Industries · E-commerce & retail
Thousands of products, millions of customers, and margins that punish waste.
E-commerce and retail run on volume: product data for every SKU in every channel and language, service requests that spike with every campaign, returns and claims that each need a decision. AI agents handle the volume, humans handle the exceptions and the brand. We build it to survive Black Friday.
Why e-commerce and retail
Retail was automating before AI agents existed. What agents add is the ability to handle unstructured work at the same scale as structured work.
Online retailers already run on systems: shop platform, PIM, ERP, WMS, marketplaces, helpdesk. What those systems cannot do is write a good product description in four languages from a supplier's spec sheet, understand that a customer's angry message is actually a question about a partial delivery, or decide whether a returns photo shows a defect or normal wear. That work has been done by people, in volumes that grow with every SKU and every campaign.
Agents change that. They enrich product data with approval workflows, answer the large share of service requests that are really lookups, prepare returns and claims decisions with the evidence attached, and turn reviews and feedback into structured signals for merchandising. The people who used to do the volume now do the judgement calls and the brand work.
The constraints are real: consumer-protection law, transparency toward customers, GDPR for customer data, marketplace policies and, above all, reliability when traffic is ten times normal. We design for the peak, not the average.
- Typical starting pointProduct content enrichment or order-status service agent
- Human checkpointContent approval, refunds and goodwill decisions
- SystemsShopify, Shopware, commercetools, SAP Commerce, PIM, ERP, helpdesk
- ConstraintPeak-season reliability, consumer law, GDPR
Use cases
Eight workflows, from the catalogue to the returns desk.
Product content generation and enrichment
From supplier data, images and spec sheets, the agent drafts titles, descriptions, attributes and SEO text in your tone of voice, translates them for each market and prepares marketplace feeds. Category managers approve in bulk with exceptions flagged; nothing publishes unreviewed.
Customer service for order status, returns and WISMO
"Where is my order", return requests, address changes and invoice copies handled end to end by an agent connected to order management and the carriers, in every language you sell in. Anything involving goodwill, complaints or ambiguity goes to a person with the context prepared.
Returns and claims processing
The agent reads the return reason, the photos and the order history, checks the return policy and warranty terms, and proposes a decision: accept, refund, replace, escalate. Below a defined value the decision executes automatically; above it, a service agent approves.
Review and feedback analysis
Reviews, support tickets and social mentions are classified by product, issue and sentiment, and summarised for merchandising and quality: which products generate returns for the same reason, which descriptions mislead, which suppliers cause complaints.
Pricing and assortment analysis support
The agent collects competitor prices and availability from sources you define, prepares margin and elasticity views, and drafts assortment recommendations. Pricing decisions remain with category management and follow your pricing rules.
Merchandising and campaign content
Landing pages, category texts, newsletter copy and campaign variants drafted from the product data and the campaign brief, checked against brand and legal guidelines, approved by marketing. See AI agents for marketing.
Supplier and catalogue data cleaning
Supplier catalogues arrive in every format. The agent maps them to your attribute model, normalises units and values, detects duplicates and inconsistencies, and flags what it cannot resolve. Data quality improves without a data-entry team.
Fraud and anomaly flags
Unusual return patterns, address mismatches, promo-code abuse and suspicious account behaviour flagged for the fraud team with the evidence assembled. The agent flags; people decide and the rules stay auditable.
Constraints specific to e-commerce and retail
Consumer-protection rules and transparency
Customers must know when they interact with an AI system and be able to reach a person; product claims must be accurate; withdrawal and warranty rights follow the law regardless of what an agent says. Agents work inside these rules with policy checks on outputs, and escalation is always available.
GDPR for customer data
Service agents see names, addresses, order histories and sometimes payment details. Access is scoped to the case, data is minimised, retention follows your policy and the hosting decision reflects the sensitivity. Documentation is prepared for your DPO.
Marketplace and platform policies
Amazon, Zalando, Otto and other marketplaces have their own rules on content, messaging and automation. Agents that generate content or messages for those channels are configured per channel and reviewed against the policy.
Peak-season reliability
A service agent that works in March and collapses in November is worse than none. We design for the peak: load testing, rate limits, graceful fallback to humans when the model provider is slow, and monitoring that alerts before customers notice.
Systems integration
Shop platforms, PIM, ERP, WMS, carriers and helpdesk each hold part of the picture. Agents need reliable connectors and a clear source of truth per data type, or they will give confident wrong answers. Our data foundations service handles this where needed.
How we start
Assess
Two to three weeks: catalogue size and channels, service volumes by category and language, returns process, systems landscape and the peaks in your calendar. Output: ranked use cases with a business case in hours, handling time and content throughput.
Design
Approval workflows for content and decisions, integration with shop, PIM and helpdesk, transparency and escalation design for customer-facing agents, data-protection review and the evaluation set from past tickets or products.
Pilot
Six to eight weeks to a production agent on one category of products or one class of service requests, with real customers, measured against handling time, throughput and quality before the pilot. Timed to be stable before your next peak.
Scale
Extend to further categories, languages and channels, add returns and claims, and hand over to your team or managed AI operations with peak-season runbooks.
Frequently asked questions
Can an agent write product content that sounds like our brand?
Yes, with your tone-of-voice guidelines, examples of your best content and a review loop in the first weeks. Quality is measured by editors rating samples and by conversion on enriched versus unenriched products. Content still gets approved by a person before publication, in bulk for routine products.
How much of customer service can an agent really handle?
The share that consists of lookups and standard processes: order status, returns initiation, address and invoice requests, product questions answered from the catalogue. That share varies by shop, and the pilot tells you yours. Complaints, goodwill and anything ambiguous go to people with the context prepared, which also makes those interactions faster.
What happens on Black Friday?
The agent is load-tested for multiples of normal volume, rate-limited to protect your systems, and falls back to your team automatically if the model provider is slow or unavailable. Monitoring alerts on latency and error rates before customers are affected. We schedule pilots so they are stable well before your peak.
Do customers have to be told they are talking to AI?
Yes, transparency obligations apply to AI systems interacting with people, and it is good practice anyway. The agent identifies itself and offers a human at any point. This is process design, not legal advice; we align the wording with your legal team.
Which shop systems do you work with?
Shopify, Shopware, commercetools, SAP Commerce, Magento and others, plus common PIM, ERP, WMS and helpdesk tools, through APIs or the Model Context Protocol. The concrete integrations are verified during the assessment.
What about our service team's jobs?
The agent takes the repetitive volume; the team takes the cases that need judgement and the customers worth extra attention. Most retailers use the change to stop seasonal overtime and outsourcing rather than to cut the core team. Where it is different, we say so early and plan it with HR.
Related
AI agents for customer service
Ticket triage, agent assist with sources, automated resolution for defined categories and a clean hand-over to people when confidence drops.
AI agents for marketing
Content pipelines with editorial approval, automated campaign reporting, search and AI-answer research, and clean lead hand-offs to sales.
Agentic workflow automation
Multi-step business processes automated end to end by LLM-powered agents across ERP, CRM, ticketing and email, with human approval steps built in.
Data foundations for AI
Pragmatic, incremental data work for agents: inventory, document pipelines, APIs, search, definitions and access control, one use case at a time.
Let's find the first workflow worth automating.
A 30-minute intro call, no slides and no obligation. We listen, ask about your processes, and tell you honestly where AI agents would pay off and where they would not.