We integrated GPT across the customer service stack over 8 weeks: (1) Zendesk GPT integration: incoming tickets are analyzed by GPT-4o — intent classification (order inquiry, return, product question, complaint, sizing), entity extraction (order number, product name, size), automatic data retrieval from Shopify (order status, shipping tracking, return eligibility) and the warehouse system (stock availability, processing status). For routine inquiries, the system generates complete responses in the customer's language and auto-sends after confidence validation. For complex inquiries (complaints, sizing advice requiring product knowledge, multi-order issues), GPT drafts a response with relevant context pulled from the knowledge base, which the agent reviews and sends. (2) Live chat GPT copilot: during live chat conversations, GPT monitors the dialogue and suggests responses to agents in real-time — providing product information, sizing recommendations, policy details, and return instructions. Agents select, modify, or ignore suggestions.
This reduced average handling time while maintaining the personal touch that fashion customers expect. (3) Product knowledge RAG: GPT is connected to the company's product catalog (15,000 SKUs with detailed sizing, material, and care information), return policy, shipping policies, and FAQ content through a RAG pipeline. This ensures responses are grounded in current, accurate product information rather than GPT's training data. Product catalog updates (new collections, discontinued items) are automatically ingested. (4) Multilingual: all integrations handle German (formal Sie-Form for customer communication), English, and French natively. DSGVO compliance: customer personal data is minimized in prompts, processing is on Azure Germany (Frankfurt), and the DPIA was completed before deployment. The Betriebsrat was consulted on the copilot system — the system assists agents but does not evaluate their performance.