AI moves margin directly in e-commerce
Search, customer service, and content are the functions where AI agents already move revenue and cost today. We build them into your existing system landscape.
AI levers in e-commerce
E-commerce companies sit on large product catalogs, high service ticket volume, and a content need that classic processes can't scale to. Search that only finds exact strings costs revenue. Service teams that handle every request manually cost margin. Content production for thousands of SKUs ties up staff who are needed elsewhere.
We apply AI where it connects to systems that already run: PIM, shop system, ticketing, ERP. Search infrastructure that matches meaning instead of just strings lowers bounce rates. Agents with access to order and product data answer most service requests without escalation. Content pipelines generate product copy and campaign variants for editorial sign-off. The result shows up in conversion rate, ticket cost, and time-to-market for new campaigns.
Functions with the largest lever
Four functions where we've already built production systems, and two more with a direct link to the result.
- 1
Search & product discovery
Semantic search instead of keyword matching, even for catalogs with millions of SKUs
- 2
Customer service
Agents with access to orders and product data resolve standard requests without a queue
- 3
Marketing & content
Content pipelines generate product copy and campaign variants for sign-off
- 4
Finance & reporting
Automated reconciliation of order, payment, and return data for faster month-end close
- 5
Software development
Agentic Engineering speeds up development of the shop, search, and backend
- 6
Supply chain & inventory
Demand forecasting and inventory agents reduce out-of-stock and overstock
→ Margin
Typical use cases
Semantic product search
Search finds products by meaning instead of strings, even for imprecise or spoken queries.
Service agent with data access
An agent with access to orders, returns, and product data answers standard requests without escalation.
Content pipeline for product copy
LLM-assisted creation of product descriptions and campaign copy, editorially reviewed before publication.
Return analysis
Automated categorization of return reasons as a basis for assortment and quality decisions.
Pricing and inventory agents
Rule-based and LLM-assisted agents monitor prices and stock and propose adjustments.
Checkout personalization
Recommendations and cross-selling based on actual purchase history instead of generic rules.
Prerequisites
| Area | Requirement | Typical state |
|---|---|---|
| Data | Product catalog, order, and return data in structured form, accessible via API | Often exists, but spread across PIM, shop system, and ERP without unified access |
| Systems | Shop system, PIM, and ticketing tool with an open API | Often in place; integration effort is usually underestimated |
| Organization | One owner for search, service, and content who is accountable for agent output | Responsibility is often split between marketing, IT, and service and unresolved |
Requirement: Product catalog, order, and return data in structured form, accessible via API
Typical state: Often exists, but spread across PIM, shop system, and ERP without unified access
Requirement: Shop system, PIM, and ticketing tool with an open API
Typical state: Often in place; integration effort is usually underestimated
Requirement: One owner for search, service, and content who is accountable for agent output
Typical state: Responsibility is often split between marketing, IT, and service and unresolved
Our approach in e-commerce
We start with an assessment of the system landscape and data flows between PIM, shop, ticketing, and ERP, because that's where most agent projects fail before they start. The assessment produces a prioritized roadmap with the functions where an agent has the biggest effect on conversion, ticket cost, or content throughput. We build the first use case together with your team, so operational ownership lands where it needs to stay long term.
| Ausgangslage | Umsetzung | Ergebnis | |
|---|---|---|---|
| E-commerce marketplace | Marketplace with over 1 billion products needed a dedicated search architecture. | Scalable search architecture concept with a migration path from the existing solution. | The platform has a search architecture designed for its actual size. |
| E-Commerce, Munich | Several teams shared one frontend and had to coordinate releases. | Micro-frontend architecture with Next.js SSR, backend-for-frontend, and its own DevOps pipeline. | Teams ship their part of the frontend independently, without a shared release window. |
| Enterprise commerce platform, Hamburg | Enterprise commerce provider with a tightly coupled architecture that had grown over time. | Headless architecture with Kafka event streaming and a staged AWS migration. | The platform runs decoupled, with Kafka as the backbone instead of a central monolith. |
Ausgangslage
Marketplace with over 1 billion products needed a dedicated search architecture.
Umsetzung
Scalable search architecture concept with a migration path from the existing solution.
Ergebnis
The platform has a search architecture designed for its actual size.
Ausgangslage
Several teams shared one frontend and had to coordinate releases.
Umsetzung
Micro-frontend architecture with Next.js SSR, backend-for-frontend, and its own DevOps pipeline.
Ergebnis
Teams ship their part of the frontend independently, without a shared release window.
Enterprise commerce platform, Hamburg
Ausgangslage
Enterprise commerce provider with a tightly coupled architecture that had grown over time.
Umsetzung
Headless architecture with Kafka event streaming and a staged AWS migration.
Ergebnis
The platform runs decoupled, with Kafka as the backbone instead of a central monolith.
Frequently asked questions
Initial call: 30 minutes, concrete.
We look at your search, service, or content production and tell you where an agent carries its weight.