Return Merchandise Authorization (RMA) Bot

The core decision logic that enforces return eligibility in real time. The bot cross-references the SKU, purchase date, and customer history against a dynamic ruleset to instantly approve or deny a request.

• Blocks returns outside the allowed time window
• Flags items on a non-returnable list (e.g., final sale)
• Checks for serial number blacklists
• Integrates with a Wardrobing Pattern Recognition model to deny fraudulent serial returners

Guides the customer through a structured data capture process that goes beyond simple form fields. The bot requests and analyzes evidence to validate the claim before a label is issued.

• Triggers a Photo Validation Check: requires a real-time image, analyzed by Computer Vision Grading for pre-existing damage
• Calls an OCR Verification service to extract and validate the serial number from the uploaded photo
• Compares the declared reason against the visual evidence to detect mismatches

Once validated, the bot doesn't just authorize a return—it determines the item's next physical destination. It queries an Automated Disposition Engine to generate a recovery plan before the label is created.

• Routes high-value items to a central refurbishment hub
• Directs defective products to a Vendor Chargeback Agent for automated debit note generation
• Sends open-box, sellable items directly back to a regional warehouse for immediate restocking based on a high Restocking Confidence Score

Monitors the customer's language in real time using Natural Language Processing (NLP). When frustration, anger, or confusion is detected, the bot gracefully exits the automated flow and creates a fully contextualized ticket for a human agent.

• Creates a Sentiment-Triggered Exception with full chat transcript
• Attaches all collected evidence (photos, reason codes) to the agent handoff
• Pre-populates a Return Reason Code Normalization suggestion for the agent to approve

Executes a risk-assessment algorithm at the moment of carrier scan to trigger an immediate refund, building post-purchase trust. The bot pre-authorizes this capability based on strict criteria.

• Evaluates the Return Propensity Score of the customer at intake
• Verifies the item is low-risk and low-value
• Issues the refund via a Payment Service Provider (PSP) API the instant the carrier scans the label, not when the warehouse receives it

Translates unstructured customer input like 'it just didn't look right' into a standardized, analytical taxonomy. This closes the feedback loop between customer experience and product quality.

• Maps 'too big' to Size Mismatch
• Maps 'stopped working after a week' to Early Life Failure
• Feeds structured data directly into a Return Rate Anomaly Monitor to detect SKU-level quality spikes in real time

The rules-based and AI-augmented system that enforces return eligibility before an RMA bot issues authorization. It blocks fraudulent or out-of-policy requests at the point of intake.

• Validates return windows and receipt requirements
• Cross-references customer history for abuse patterns
• Applies category-specific restrictions (e.g., final sale items)
• Feeds the RMA bot with real-time eligibility decisions

The AI process of mapping unstructured customer narratives to a standardized taxonomy of root-cause codes. When a customer types 'didn't fit right,' the system normalizes this to a structured code like SIZING_INACCURATE.

• Uses natural language processing to interpret free-text reasons
• Enables trend analysis across millions of returns
• Feeds product quality teams with actionable defect data
• Eliminates manual categorization labor

An automated risk-assessment engine that approves or denies a monetary refund immediately upon carrier scan of the return label. This is a critical post-RMA workflow that determines customer experience.

• Evaluates customer trust score and return history
• Considers item value and fraud risk thresholds
• Triggers refund before the warehouse even receives the item
• Reduces customer service inquiry volume significantly

A machine learning model that identifies the fraudulent practice of purchasing items for short-term use before returning. The RMA bot integrates with this system to flag suspicious requests.

• Analyzes return timing relative to purchase date
• Detects patterns across multiple accounts and addresses
• Identifies serial wardrobers through behavioral clustering
• Automatically escalates high-confidence fraud cases for review

An AI-powered gate that requires the customer to upload a real-time photo of the item before the RMA bot authorizes the return. Computer vision verifies condition and authenticity.

• Confirms the item matches the original SKU
• Detects obvious damage or missing components
• Prevents empty-box fraud at the intake stage
• Stores images for downstream computer vision grading

An automated workflow that escalates a return case to a human agent when natural language processing detects high negative emotion in customer communications during the RMA process.

• Analyzes tone, urgency, and sentiment in real-time
• Routes distressed customers to senior support staff
• Prevents churn by prioritizing high-emotion cases
• Integrates directly with the RMA bot's conversation flow