Integrations & UI
Quoted separately
ERP sync, conversational storefront, attribution, and rollout outside the AI-core scope.
AI Commerce Optimization for Agent-Readable Catalogs
Your store. Agent-readable. AI-shoppable. Shopping agents need structured attributes, live price and stock, and checkout hooks on your APIs. We build catalog enrichment, Schema.org markup, and MCP endpoints for channels like ChatGPT Shopping and Perplexity. Typical timeline: 2–3 weeks.
When to build custom
Worth it when agents misquote price, SKU, or availability because contract tiers, MOQs, or checkout paths are missing from your product data.
- Retailers with 500+ SKUs and gaps in structured attributes: dimensions, compatibility, use case.
- B2B merchants with contract pricing, MOQs, or approval rules that standard product graphs omit.
- Non-Shopify platforms that need MCP or WebMCP checkout for agent-initiated purchases.
- Teams building conversational shopping or procurement assistants on their own catalog.
What you get
Enrichment jobs on your catalog, Schema.org Product and Offer markup, and MCP endpoints for live inventory, price, and cart on your APIs.
- Structured catalog. Product attributes, compatibility, dimensions, and use cases as structured fields. Agents map buyer intent to SKUs from specs and offer data.
- Enrichment pipeline. Gap detection and fill across large catalogs: missing specs, inconsistent variants, and cannibalized titles at SKU scale.
- Agent-readable schema. Schema.org Product and Offer markup aligned to how shopping agents compare and recommend.
- MCP checkout. Model Context Protocol or WebMCP endpoints so agents query live inventory, price, and cart state on non-Shopify stacks.
- B2B pricing graph. Contract tiers, MOQs, and approval rules exposed as structured data where standard storefronts hide them.
How we deliver
Scope & estimate
Intro call: we map catalog size, platform, agent channels, and checkout requirements. You get a fixed-scope estimate before any paid work.
Catalog audit
We sample your catalog for structured-data gaps and checkout readiness. A short audit confirms scope before contract.
Contract & payment
We agree scope, milestones, and IP terms in the contract. Work on the AI core starts after the first milestone is paid.
Deliver AI core
We build the commerce module: enrichment, structured output, schema deployment, and MCP checkout hooks on agreed scope.
Integrate if needed
Optional phase: ERP sync, conversational storefront, attribution, or multi-store rollouts. Quoted separately if outside the initial scope.
Pricing
Fixed scope and price for the AI module, agreed before the build starts.
AI module
$6,900+
Catalog enrichment, structured data, schema, MCP checkout hooks.
2–3 weeks for a defined catalog slice; full catalog or ERP, longer delivery.
Final module price depends on SKU count and checkout path. Deliverables are fixed during the catalog audit before contract.
FAQ
What is AI commerce optimization?
R[AI]SING SUN builds catalog infrastructure for AI shopping agents: attribute enrichment, Schema.org markup, and MCP or WebMCP endpoints for live inventory and price. Output is structured SKU, spec, and checkout data agents pull from your APIs.
How much does an AI commerce module cost?
The AI module starts from €6,000, $6,900 USD, or £5,100 GBP for fixed scope agreed before the build. Final price depends on SKU count, platform, and checkout complexity. ERP sync, conversational storefronts, and multi-store rollout are quoted separately. An intro call gives a range; deliverables are fixed during the catalog audit before contract.
How long does AI commerce optimization take?
Typical delivery is 2–3 weeks after contract and first milestone payment for a defined catalog slice and one checkout path. Full-catalog enrichment or custom ERP integration takes longer. The audit usually takes a few days before the paid build is contracted.
What is included in the AI module vs integrations?
The module price covers enrichment, structured data output, schema deployment, and MCP checkout hooks on agreed scope. Full ERP replatforming, custom storefront UI, and ongoing catalog ops are separate unless scoped. See Integrations & UI on this page.
Do Shopify stores need custom AI commerce work?
Shopify under ~100 SKUs often gets agent checkout from the platform. Custom work fits larger catalogs, B2B pricing rules, non-Shopify stacks, or merchants who need enrichment beyond default schema.
What is MCP for ecommerce?
Model Context Protocol exposes product search, inventory, pricing, and cart actions as tools agents call on your APIs. We build MCP or WebMCP servers when the platform has no native agent checkout.
Who owns the code after delivery?
Rights are defined in the contract before work starts: full buyout or a license for your deployment, with different pricing for each. Module pricing reflects scope fit and reuse of proven pipeline components from prior deliveries, not a greenfield six-figure build from scratch.
Which AI shopping channels does this target?
Structured catalog data and MCP endpoints help wherever agents index products: ChatGPT Shopping, Perplexity, Google AI Mode, and custom procurement bots. Which listings surface depends on each platform; we fix the catalog and API layer you control.
Get in touch
Describe your catalog, platform, and where agents get price or stock wrong today. We'll reply with fit and next steps.
Or email [email protected]