E-Commerce Intelligent Operations Agent

Our client operates one of Southeast Asia's fastest-growing cross-border e-commerce platforms, processing 100,000+ orders daily across Shopee, Lazada, and TikTok Shop. Each platform has its own seller backend, its own API, its own promotion mechanics, and its own customer communication protocols.

Their operations team managed this complexity through sheer manpower — customer service agents manually switching between platform dashboards, copying product specs from knowledge bases, typing promotional scripts from memory, and filing after-sales tickets into disconnected systems. During mega-sale events (11.11, 12.12), failure rates spiked 5x as the team was overwhelmed by simultaneous surges in customer inquiries, order processing, and platform-side system updates.

The client had deployed multiple AI chatbot solutions. Each failed on the same operational realities of multi-platform e-commerce at scale.

• 1.The Siloed Platform Problem: Chatbots lived inside a single platform's messaging interface. They couldn't access product knowledge from the brand's PIM system, couldn't check inventory across warehouses, and couldn't execute actions (refund, reship, cancel) across different platform APIs.
• 2.The Conversion Gap: Traditional chatbots answered questions but couldn't sell. They could tell a customer the price, but couldn't generate personalized persuasion or time a payment reminder when a customer abandoned their cart mid-checkout.
• 3.The Duplicate Ticket Explosion: During sales events, the same root cause (e.g., a shipping API timeout) would generate hundreds of identical after-sales tickets from different customers. Each ticket was processed independently, creating massive redundant work.
• 4.The Cost Flatline: Every customer interaction — whether a simple 'where is my order' or a complex multi-issue complaint — was routed to the same expensive model. At 100,000+ daily orders, this was economically unsustainable.