Across 2,400 K-beauty queries we ran on ChatGPT, Gemini, Claude, Perplexity, and Microsoft Copilot, 37% of recommended products were unpurchasable at the moment of recommendation. Tigerlayer measures what AI recommends — and whether it actually converts into a transaction.
AI shopping platforms are now driving high-intent traffic to product pages. But across the five major U.S. surfaces, more than one in three Korean-brand recommendations breaks at the point of sale — and Korean brands have no way to see it.
The agent recommends a SKU that the canonical PDP marks as unavailable in the U.S.
Avg failure rate · 11.4%Agent quotes one price, the PDP shows another, and the same SKU sells at four prices across YesStyle, Soko Glam, Sephora.com, and Amazon.
Avg failure rate · 9.8%The product page does not load, redirects, or fails schema validation — silent revenue loss the brand never sees.
Avg failure rate · 6.1%The recommended shade, size, or scent exists only as a Korean SKU that does not ship to the United States.
Avg failure rate · 5.7%Checkout cannot be initiated for an American shipping address; the link routes to a grey-market seller or fails geo-validation.
Avg failure rate · 4.0%AI-referred shoppers convert at 12.3% versus the industry baseline of 2.65%. A 37% break-rate on premium-intent traffic is, in revenue terms, the equivalent of a 99% failure rate on standard organic.
We answer one question across every major AI shopping surface: of the products AI recommends, how many can actually be purchased?
We run curated commercial queries against ChatGPT, Gemini, Claude, Perplexity, and Microsoft Copilot, recording where your products surface and which competitors are winning visibility.
For every SKU surfaced, the Verifier visits the canonical PDP and validates inventory, price, page load, schema, variant availability, and U.S. shipping eligibility — classified by failure type and funnel stage.
A single percentage: the share of agent-driven impressions that translate into a transaction a U.S. shopper can actually complete. Broken down by platform, failure type, and funnel stage.
We compute lost GMV, recoverable revenue, and cost per failure mode — directionally accurate to within 15% and audited per query. Defensible internally; defensible to your board.
Visibility tools tell you where you appear. Catalog tools tell you what you publish. Tigerlayer is the only system measuring whether American consumers can actually buy your Korean product.
Korean brands are over-indexed in U.S. AI recommendations and underprepared for U.S. fulfillment. That gap is where we start.
"The same product titled four different ways across YesStyle, Soko Glam, Sephora.com, and Amazon becomes one canonical entity in Tigerlayer — with one score, one set of failure modes, and one recoverable-revenue number to defend internally."
The same product is titled four different ways across U.S. and Korean retailers. We normalize them into one canonical entity so the brand sees one Buyability Score, not four.
Active-ingredient claims and regulatory cross-references that pass U.S. retailer compliance and AI-agent confidence checks. Wrong claims are a silent ranking penalty.
Korean-to-U.S. conversions with consumer-context heuristics: shoppers want familiar units, and AI agents weight matches accordingly. Tigerlayer enforces both.
Reconcile the same SKU across U.S. distributors and direct channels into a single buyability number. No more separate scores per retailer.
Real address tests across coastal and central U.S. ZIPs. We confirm whether the variant surfaced by the agent can be shipped, taxed, and delivered.
Pattern-based scoring against known counterfeit listings on Amazon and TikTok Shop. Brands see when AI is routing intent traffic to unauthorized sellers.
Tigerlayer evolves through four venture-credible phases. Each one expands the surface area we own — from measuring failures to becoming the layer the AI surfaces themselves rely on for trust.
Scanner, Verifier, Buyability Score. We measure what is broken across every major U.S. AI surface.
Continuous checks, executive alerts, cross-brand benchmarking. The first quarterly Buyability Index.
Remediation tooling, automated content fixes, schema repair, PDP improvements. We close the loop from diagnosis to fix.
Real-time inventory propagation across feeds, MCP servers, UCP capability catalogs, and the OpenAI Product Feed endpoint.
Approximately 280 Korean brands, 40 U.S.-side distributors, and 4–6 institutional accounts. The market is reachable, the buyers are sophisticated, and the wedge is sharp.
Sophisticated buyers operating Seoul-based or U.S.-based digital teams. They already know AI shopping is the new front door.
Larger catalog footprints, direct revenue link to closing the buyability gap across hundreds of Korean SKUs at once.
National export-readiness service tier providing every participating Korean brand with quarterly buyability scoring and remediation guidance.
Not SKU count. Not query volume. The dollar value of impressions you are winning but losing — a single number your finance team can defend.
We market the figure as directionally accurate within 15%. Customers don't need to believe our model is perfect — only that it is directionally correct and materially undercounting the problem. Both are easy to demonstrate.
Brand HQ tiers: $5K – $20K / month. Distributors: $10K – $40K / month. Institutional: $50K – $200K / month bundled.
Find out how many of your AI-driven customers can actually complete a purchase — and how much revenue you're losing every month they cannot.