Order-Aware Support Agent for Faster Resolutions

Retailers face large volumes of post-purchase contacts about shipping delays, wrong or damaged items, refunds and return logistics. Agents waste time pulling order details, searching policy documents and composing replies, creating long wait times, inconsistent decisions and higher escalation and refund costs.

Build a retrieval-augmented support assistant that surfaces live order context, diagnoses intent, and proposes next-best actions in the agent workflow while gating risky actions for human approval.

• Data connectors: integrate OMS, CRM, warehouse/inventory, and ticketing systems and index product and policy docs into a vector store for fast retrieval.
• Modeling: use an LLM + RAG for intent classification, suggested reply templates and step-by-step resolution plans; fine-tune classifiers on historical tickets for common issue types.
• Workflow: agent UI displays highlighted order facts, confidence-scored suggested responses, pre-approved action buttons (refund, re-ship, return-label) and an option to auto-fill responses for low-risk requests.
• Human-in-the-loop & governance: require agent review for high-value or out-of-policy actions, implement monetary and policy thresholds for automated approvals, log decisions for auditability and include source citations for explainability.
• Operations: continuous monitoring, A/B testing, feedback capture to retrain models, PII redaction, and KPIs tied to AHT, FCR, escalation rate and CSAT.

Illustrative impact: teams typically see a 20-40% reduction in average handle time, a 10-25% improvement in first-contact resolution and 15-30% fewer escalations. That commonly translates to a 15-35% reduction in support cost per ticket and faster return/refund cycles that lower lost-sales risk and improve customer satisfaction.