Capture intent inside ChatGPT, Claude, Gemini, OpenClaw
SellToAI is the merchant-side bridge into the AI surfaces buyers already trust. The intent is parsed before it leaves the conversation, then routed to your store with full buyer context.
Shoppers are moving from search boxes to AI conversations. SellToAI captures that demand inside ChatGPT / Claude / Gemini / OpenClaw and lets your store answer with real offers — supply, fulfillment, settlement, and attribution stay yours, with content evidence sourced from Moras creators.
A buyer asks an assistant for a product. Your store can answer that intent without the buyer starting on your site.
Price, inventory, checkout, fulfillment, returns, and customer relationship stay connected to your existing commerce stack.
Creator videos, community proof, policy, and MatchToken attribution travel with the recommendation.
Merchant value
SellToAI is not another storefront. It is a sales channel that captures intent inside AI chat, books supply against your stock, fulfills, settles, and attributes — making your existing store legible to AI agents and measurable after the click.
SellToAI is the merchant-side bridge into the AI surfaces buyers already trust. The intent is parsed before it leaves the conversation, then routed to your store with full buyer context.
When shoppers ask assistants what to buy, your store can be returned as a structured answer instead of waiting for search or ads.
AI agents need price, stock, shipping promise, evidence, and validity window. SellToAI packages those into proposal objects agents can rank.
SellToAI runs alongside Shopify, WooCommerce, custom checkout, or your own agent endpoint. It does not take over your store.
Optional supply-chain layer for merchants who want SellToAI to also handle WMS, last-mile carriers, returns, and MatchToken settlement — early-access; you can keep your own ops layer until you opt in.
Every recommendation carries recId and MatchToken context, so clicks, orders, refunds, and feedback can tie back to the original intent.
Before / after
A web store is built for humans browsing pages. An AI store must answer a specific need with a committed offer and proof.
The buyer sees scraped product text, stale availability, weak trust signals, and a link you cannot reliably attribute.
The buyer gets a current offer with inventory, shipping promise, proof, buy path, and a transaction trail your team can reconcile.
Merchant loop
This is the merchant-side story SellToAI makes obvious: demand enters through an assistant, but the sale still lands back on the merchant.
A shopper describes a need in an assistant, agent workflow, or shopping copilot.
SellToAI maps that intent to stores that can actually fulfill the category, region, price, and context.
The offer includes product, price, inventory, shipping promise, evidence, and validity window.
The recommendation gets a path-bound recId and MatchToken for attribution and settlement.
Checkout, fulfillment, refunds, and customer relationship remain tied to your existing operation.
Good evidence, fulfillment, conversion, and feedback make future recommendations sharper.
Why the AI chooses you
The merchant job is not just uploading SKUs. It is giving the AI enough evidence to confidently recommend one offer over another.
Short videos, demos, and product explainers that show the item in use.
External discussion, reviews, comparisons, and real owner notes that reduce buyer uncertainty.
Shipping promise, return window, warranty, region support, and service terms.
Clicks, purchases, refunds, failures, and feedback tied back to intents and offers.
We will reach out as we open the next beta cohort. No spam, no SLA promise — just a real conversation when supply makes sense for both sides.
Brands or stores with live SKUs, fulfillment ownership, creator/community evidence, and a reason AI shoppers should trust the offer.
If price, inventory, shipping, returns, and proof are still unclear, fix that first. SellToAI should route committed supply, not guesses.
TECHNICAL ONBOARDING
This calls the real /v1/merchant/register and agent-readiness endpoints. Use it when you are ready to expose endpoint, supply tags, capability profile, and integration gaps.
POST /v1/merchant/register
curl -X POST https://selltoai.ai/v1/merchant/register \
-H 'Content-Type: application/json' \
-d '{
"merchant_id": "mch_toylab_us",
"agent_endpoint": "https://toy-lab.example/a2a",
"subscription_tags": ["gift", "kids", "toy"],
"capabilities": ["stock_check", "price_quote", "proposal_submit"],
"regions": ["US"],
"workflow_context": {
"origin_workflow": "ChatGPT shopping research",
"required_context": ["budget", "recipient", "occasion"],
"checkout_modes": ["agent_redirect"]
},
"trust_evidence_policy": {
"community_proof_required": true,
"accepted_sources": ["reddit", "creator-video", "merchant-sla"]
},
"agent_ready_profile": {
"proposal_contract": "committed_offer_with_evidence",
"attribution_mode": "match_token_required"
}
}'
Agent-ready workflow contract
The readiness endpoint turns the article insight into a concrete merchant checklist: workflow context, committed proposals, portable trust evidence, and MatchToken attribution.
GET /v1/merchant/:merchantId/agent-readiness
Declare the assistant surface, intent shapes, and context fields needed before ranking supply.
Advertise proposal_submit or a committed_offer_with_evidence contract instead of plain catalog links.
Require Reddit/community proof, creator video, SLA, or other portable evidence the buyer agent can inspect.
Keep checkout modes and attribution explicit so outcomes flow back into reputation.
Merchant integrations must be auditable and reversible because they influence ranking and attribution.