When a growing online retailer came to us, their support team was drowning in repetitive tickets — order status, returns, and billing questions that ate up hours every day.

We built an autonomous AI agent that plugs directly into their Zendesk instance and order database. The agent reads incoming tickets, checks order and customer history, and resolves the request end-to-end wherever it safely can — replying, updating records, and only escalating to a human when it hits a genuine edge case.

Within the first month, the agent was resolving 60% of incoming tickets without any human involvement, and average response time dropped from hours to under a minute.

Model Context Protocol (MCP) is quickly becoming our default way to connect language models to real business systems — and our recent inventory assistant project is a good example why.

Instead of writing brittle, one-off integration code every time a model needed fresh inventory data, we built a dedicated MCP server that exposes structured, permissioned access to the client’s ERP. Claude can now query live stock levels, flag discrepancies, and answer natural-language questions from warehouse staff — all without ever touching raw database credentials.

The result is an integration that’s easier to maintain, safer by design, and ready to plug into whichever model the client wants to use next.