AI Impact Layer · For your customers
Advise every customer. Visible to every assistant.
Agents advise in the shop, answer service requests and prepare the next order. At the same time, you provide your range, prices and availability in a way that lets your customers’ AI assistants find you and order from you.
The starting point
Consumers and buyers already research and compare in AI assistants; the purchase still happens in the shop. For your direct business, this creates three tasks.
Every customer’s history, needs and context are in your systems. Even so, usually only the key accounts get individual advice; everyone else gets the standard.
Solution: Advisory and service agents
Agents know order history, terms and product data and advise every customer accordingly, in the shop, in the B2B portal and in service. When in doubt, they hand over to your team.
When a customer will order again is often already in the data. Yet the order only comes when the customer thinks of it.
Solution: Reorder agent
The agent recognizes recurring demand, sends a timely reminder and prepares the reorder. The customer confirms, or the agent orders on its own within approved limits.
Assistants answer with what they find in machine-readable form. If product data, prices and availability are prepared for people only, other suppliers end up in the selection.
Solution: Commerce for people and agents
We make your range, prices and availability readable and orderable for agents, through structured product data and MCP servers on your commerce system.
Serve people and agents alike, and you stay in the selection.
Use cases
Agents for your customers contribute to the revenue lever: higher conversion, more repeat purchases, more customers you reach at all.
The agent understands the intent behind a question, even without the exact product name, and recommends from your range, with your prices and your availability. In service, it answers based on order history, tickets and product data and hands over to your team when things get tricky.
Result: higher conversion and faster answers in service.
In the B2B portal and in the shop, the agent suggests the right reorder, with the customer’s terms and at the right time. What only key account managers could do before, every customer now gets.
Result: more repeat purchases and higher revenue per customer.
Structured product data and access for agents ensure that ChatGPT, Copilot and other assistants know your range with current prices and availability and can order wherever you allow it.
Result: your products stay in the selection when agents shop for your customers.
Measured by revenue per customer.
Conversion, basket size and repurchase rate are fixed before the start. Afterwards, your reporting shows what each agent achieves net of its costs.
References
An agent advises only as well as the search, the data and the platform it draws on. These projects created exactly that foundation: search that sells, and commerce platforms that retain customers across all channels.
The outdated search was costing Cyberport measurable conversion. With Algolia integration, a new search experience, and a training program for the internal team, performance was significantly improved.
The goal: turn a leading pet supplies retailer's previously isolated online business into a profitable growth driver. After joint strategy work, foobar Agency built the platform from the ground up – live in six months, with expanded Click & Collect and a loyalty program that works everywhere.
Since 2019, foobar Agency has accompanied porta Möbel on its path to digital market leadership: from legacy analysis via incremental headless commerce to a full composable commerce architecture on MACH principles.
FAQ
AI agents research, compare and buy on behalf of customers. This is already starting: consumers and buyers research and compare in AI assistants, while the purchase still happens in the shop. As soon as agents also complete the purchase, only those who provide range, prices and availability in machine-readable form make it into the selection. We prepare your commerce stack so that it serves people and agents alike.
An agent works with your real data and systems: it knows the catalog, order history and terms, checks availability and can prepare or place an order. Its answers are based on these sources and are traceable.
Guardrails are set before go-live: what the agent may answer and trigger, when it hands over to people, which limits apply to prices and quantities. Every decision is logged, and deviations are detected and, depending on severity, corrected or escalated.
Product data with complete attributes, current prices and availability and, for personal advice, the customer's history. The agent advises as well as this data allows. That is why we review the data before the first agent.
With metrics you already manage: conversion, basket size, repeat purchase rate. For service agents, resolution rate, escalation rate and answer quality are added.
In two weeks and at a fixed price, we assess how well customers and their assistants reach your range today and design the blueprint for your first agent.
We look forward to your enquiry.
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