Our Work

Develop an AI-powered airline customer support system that automates responses to passenger queries using real-time flight data and a retrieval-based knowledge base. Integrate LLMs, PostgreSQL, and n8n workflows with safety guardrails to ensure accurate, grounded, and compliant customer interactions.

Problem Statement:
Airlines receive thousands of customer queries daily—ranging from flight status checks and baggage inquiries to refund requests and booking changes. Traditional support systems depend heavily on manual responses, leading to longer resolution times and inconsistent customer experiences. Generative AI and intelligent workflow automation can significantly improve this process. By combining Large Language Models (LLMs) with structured data (PostgreSQL) and retrieval-based knowledge (RAG), the airline can automate and personalize customer support with safety guardrails and accuracy.
Design and implement an AI-powered customer support system that:
● Responds to user queries about flight information, delays, cancellations, and baggage policies.
● Combines real-time flight data (from PostgreSQL) and policy information (from a Knowledge Base) using RAG (Retrieval-Augmented Generation).
● Filters and moderates user input/output using Guardrails for safety and compliance.
● Integrates the workflow using n8n to orchestrate multiple agents and tools.