Transforming Hospitality Chatbots: How Guesty accelerated their RAG-based LLM deployment

Learn how Qwak's MLOps platform assisted Guesty in accelerating their RAG-based LLM deployment of hospitality chatbots from months to days.
Pavel Klushin
Pavel Klushin
Head of Solution Architecture at Qwak
January 10, 2024
Table of contents
Transforming Hospitality Chatbots: How Guesty accelerated their RAG-based LLM deployment

Introduction

Founded in 2013 and headquartered in Tel Aviv, Israel, Guesty has established itself as a pivotal player in the hospitality industry with its cloud-based property management software. Designed to cater to the intricate needs of short-term vacation rentals, Guesty's platform is a confluence of robust features such as unified listings management, accounting, automation, and advanced analytics. With a mission to streamline hospitality operations, Guesty has integrated a state-of-the-art RAG chatbot within its communication ecosystem to empower hosts with prompt and contextually relevant guest interactions.

RAG-Based Chatbot - A Shift in Guest Communication

The integration of the RAG-based chatbot within the Guesty inbox product has changed the way hosts engage with guests. This new model is adept at recommending precise responses to incoming messages, thus significantly reducing the hosts' time spent on correspondence. The overarching objectives of this new initiative are twofold: to expedite response times—critical to maintaining service level agreements (SLAs) across various platforms—and to allow hosts to allocate more time to complex operational challenges.

Challenges and Innovations

The journey of integrating a RAG-based chatbot using LLMs presented Guesty with a set of unique challenges:

  • Updating Model Tech Stack: The team got the challenge of deploying a RAG model for the first time, navigating the complexities associated with leveraging LLMs in different areas of their business.
  • Lack of a Vector Database: The absence of an existing vector database meant that Guesty needed a reliable solution that could handle the complexities of vector storage and retrieval with low latency requirements.
  • Scalability of the Solution: A notable challenge was ensuring the scalability of the chatbot solution. Guesty required a system that could easily transition from a 'low scale' proof of concept to a 'high scale' deployment, catering to an increasing number of customers. 

To address these challenges, Guesty collaborated with the Qwak team, to enable the vector database technology which offers the required infrastructure and expertise. Qwak's solutions were specifically designed to be user-friendly, allowing Guesty's data science team to lead the implementation with minimal assistance from engineering.

The Implementation Journey - Technical Deep Dive

In order to implement the new model, the team created the following process:

  • Resource Data Synthesis: The chatbot sources information from previous guest conversations, property characteristics, and user-saved replies to construct a robust knowledge base saved in Google BigQuery.
  • Data Pre-Processing: Prior to vector embedding, resource data is pre-processed for optimal compatibility with the AI model.
  • Embedding Generation via OpenAI: OpenAI's algorithms are employed to transform pre-processed data into dense vector embeddings, capturing the nuanced semantics of the text.
  • Vector Database: Qwak's Vector DB is leveraged to store and manage these embeddings, optimizing for rapid similarity searches and retrieval.
  • Continual Data Enrichment: The database undergoes daily updates of 50,000 rows to incorporate new interactions, constantly evolving the chatbot's response accuracy.

User Query Resolution Framework

  • Vectorized Query Processing: User queries are vectorized following the same protocol as the resource data, ensuring consistency in response quality.
  • Retrieval Mechanism: The Vector DB conducts a similarity search to retrieve the most relevant vectors corresponding to the user query.
  • AI-Powered Response Formulation: Utilizing ChatGPT 3.5's contextual prowess, the chatbot proposes suggested answers to the hosts.
  • Tailored Response Generation: A specialized prompt function is employed to craft the final guest response, drawing on the suggested answers and ensuring relevance and personalization.

Results and Benefits

The strategic alliance between Guesty and Qwak delivered significant results in a short time frame:

  • Quick Rollout: The chatbot was up and running in just three weeks, a testament to Qwak's straightforward tools and the know-how of the data science team.
  • Data Science-Led Implementation: The deployment was predominantly executed by data scientists, with minimal need for engineering support enhancing the user-friendliness and autonomy enabled by Qwak's solutions.
  • Enhanced Operational Efficiency: Qwak's managed vector database and serving capabilities facilitated immediate improvements in response time, directly impacting operational efficiency.
  • Cost Savings and Guest Satisfaction: The automated system led to cost reductions and elevated guest service quality, as evidenced by the fast and accurate responses.
  • SLA Performance: Hosts experienced a notable uptick in their ability to fulfill SLA commitments, thanks to the chatbot's efficiency.
  • Scalability: the transition from POC to Live production was achieved with just one click, showcasing the flexibility and scalability of the Qwak platform. This feature was crucial, as it meant that the solution could be quickly adapted to meet growing demand without the need for extensive reconfiguration or downtime.

Based on this architecture, Guesty plans to ship 2 more models to help improve customer support and internal engineering efficiency.

The chatbot deployment has led to an increase in user engagement, with usage rates rising from 5.46% to 15.78%. This growth indicates a positive reception and increased utilization of the chatbot among users. Alongside this, there's been an improvement in user satisfaction. Users have generally reported satisfaction with the chatbot's responses, finding them accurate and useful for their interactions. This feedback suggests that the chatbot is successfully meeting the needs of its users, aiding in more effective guest communication.

Summary

Guesty's deployment of a RAG-based chatbot, enabled by Qwak's ML platform, represents a significant advancement in guest communications for the hospitality industry. Achieved remarkably within just three weeks, this project highlights the harmonious collaboration between Guesty's innovative data science strategy and the accessible, minimal-engineering solutions offered by Qwak. The outcome is an effective, AI-powered chatbot that not only boosts guest satisfaction but also enhances operational productivity and SLA adherence.

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