Agentic Commerce: Building the Next Channel Before Competitors Do

Keyword search fails exactly where natural language succeeds: a shopper describing a need ("something for a rainy hike under €150") rather than typing a SKU or category name.

We connect Claude to your existing search and ranking infrastructure through a dedicated discovery skill, so the model reasons about intent while your proven ranking logic still decides what's actually relevant. Results are returned through a structured presentation tool rather than plain text, so they render as proper product cards. We also assess how far your underlying product data needs to improve before this pays off, since discovery quality is as much a data problem as a model one.

The aim is simple: Show the right products for what a customer means, not just what they typed.

An agent can find the right products and build the cart, but checkout is different. It involves real money, customer consent, payment credentials, refunds, and fraud controls. Your existing payment stack needs to know what the agent is allowed to do and when a transaction still needs customer approval.

We connect the agent to your existing cart and payment systems without giving it direct access to move money. We define what it can add to a cart, what it can change, and when it needs to stop and ask for approval. We then map the available payment standards and tokenization options to your existing PSP, acquirer, and payment setup, so you can support agentic checkout without replacing the infrastructure that already works.

The aim is simple: Let customers complete a purchase through the conversation while keeping payment authorization and risk controls in the systems that already govern your checkout.

Composing a multi-supplier purchase (flights, hotels, cars) is a harder reasoning problem than a single product cart, and settling across suppliers and currencies isn't a solved problem either.

We build the planning skill so Claude calls multiple supplier systems in parallel within one conversation, and wire in memory so preferences, such as a preferred airline or loyalty tier, carry across the session automatically. On the payment side, we determine whether your case needs supplier-scoped payment mandates or a merchant-of-record layer that settles one checkout across multiple providers behind the scenes.

The aim is simple: Give customers one coherent recommendation instead of five separate bookings, without pushing settlement complexity onto them.

Post-purchase requests are where customer expectations meet financial risk. Refunds are straightforward when a person handles them, but an agent needs clear rules about what it can approve and when a person needs to step in.

We set up a simple review process so refunds can be prepared by the agent but only issued when the right conditions are met. Every request is checked again before the money moves, so limits and customer details are still valid at that point. We also make sure the refund goes back through the same payment path as the original purchase, including when an agent was involved in the payment.

The aim is simple: Resolve customer issues fast without ever letting the agent hold financial authority it shouldn't have.

Catalog and pricing accuracy degrade quietly across a large product range, and errors here are exactly what eventually surfaces as a wrong price at checkout.

We connect Claude, with read and write access, to your product, inventory, and pricing systems so it can catch drift and stale listings, and set every correction up as a staged change: nothing reaches your live storefront without a person's approval, unless you deliberately choose to automate part of it under a defined policy.

The aim is simple: Keep the catalogue accurate at a scale a manual team can't sustain, without giving up control over what actually goes live.

Promotional and campaign work moves fast, but a discount that isn't validated against the same rules checkout enforces can quietly cost more than it earns.

We connect Claude to your sales and performance data so it can draft promotions and campaigns, choose the right model tier for analysis-heavy work versus latency-sensitive conversation, and validate every draft against the same pricing and cart rules your checkout agent enforces live, before anything reaches a customer.

The aim is simple: Move as fast as the market while keeping every discount inside the boundaries the business actually approved.