Make your products understandable to AI shopping assistants.

I don’t have enough information about this product to recommend it.
Example response when required product data is missing

We prepare product data, feeds, conversational discovery, merchant-side protocols, checkout actions, and monitoring so assistants can find the right product and verify what they say about it.

Check agent readiness

A complete-looking product page can still contain incomplete data.

Both of these describe the same product. The one on the left is what your customer sees and it looks complete. The one on the right is what an assistant receives.

What a shopper sees
What an assistant reads

Front brake pad set

  • identifierSKU-4471changes on every catalog import
  • titleFront brake pad set
  • price84.00no currency, no tax basis, no validity
  • availabilityIn stocka badge, not a quantity
  • fitment
  • specifications
  • media3 imagesno angle, no alt text
  • returnsa link to /returnsprose an agent cannot evaluate
  • shipping
  • warranty
  • purchase

5 blank·5 present but not machine-readable·1 usableAn assistant has to guess, so it recommends something else.

An assistant needs facts it can verify. If price, stock, compatibility, policy, or purchase data is missing, it may exclude the product or prefer a better-defined alternative.

What makes a catalog discoverable and purchasable by AI.

Start with the layer you are missing. Together, these layers connect accurate product information to a controlled purchase flow.

  1. Product truth and structured discovery

    Scattered catalog information becomes one product source a machine can read and trust.

    • Stable product and variant identifiers
    • Schema.org Product, Offer, shipping, and return data
    • Current merchant feeds, sitemaps, price, stock, and policy data
    • Validation that catches drift between the page, feed, and source system

  2. Conversational product discovery

    Real customer requests can be matched to products using facts and constraints instead of page keywords.

    • Intent and constraint-based product search
    • Comparable facts across products and variants
    • A read-only storefront MCP and, where useful, an NLWeb ask endpoint
    • Honest fallbacks when data or confidence is not sufficient

  3. Permissioned cart, checkout, and orders

    Agent actions are connected only after live commerce systems can enforce the merchant’s rules.

    • Live cart creation, pricing, inventory, tax, and fulfilment
    • ACP or UCP adapters where the channel and merchant qualify
    • Consent and payment boundaries designed for AP2-compatible flows
    • Idempotency, confirmation gates, authentication, and audit logs

  4. Identity, channel adapters, and monitoring

    The same product truth reaches current channels without giving unknown agents uncontrolled access.

    • ChatGPT, Gemini, storefront, marketplace, and custom adapters
    • Bot policy, agent identity verification, scoped permissions, and rate controls
    • Freshness, protocol, visibility, and conversion monitoring
    • Order events, safe failure states, and operational ownership

Build the next layer only when the earlier one is reliable.

Not every merchant needs every protocol. We map the shortest credible route from readable product facts to safe agent actions.

  1. Make product facts machine-readable

    Normalize identifiers, variants, attributes, price, stock, shipping, returns, and policy data.

    Gate: the feed and visible page agree

  2. Publish and synchronize discovery data

    Expose valid schema.org markup and current merchant feeds from an authoritative catalog.

    Gate: changes arrive before stale facts spread

  3. Open conversational discovery

    Add a focused read-only MCP server and, when it improves discovery, an NLWeb-compatible ask endpoint.

    Gate: results are relevant, sourced, and safe

  4. Define who may act

    Set bot policy, agent identity checks, authentication, permissions, consent, and audit boundaries.

    Gate: unknown callers cannot mutate commerce

  5. Connect transaction protocols

    Implement ACP or UCP only for channels and flows the merchant can support end to end.

    Gate: checkout is authoritative and idempotent

  6. Operate the full lifecycle

    Monitor price and stock freshness, order events, failures, refunds, cancellations, and conversion.

    Gate: operations can recover when automation fails

We do not publish an ACP or UCP capability until live catalog, checkout, payment, and order systems can actually honour it.

Connect one product record to current and future channels.

Shopping channels will change. A stable product source and channel adapters let you support new surfaces without rebuilding the catalog each time.

  1. 01

    ChatGPT shopping

    Products can appear inside a visual answer with current facts and a merchant-controlled path to purchase.

    Catalog quality, product feeds, discovery readiness, eligible integration, and a reliable checkout path.

  2. 02

    Gemini and AI Mode

    Customers can research, compare, and check availability inside a conversational flow.

    Structured product information, inventory accuracy, merchant data, and eligible integrations.

  3. 03

    Your storefront agent

    Customers receive guided product matching and controlled actions without leaving your brand experience.

    A focused MCP or NLWeb layer over live search, product, policy, cart, and support systems.

  4. 04

    Future AI and marketplace channels

    New buying interfaces can use the same product source and transaction rules.

    A stable catalog, verified identity, protocol adapters, and observable operations.

Shared underneath all four

One reliable product record, connected to approved actions.

  • Identifiers and attributes
  • Price, stock, and policy
  • Search and comparison
  • Cart and checkout
  • Permissions and audit
  • Freshness monitoring

Where to start.

Most merchants begin with a readiness audit, then fix the data, discovery interface, transaction layer, or channel connection that is blocking progress.

  1. 01

    Readiness audit

    You need a starting point.

    We assess catalog completeness, schema, feeds, source systems, bot access, and which channels or protocols are realistic now.

  2. 02

    Product data rebuild

    An agent cannot trust your catalog.

    We rebuild identifiers, attributes, media, policy, and synchronization rules, then add tests that keep the record reliable.

  3. 03

    Storefront MCP and NLWeb

    You need conversational discovery.

    We expose focused read-only discovery first, then add typed cart or support tools only when identity and permissions are ready.

  4. 04

    ACP or UCP connection

    Your commerce operation is transaction-ready.

    We connect eligible channels to authoritative checkout and order systems with consent, idempotency, observability, and safe handoff.

Can an assistant accurately recommend your products today?

Send a catalog URL and one product an assistant should be able to recommend. We will identify the missing or unreliable fields and explain the practical first fix.

Check agent readiness Or talk directly