Customer Support and Inventory Intelligence

An online retailer receives repetitive questions about order status, returns, delivery, product details, and stock. Support agents move between order systems and policies, while forecasting errors create stockouts and excess inventory.

• Order and inventory facts change frequently; model memory cannot provide reliable live status.
• Return eligibility depends on product, date, location, and order state.
• Customer messages may contain personal information.
• Demand varies with season, promotions, price, channel, and supply constraints.

Combine a permission-controlled support assistant using current policies and live order APIs with a separate demand forecasting pipeline. The assistant answers routine questions and prepares actions, while refunds, cancellations, and sensitive actions require identity checks and explicit authorization.

• Retrieve current order, shipment, inventory, and policy data through approved APIs.
• Authenticate customers before exposing order details.
• Answer routine questions with policy references and escalation routes.
• Use deterministic rules for refunds and cancellations.
• Require confirmation and authorization before account actions.
• Forecast demand using sales, promotions, lead times, stockouts, and seasonality.
• Present inventory recommendations with uncertainty and explainable drivers.

Evaluate resolution rate, answer correctness, escalation appropriateness, and customer satisfaction on reviewed conversations. Test stale data, policy conflicts, unauthorized requests, prompt injection, and API failures. Evaluate forecasts with time-based holdouts and simple baselines by product segment.

• First-response and resolution times.
• Answer correctness and transactional action error rate.
• Escalation and customer satisfaction.
• Forecast error by product segment.
• Stockout, excess inventory, and planner override rates.