The Problem
Before automation, support agents manually read long tickets, switched between SOP documents, product manuals and order details, and made case-by-case decisions on returns, refunds and warranty claims — with the constant risk of policy mistakes from human interpretation and outdated manuals.
The result was a high manual workload, inconsistent replies, longer resolution times, difficult onboarding for new agents, and poor scalability as ticket volume grew. The team had the knowledge — but no system to retrieve, apply and respond with it consistently.
How It Works
Each incoming ticket is read and classified automatically — ticket type (product usage, warranty, return, refund, missing or defective items, order status, payment), plus the relevant details like order ID, SKU, purchase date and urgency — and the right SOP or manual is selected.
The agent then applies company SOPs exactly as written — warranty rules, return eligibility, refund conditions, price thresholds — and retrieves product-specific answers from the latest manuals via RAG. For every ticket it produces two things: a friendly, customer-ready reply in the customer's language, and internal notes explaining the reasoning and which SOP or manual was used. Manuals and SOPs stay synchronized automatically, so knowledge never drifts.
Impact
- Resolution drafts generated instantly — routine tickets answered in the customer's language without an agent reading a single manual
- ~50–60 hours saved per team each month — freeing 1–1.5 full-time agents, at roughly $30–40/month in AI cost per workflow
- 100% consistent policy application — returns, refunds and warranty rules applied exactly as written, every time
- Zero knowledge drift — product manuals and SOPs stay current, so answers never reference outdated information
