LLM catalog API

Make your retail catalog readable, reliable, and safe for AI agents.

IMV exposes governed product facts, availability, policies, content, store context, and next-action rules so answer engines and shopping agents can respond without inventing retail truth.

Review the API model Connect search commerce

Structured product facts Availability and policy context Safe commerce handoffs

Agent-safe response

{
  "intent": "find travel-ready waterproof jacket",
  "recommendedProducts": [
    {
      "sku": "JKT-STORM-M-NVY",
      "why": "waterproof, packable, in preferred size",
      "availability": "pickup today at Store 014",
      "nextAction": "reserve_for_pickup"
    }
  ],
  "policyContext": "30-day return, unworn condition",
  "handoff": "owned_storefront_checkout"
}

The AI commerce problem

AI agents cannot safely sell from messy, partial, or stale catalog context.

A product feed alone does not tell an agent whether an item is available, eligible, returnable, compatible, margin-safe, store-specific, or ready for checkout handoff.

IMV approach

Expose AI-readable catalog data from the same governed operating layer that controls inventory, orders, content, policies, and ecommerce.

Missing availability

Agents need to know whether a product can be bought, shipped, picked up, transferred, or backordered.

Unsafe actions

AI needs guardrails for pricing, policy, substitutions, customer data, checkout, and support handoffs.

Content gaps

Product copy, buying guides, policy answers, and knowledge content must be structured and linked.

Disconnected systems

A catalog API is weaker when product, inventory, order, policy, and content truth live apart.

API data model

The catalog API should return the context an AI needs to answer responsibly.

IMV packages product, availability, policy, content, and next-action context in a way that is useful to search, copilots, agent workflows, and owned storefront handoffs.

Product facts

Attributes, variants, compatibility, media, fit, materials, restrictions, and related products.

Availability facts

Store stock, warehouse stock, reserved state, transfer options, pickup windows, and ship promise.

Content facts

Buying guides, FAQs, policies, support notes, warranty, returns, and service guidance.

Action facts

Recommended handoff, safe next steps, checkout route, store visit, support path, or notify action.

Agent workflow

From customer question to safe retail handoff.

The API should help AI understand the shopper need, retrieve the right catalog context, explain the answer, and hand off to a trusted retail workflow.

Interpret intent

Need, location, constraints, compatibility, budget, timing, and customer-provided context.

Retrieve context

Products, inventory, policies, guides, store data, and fulfillment options.

Explain recommendation

Return grounded reasons, alternatives, caveats, and freshness metadata.

Handoff safely

Reserve, checkout, contact store, schedule service, notify when available, or route to support.

Governance controls

Freshness and source

Expose when a fact was updated and which system owns it.

Action permissions

Limit what AI can recommend, reserve, quote, or hand off.

Policy boundaries

Prevent unsupported promises for returns, pricing, warranty, availability, or service.

Governed AI commerce

The API should make uncertainty explicit.

AI commerce only works when the model knows what it is allowed to say, which facts are fresh, which actions are permitted, and where to send the shopper when confidence is not high enough.

Review governance model

Where the API can power experiences

One catalog interface for many AI and commerce surfaces.

Expose the same governed facts to site search, shopping assistants, answer engines, service copilots, merchandising tools, and agent-commerce experiments.