
The existing chatbots handled a fixed set of FAQs with no access to live orders, returns, or product availability, so most real customer questions still reached the service team.
Product, order, and customer data lived in separate ecommerce, order management, and CRM platforms, and any AI assistant had to work across all three without replacing them.
The pod studied real customer conversations, APIs, and data flows first, then scoped the workflows an AI agent could resolve end to end.
Engineers built an agentic assistant connected to the catalog, order management, and CRM through secure APIs and MCP tools, with no rip-and-replace.
The agent helps shoppers find products, checks live order status, and starts return requests, all in a single conversation.
Policy guardrails keep answers on track, and complex cases move to a service agent with the full conversation context attached.
One workflow went live first, and new workflows followed as usage and answer quality confirmed value.
Production Go-Live achieved on live catalog, order, and customer data.

Seamless Human Handoff delivered with full context for complex customer cases.

Reusable Agent Framework built for adding new service workflows over time.


