Agentic CommerceReadiness

The problem: if your products are not in the agents' data pool, you appear in no answer.

Every agent platform sources product data differently. ChatGPT works through structured feeds aligned with the Agentic Commerce Protocol. Gemini and AI Mode rely on Merchant Center and the Shopping Graph. Perplexity works through web crawling and its own merchant programme. Amazon's agent weighs in-marketplace catalogue, A+ content, reviews and logistics signals.

• Access audit for AI crawlers: a deliberate policy at robots.txt and CDN level for GPTBot, ClaudeBot, PerplexityBot, OAI-SearchBot and Google-Extended
• llms.txt setup pointing to product and policy sources
• Merchant Center feed health and attribute enrichment
• ACP-compatible feed extension and incremental update infrastructure
• Server-side rendering of product pages, so all product information is reachable without JavaScript
• Citation layer: agents look not only at a seller's own claims but at consensus in independent sources. This layer runs jointly with AX.

Output: discoverability report per channel, crawler access roadmap, feed health score and fix list.

The problem: the agent found your product but misread it. It thought an in-stock variant was sold out, could not work out the delivery window, could not find the return policy. Result: the competitor was recommended.

Agents read structured key-value pairs, not marketing copy. Abstract descriptions written for the human eye are noise to an autonomous decision-maker.

• Product and Offer audit: GTIN, MPN and SKU identifiers; price, currency and availability fields
• Variant hierarchy: size, colour and material variations separated with hasVariant, each variant carrying its own price and stock
• Technical specifications encoded through additionalProperty in standard units
• Shipping terms in OfferShippingDetails, returns in MerchantReturnPolicy, warranty in WarrantyPromise
• Verification of price and stock consistency across feed, product page, structured data and API
• Assessment of a .well-known merchant manifest for UCP

Output: product data quality score, missing attribute map, structured data templates for policy pages, consistency audit report.

The problem: the agent found the product, understood it, recommended it. The user said "buy". Checkout is closed to agents. The sale is lost.

This layer is what separates Agentic Commerce Readiness from classic search optimisation. Here the agent stops being a search engine gathering information and becomes a proxy carrying payment authority on the user's behalf. The overwhelming majority of agent requests target product pages, while interaction with basket and checkout stays in the low single digits. The biggest gap, and the biggest opportunity, sits in this layer.

• Protocol suitability assessment: ACP, UCP and AP2, and when each becomes meaningful for which market
• Exposing the checkout flow through an API while preserving merchant-of-record status
• Offering catalogue search, stock checks, basket creation and checkout sessions to agents as tools via MCP
• Alignment with your payment provider: shared payment tokens and agent payment programmes, with a migration plan for local infrastructure
• Agent authentication: separating legitimate shopping agents from malicious bots with signature standards, and making sure firewall rules do not block legitimate agents
• Testing 3D Secure and OTP flows under an agent scenario, and defining the thresholds that require human approval
• Redesigning spending limits, authority scope, fraud and chargeback processes for the agent scenario

Output: protocol suitability matrix, checkout API design document, payment integration plan, security and authorisation architecture, autonomous payment simulation report in a sandbox.

The problem: site sessions are falling but orders are rising. Which channel gets credited with agent-driven revenue? When the agent wants the cheapest and fastest option, what happens to your margin?

Traditional web analytics measures a person running JavaScript in a browser. Agents connecting straight to the backend over an API or MCP fire no page view. That analytical blindness misleads investment decisions.

• A dedicated channel group and pattern set for agent traffic in analytics
• Separating traffic at server and CDN log level into human, search bot, training bot and verified shopping agent
• A new generation of metrics: agent impression rate, inclusion in recommendation lists, autonomous basket creation, autonomous conversion rate
• Visibility measurement with Brantial: your brand's share across six answer engines, a citation map and your position against competitors
• Consistency monitoring: live tracking of price and stock mismatches between feed, product page and API
• A margin protection rule engine: closing express shipping to agents on low basket values, halting autonomous purchase at critical stock levels, requiring human approval above a threshold
• GDPR and local data compliance: the data scope opened to agents, cross-border transfer, consumer rights

Output: agent traffic and revenue dashboard, monthly visibility report, consistency alerting, governance policy and risk register.

A shopping model in which an AI agent discovers and compares products and completes the purchase within the authority and spending limit the user defined. The agent does not merely recommend; it executes the transaction.

AX makes agents understand your brand correctly. Agentic Commerce Readiness makes agents able to buy from your brand. One is the comprehension layer, the other the transaction layer.

Yes, for two reasons. First, most of the readiness work (feed, structured data, policy readability, measurement) is protocol independent and can be done today. Second, in-marketplace agents already operate in many markets and decide on catalogue quality.

The checkout layer is currently US focused. The recommendation and comparison layer works globally and looks at feed quality. We prepare your stack today and switch on payment integration as local infrastructure matures.

Partly. Agentic Storefronts and the catalogue layer make the feed side considerably easier. The quality of your product data, policy readability, security rules and measurement remain your responsibility.

In-marketplace agents look at catalogue completeness, review quality, return rate and logistics performance. The focus in this segment is managing catalogue and operational signals for agent algorithms.

It depends on the target market. For US-focused D2C, ACP and UCP come first. For a domestic brand the sensible order is protocol-independent foundations, then a UCP manifest and MCP tools. We take that decision together during the audit.

Custom channel groups in analytics capture traffic that carries a referrer; traffic over API and MCP is separated at log level. Brantial measures the visibility side. Together the three give the full picture.

Proof of authorisation is critical where the cardholder is not present. The cryptographic tokens and signed-authority mechanisms brought by the card networks and platform protocols exist for this. In the audit we revisit your fraud and chargeback processes for this scenario.

Which data is opened to agents, cross-border transfer and consumer rights all fall under data protection law. In the governance layer we define your data sharing policy in that frame and keep the area under review, since formal regulatory guidance is still forming.

You need to separate training bots from shopping agents. Blocking shopping and search agents closes off the sales that would come through the agent channel. In the audit we design a deliberate access policy.

Agents read logic, not visuals. Control is preserved through the clarity of your data and the openness of your policies. Merchant-of-record status keeps the customer relationship with you.

The free score takes minutes. The audit takes three to five weeks. Implementation varies with scope and stack. Monitoring runs on a monthly cycle.

Data integrity and readability fixes show up in the visibility score within weeks. The impact of the transaction layer depends on the target protocol going live in your market.