Gander is an AI-powered customer self-service platform for airlines. It automates disruption customer service, from initial contact to compensation claims. Gander uses AI-enabled workflows to serve customers faster and more accurately than chat or voice. It helps airlines resolve customer claims instantly, reduce costs, and stay compliant with passenger rights laws. Gander offers features such as disruption reimbursement, passenger rights compensation, baggage delay and damage handling, ancillary refunds, special case refunds, and feedback management. It integrates with popular PSS systems and is customizable to fit specific airline needs. Gander also offers outbound voice capabilities for disruptions, using GenAI to call impacted customers and offer rebooking and other assistance.
Features & Capabilities
—Automate disruption customer service workflows from initial contact to claim resolution.
—Authenticate customers using PII-compliant data and integrate with airline systems.
—Qualify passenger eligibility for compensation or reimbursement using AI-driven analysis.
—Collect user input via various methods and use AI to parse information and trigger appropriate responses.
—Provide customers with clear status updates and explanations aligned with airline policies.
—Resolve claims instantly or manage disputes.
—Direct customers to insurance coverage when applicable.
—Utilize AI to analyze operational data and determine reimbursement eligibility.
—Transform feedback forms into dynamic workflows that understand customer intent and resolve cases automatically.
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✓Resolve 50%+ of technical issues without human involvement
Intelligent Escalation
Identify when human touch is needed and route appropriately
Example: Detect frustration, refund requests, or technical complexity → escalate to tier 2
✓Humans handle only complex cases, improving job satisfaction and resolution quality
Architecture
Customer support agents combine LLMs with knowledge bases, ticketing systems, and escalation logic to handle customer inquiries autonomously while knowing when to hand off to humans.
LLM Core
Large language model for understanding and generating responsesParse customer intent, generate contextual responses, maintain conversation flow
Knowledge Base Integration
Vector database with company docs, FAQs, product infoRetrieve accurate information to answer customer questions
CRM/Ticketing Integration
Connection to Zendesk, Intercom, or custom ticketing systemLog conversations, escalate to human agents, track resolution
Escalation Logic
Rules engine for when to transfer to human supportHandle complex cases, angry customers, or sensitive issues appropriately
Implementation Guide
Prerequisites
Structured knowledge base (docs, FAQs in searchable format)
API access to CRM/ticketing system
Defined escalation criteria and human-in-loop workflows
Test environment separate from production support
Steps
1Audit most common support tickets (top 20 questions)
2Build knowledge base with answers to common questions
3Set up LLM with RAG over knowledge base
4Integrate with ticketing system API for logging and escalation
