E-commerce has made buying products faster than ever. Customers can place an order in seconds and, in many cases, receive it within hours. Yet when something goes wrong, the experience can become considerably slower. Refunds and returns often involve customer-service calls, photographs, forms, manual verification, return pickups, and long waiting periods.
A new generation of multimodal voice AI technology is beginning to address this problem by combining voice conversations with real-time camera-based product verification. Instead of asking customers to explain a problem and then separately upload evidence, AI systems can guide customers through a live interaction in which they can show the product while describing the issue.
Vanira, an AI infrastructure company, is developing this approach to help businesses automate customer interactions involving refunds, returns, product claims, and verification.
The Problem With Traditional Refund Processes
Consider a customer who orders a product and receives it damaged.
The customer contacts support and explains the problem. The agent may then ask the customer to upload photographs or a video of the product. The claim may be sent for review before the company decides whether to approve a refund, replacement, or return.
For customers, this creates unnecessary friction.
For businesses, every additional step creates operational costs. Support agents have to review claims, verify order information, inspect evidence, and communicate decisions. At large e-commerce companies handling thousands of refund requests every day, these processes can consume significant resources.
Returns create an additional challenge because the business may need to arrange reverse logistics after determining that a claim is legitimate.
A Different Approach: Show the Product to the AI
Multimodal AI introduces a different model.
Instead of telling a customer to upload a photograph, an AI agent can ask the customer to show the product to the camera during the conversation.
For example, a customer might say:
“My order arrived damaged.”
The AI agent can respond by asking the customer to open the camera and show the damaged product.
The customer points the camera toward the product. The AI can then analyze the available visual information while continuing the conversation.
This creates a more interactive verification process.
The system can potentially identify the product, inspect visible damage, examine packaging, and compare the information with the customer’s order details and the company’s refund policy.
This is where AI-powered refunds and returns can change the traditional customer-support workflow.
How the Process Could Work
A typical AI-powered refund interaction could follow several steps.
First, the customer contacts support and explains the issue.
The AI retrieves the relevant order information and determines what type of verification may be required.
The customer is then asked to show the product through the camera.
The AI analyzes the visual information and combines it with what the customer has said.
The company’s predefined rules are then applied to determine whether the request qualifies for a refund, replacement, return, or human review.
If the conditions are satisfied, the relevant workflow can be initiated.
The important point is that the AI does not necessarily have to make every business decision independently. Companies can define their own policies and determine which cases can be automatically resolved and which should be escalated.
Applications Beyond Damaged Products
The same approach can be applied to a wide range of e-commerce situations.
Consider a quick-commerce platform similar to Zepto. A customer orders several grocery items but receives the wrong product.
Instead of uploading photographs, the customer could show the delivered product to the AI agent. The system can potentially compare the visible product with the order information and determine whether the situation requires a refund, replacement, or further review.
The technology could also support cases involving damaged packaging, incorrect items, missing products, warranty claims, or product-condition verification.
For businesses, this means the customer conversation itself can become part of the verification process.
Reducing Customer-Service Workload
Refund and return requests represent a significant amount of repetitive work for customer-support teams.
Agents frequently ask similar questions, request similar evidence, and follow similar verification procedures.
AI can automate these repetitive steps while allowing human agents to focus on complicated or unusual cases.
This does not necessarily mean eliminating human support.
Instead, the model can be viewed as dividing customer-service work between AI and human agents. Straightforward claims can move through automated workflows, while ambiguous cases can be escalated to a human representative.
This can potentially reduce handling time while maintaining human oversight where it matters.
Improving the Post-Purchase Experience
The importance of refunds and returns goes beyond operational efficiency.
A customer who receives a damaged or incorrect product is already experiencing a negative moment. A complicated refund process can make that experience considerably worse.
If the company can quickly understand the issue and provide an appropriate resolution, the same situation can be handled with significantly less frustration.
This makes the post-purchase experience an important part of customer retention.
Companies have invested heavily in making product discovery, checkout, payment, and delivery faster. The next opportunity is making the resolution process equally seamless.
The Rise of Multimodal Customer Support
Traditional automated customer support primarily focused on text and voice.
Multimodal AI adds another capability: understanding what the customer can show through a camera.
That capability is particularly useful for problems that are difficult to explain verbally.
A customer can describe a damaged product, but showing the damage provides additional context.
A customer can say that the wrong item was delivered, but showing the product can help verify the claim.
A customer can describe a device error, but showing the device screen can provide more useful information.
Vanira’s approach to multimodal AI for customer support reflects this broader shift toward AI systems that can listen, see, understand context, and connect conversations to business workflows.
As e-commerce continues to grow, companies will increasingly look beyond automating the purchase journey and focus on automating what happens when something goes wrong.
Refunds and returns may have traditionally been viewed as unavoidable costs of doing business. With multimodal AI, they are becoming another opportunity to improve efficiency, reduce friction, and deliver faster customer experiences.
