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Voyager: Reimagining Travel with LLMs

Abstract

Travel planning and booking can often feel overwhelming, especially in India, where multi-modal transportation (trains, roadways, airways) and customized itineraries are common. With the growing potential of the aviation sector, simplifying air travel is also crucial.

We are building a voice-based, multi-agent framework designed to enhance the user experience by providing data-driven decision-making, advanced search capabilities, and extensive customization. This framework serves as a personal travel assistant, offering personalized guidance and optimizing every step of the journey—from planning and booking to airport procedures and post-arrival logistics.

Problem Statement

Modern travel planning in India faces three critical challenges:

  1. Complexity of Coordinating Multi-Modal Transportation
    Travelers often need to combine different modes of transport (trains, buses, flights) across various platforms to create budget-friendly plans. This becomes mandatory when there is no direct connectivity.

  2. Overwhelming Decision-Making
    With an abundance of options and a lack of real-time data support, making informed decisions becomes mentally exhausting.

  3. Airport Navigation Stress
    Navigating airport procedures—from check-in to baggage claim—adds further stress, especially with fragmented verification systems. The lack of integrated, data-driven support means travelers spend excessive time researching, comparing, and coordinating.

Proposed Solution

To address these issues, we are developing a system that seamlessly integrates with existing booking platforms while enhancing their functionality through personalization and real-time continuous support. Our AI-driven approach tackles the challenges in three key areas:

  • Planning

    • Create optimized itineraries based on user preferences such as budget, urgency, and other constraints.
    • ML-driven framework to process complex travel requirements and dynamically analyze transportation options from source to destination.

  • Hyper-Personalization for Decision Support

    • A voice-based agent, with the knowledge of an experienced traveler, provides contextual, real-time recommendations.
    • Actively assists users in decision-making by analyzing real-time data and preferences.

  • Automated Journey Management

    • The agent is aware of journeys scheduled on specific dates.
    • Simplifies airport procedures through turn-by-turn navigation and automated handling of verification and documentation.
    • We plan to use digital public infrastructure like DigiYatra and DigiLocker for this purpose.

Key Features

  • Plan Mode
    A conversational voice agent fine-tuned for travel queries, assisted by a search agent to build plans and observe real-time data (weather patterns, price trends, historical data).

  • Book Mode
    Skip the endless tab-switching. Simply tell us your destination and timeline, and the system generates customizable travel plans optimized for different priorities. By default, we suggest three variants: Best, Quickest, and Most Affordable. These can be later modified by the user on demand.

  • Navigate Mode
    For automated in-airport navigation. With DigiYatra integration, start your journey with a simple facial scan. Our voice-assisted companion guides you through every checkpoint—from entry to baggage claim—with real-time mapping and personalized instructions.

Target Audience

  • Regular Internet-Savvy Travelers
    Our primary audience consists of digitally proficient adults who travel multiple times per year for business or leisure. They're frustrated with the time-consuming nature of comparing multiple options and coordinating complex itineraries. They are already comfortable with online booking platforms but seek efficiency and value optimization.

  • Travel-Anxious Individuals
    Those who travel occasionally and face significant anxiety about the travel process. These travelers seek more than just a booking platform; they need a guiding hand that can provide reassurance and step-by-step assistance.

  • Senior and Tech-Adapting Travelers
    Older adults who are willing to use technology but require additional support and simplified interfaces. They value clear communication and prefer human-like interactions over complex digital interfaces. This group benefits the most from our voice-based AI assistant.

Developing this system for a potential customer base of 100K+ will require large storage and significant investment. Currently, our focus is to nurture this as an efficient PoC (Proof of Concept). Most of our work will be based on lightweight open-source models to mitigate these constraints.