lift to your existing buy-cycle.
Why this works
Why DTC brands get cited (or do not).
When a buyer asks AI "best running shoes for plantar fasciitis under $150", the model has minutes to assemble a recommendation across hundreds of candidate brands. Three signals win: structured Review aggregation in JSON-LD, an authored founder presence the model can verify, and third-party comparison content beyond the brand site. The brands that lose are not the ones with worse products. They are the ones whose data layer says nothing about the product. Same shoe, two outcomes — the cited brand earns the buy-cycle close.
FAQ
Common questions.
- Q: Does this work for marketplace sellers (Amazon, Etsy, Wildberries)?
- A: Indirectly. The marketplace itself is a closed ecosystem we do not touch. But buyers research outside the marketplace before they enter it. We lift the brand citation in AI engines so that when buyers go to a marketplace they search for you by name. The conversion path stays on the marketplace; the awareness path gets earned through AI.
- Q: Do we need a founder face for this to work?
- A: Not a face, but a name + biography + verifiable expertise. AI engines weight authored brands. A nameless DTC brand reads as commodity; a founder-led DTC brand reads as authored. Even a brief Person node with one sameAs link makes a measurable difference.
- Q: What about review-fraud risk?
- A: We only aggregate from verified third-party sources (Trustpilot verified, Google verified, Sortlist). AI engines weight verified higher than unverified, so synthetic reviews actively hurt now. Our schema work surfaces real reviews and ignores any inflated counts.
- Q: How does this affect paid Meta ads cost?
- A: Indirectly downstream. Branded queries that come from AI recommendations close at a higher rate, which lifts your overall Meta ROAS as more buyers search you by name. We do not run the ads; your performance team sees the lift in their dashboard.
Cases · 2 shipped
What we shipped in e-commerce.
Prior-engagement portfolio (2024–2024) — pre-Hermes, pre-ContentOS. Real client outcomes at scale. The 2026 playbook above is what we ship today; this archive proves we have done the operator work behind it.
Run your brand through Hermes for free.
You arrive to a per-engine citation map of your category, the open gaps, and an honest read on whether any tier fits.