Shyna
Kovil AI Assistant
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Deploy cognitive returns fraud inspectors that analyze customer return photos automatically, evaluate past order behavior patterns, and flag high-risk transactions before a refund commits.
The Auditing Pipeline
Trigger, analyze visual assets, and log customer risk parameters.
A customer submits a return request with descriptions and photos of the item through your existing returns portal.
Vision-language models compare photos with catalog records, identifying tag authenticity, damage state, and item match.
Cross-references customer purchase and return history, LTV, and photo audit results into a single fraud risk score.
Low-risk returns under your configured threshold commit automatically; flagged cases route to a human reviewer.
In Practice
A real deployment, walked step by step — from vision-model photo auditing to a multi-agent risk-scoring handoff to the human review that catches what the model flags.
Kovil AI deployed a vision-model auditor that compares return photos against catalog reference images automatically, cutting average processing time from a week down to two days.
The auditor cross-references submitted photos with the original catalog image, flagging inconsistencies in damage description or tag authenticity before any risk score is calculated.
The Photo Auditor analyzes the images, the Risk Scorer weighs that result against purchase and return history, and the Router either auto-approves or escalates — with every decision logged.
Only returns under a configurable dollar threshold with a clean history are auto-approved; anything else routes to a reviewer with the photo comparison pre-analyzed for a fast decision.
Legitimate returns clear in under 48 hours while the reviewer team's time goes only to the cases that genuinely need a second look.
Technical Features
Vision networks scan item labels and tags, identifying wear metrics and preventing worn-and-returned fraud exchanges.
High-value customers with clean purchase histories get instant approvals, routing items directly to the closest warehouse.
Hooks directly into standard e-commerce returns APIs, managing exchange logic autonomously alongside your existing returns platform.
Flags customers with unusual return-rate patterns across their order history, not just single-transaction anomalies.
Walkthrough
A customer submits a return claiming a product arrived damaged, uploading two photos.
The vision model compares the photos against catalog reference images, confirming the item and tag match, but flags an inconsistency in the described damage location.
The risk model checks the customer's return history — a first-time return with strong purchase history lowers the risk score.
Because the inconsistency is flagged but risk is otherwise low, the case routes to a human reviewer rather than auto-approving or auto-denying.
The reviewer resolves it in under a minute using the pre-analyzed photo comparison, instead of starting the investigation from scratch.
Compatibility
Manual Review vs. Auditor Agent
| Capability | Manual Review | Kovil AI Auditor |
|---|---|---|
| Photo review | Manual visual inspection by a rep | Vision-model comparison against catalog images |
| Processing time | 5–7 days typical turnaround | 48 hours average |
| Risk detection | Reactive, based on rep intuition | Cross-order pattern detection and LTV scoring |
| Low-risk case handling | Every case reviewed manually | Auto-approved under configured threshold |
| Consistency | Varies by reviewer judgment | Consistent, logged scoring criteria |
FAQ
Answers regarding vision constraints and approval overrides.
Partner with Kovil AI to map returns schemas and deploy automatic photo verification loops under a 2-week risk-free trial.