AI Assistant for TravelTech: 25% Cost Reduction & 60% Faster Support

Team Composition:

  • Business Analyst
  • Project Manager
  • Data Scientist
  • Senior Python Engineer
  • Middle Python Engineer
  • DevOps
TravelTech

We built a travel assistant for a major tour operator to streamline customer interactions and enhance internal workflows. The AI chatbot delivers human-like responses, reducing inquiry handling time by up to 60%, boosting customer engagement by 35%, and automating lead management in the CRM.

  • Python
  • Django
  • Celery
  • OpenAI API
  • AWS ECS
  • Terraform
  • Github Action

Background

The client is one of the region’s largest and most established tour operators, founded in the early 1970s. The company is active across both inbound and outbound tourism, offering a wide range of travel and hospitality services.

It specializes in cultural and pilgrimage tours, educational travel programs, and custom vacation packages. The company also operates a series of signature tours with guaranteed group departures.

On the outbound side, the team focuses on customized European travel experiences, including scenic tours and river cruise holidays.

CHALLENGE

The client approached us with the need to modernize their travel management ecosystem to enhance customer experience and improve efficiency for internal teams.

The main challenges included:

  • Fragmented customer journey: Trip planning and booking were cluttered, requiring customers to navigate multiple websites and agents, while post-purchase access to bookings, payments, and support was limited.

  • Operational inefficiencies: Back-office workflows suffered from slow response times and manual processes, leading to missed opportunities.

  • CRM and interaction management gaps: Customer interactions were difficult to manage and optimize across channels, limiting personalization and follow-up.

  • Need for scalable intelligence: The existing setup could not improve efficiency or service quality without increasing internal workload.

Solution: Agentic AI in Travel

To address these challenges, Beetroot engaged a multi-disciplinary team of AI experts, backend developers, and QA engineers to develop an efficient virtual travel assistant — a unified platform that serves customers, support specialists, and travel managers alike. Core technical components of the travel AI agent include:

  • Natural language processing and AI integration:

    Powered by the OpenAI API, the AI virtual assistant leverages advanced NLP capabilities to understand user input with high accuracy. It enables natural, conversational interactions across multiple contexts — from helping customers explore tours (sales) and access booking information (support) to assisting managers with quick answers during phone sales (copilot).

  • Omnichannel and messaging integration:

    Built on Python (Django/DRF), the travel AI agent integrates directly with platforms like WhatsApp, allowing customers to chat, ask questions, and receive travel updates without leaving their preferred messaging environment. The system also supports future integration with other messengers, ensuring a consistent, user-friendly experience across channels.

  • Backend architecture and management:

    The back-office panel, developed with Django, provides an intuitive interface for administrators to manage chatbot configurations, content, and performance and fine-tune responses. It also connects to the CRM, automatically adding leads and summarizing customer interactions to provide actionable insights into what users most frequently ask and how offerings can be improved.

  • Efficient background processing:

    Celery is implemented to manage asynchronous tasks, enabling the assistant to handle a high volume of concurrent user interactions. This architecture also facilitates seamless communication with the OpenAI API, ensuring real-time responses even during peak usage.

  • Scalable and secure cloud infrastructure:

    Hosted on AWS ECS with managed EC2 instances, the solution uses PostgreSQL for database management, while Redis handles caching and performance optimization for fast data access and retrieval. Infrastructure as Code (IaC) is managed with Terraform, ensuring secure, consistent deployments that are easily replicated across different environments.

  • Continuous integration and delivery (CI/CD):

    A GitHub Actions-powered CI/CD pipeline enables seamless updates and continuous deployment of new features. This infrastructure allows for rapid iteration and deployment, ensuring that the AI travel assistant stays current with the latest AI advancements and user requirements.

From the first call Beetroot spoke our language both technically and collaboration wise. It’s not just another chatbot, the AI assistant handles thousands of requests every day, talks to our CRM, and it’s easy to maintain and scale on AWS. It reduced the pressure on our team a lot. We’re planning the next automation project and the vendor choice is out of question now.

Project Manager,
Leading Tour Operator

Results

The AI-powered virtual assistant we built for the client has resulted in significant improvements across all business areas.

  • Built on AWS, the solution is designed to scale smoothly as the client’s business grows, maintaining stable performance even during peak user activity periods. While the integration of LangChain is planned for future updates, the current system has already delivered measurable results and clear operational benefits:

  • Up to 60% faster response times

    for customer inquiries through AI chat and WhatsApp integration.

  • Up to 35% increase in customer engagement

    due to personalized recommendations and multi-channel accessibility.

  • 30% reduction in manual workload

    for customer care and sales teams through automated responses and lead creation in the CRM.

  • 5× increase in concurrent sessions

    handled without performance degradation.

  • Continuous improvement loop

    with insights from conversation data helping optimize travel offerings and marketing campaigns.

  • In addition to performance gains, the assistant has led to an estimated 20–25% reduction in operational costs and a 3–5% increase in sales conversions.

  • Thanks to its modular architecture and flexible AI core, the assistant can be easily adapted for use in other industries that rely on customer communication and data-driven personalization.

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