Managing Return Authorization and Warranty Status Using Existing Support Documentation
Automate return authorizations and warranty lookups by grounding AI agents in your existing documentation and connecting them to Salesforce and internal APIs.
Customer support agents spend hundreds of hours every month explaining that a thirty day return window expired last Tuesday. They repeat shipping instructions and look up tracking numbers in a CRM while the customer waits on hold. This repetitive work drives up the cost per conversation without improving the actual product.
Most return processes fail because they require two distinct types of information. The agent needs to know the specific policy buried in a PDF manual and they need to see the live status of an order in Salesforce. When these pieces are disconnected, the customer gets frustrated and the agent gets burnt out. This is why anchoring multi channel agents to a single technical knowledge base is necessary for consistency.
Duvi addresses this by anchoring an AI agent in the specific documentation already on a company website. By crawling the site and parsing uploaded files into a vector store, the agent knows the difference between a warranty claim for a motor and a standard return for a casing. It does not guess based on a general language model. It uses the actual text of the business's legal and technical documents.
Actionable agents require more than just reading manuals. To process a return or check a warranty, the agent must interact with external systems. Many companies find that RAG is insufficient for transactional customer support because it cannot take action. Duvi allows these agents to call HTTP APIs or connect to Salesforce as tools. They can also interface with internal databases using MCP servers. Connecting support agents to live business data ensures they can verify a purchase date and shipping status immediately when a customer provides an order number over WhatsApp.
If the policy requirements are met, the agent can trigger an API call to generate a return label or update a ticket status. This avoids what we call the read only tax, where an AI can answer questions but cannot actually resolve the underlying issue. These interactions happen where the customer already is. A user might start a conversation on a website using a single script tag to ask about a replacement part. If they need to step away from their desk, they can continue the same conversation on WhatsApp. The agent retains the context. It uses the same knowledge base regardless of the channel used.
When the conversation requires a phone call, the experience remains consistent. Businesses connect their own Twilio or Meta accounts to Duvi to handle live voice and WhatsApp messaging. The agent talks to the customer using the same data used for text chat. This infrastructure helps in ending the technical follow up cycle by providing immediate answers during the initial contact. If a situation becomes too complex for the AI, such as a high value dispute or a specialized technical failure, the agent hands the conversation over to a human team member.
Implementing this does not require engineering resources or complex infrastructure. The setup involves pointing the crawler at a URL and connecting the communication channels. Usage is tracked through credits, allowing for predictable scaling as volume increases. All completed conversations are recorded and analyzed, providing insights into which policies cause the most confusion or which products have the highest failure rates. The goal is to remove the friction of routine lookups. When an agent can verify a status or explain a warranty limitation without manual intervention, the support team can focus on complex edge cases that require human judgment.