Meta Model API

CASE STUDY

Enhancing guest experiences with an AI customer service chatbot

At a glance

Industry

Technology

Use case

Automating micro-hotel customer service

Goal

Improve the guest experience and streamline operations

Llama version

Llama 3.1 405B

Deployment

Oracle Cloud Infrastructure (OCI)

Download full case study

THEIR STORY

Empowering businesses to put AI to work

Brain4Data specializes in helping businesses use advanced AI technology and retrieval augmented generation (RAG) to prepare data for generative AI applications. Their digital AI assistant, FRED, transforms distributed company information into high-value, AI-ready knowledge bases and generates precise, context-specific answers. Currently, FRED generates 40,000 responses a month for clients in 30 countries.Roatel Holding AG is the leading provider of smart micro-hotels in Germany. Self-service Roatel facilities provide travelers with convenient, affordable lodging with all the modern conveniences. Based in Germany, Roatel plans to establish a network of hotels across Europe.

THEIR GOAL

Automating guest service to unlock business growth

Brain4Data’s client, Roatel, operated self-service micro-hotels in Germany. Hotel guests relied on a remote customer service team to answer their questions, but as Roatel began to expand, the service team struggled to keep up with increased demand. Brain4Data set out to build a scalable AI solution that could automate customer service in multiple languages and provide accurate, fast responses 24/7. The goal was to maintain guest satisfaction and reduce reliance on live customer service agents.

THEIR SOLUTION

A Llama-powered customer-service chatbot

Brain4Data used its digital assistant platform FRED, Llama and OCI Generative AI to develop a chatbot for Roatel. The chatbot provides fast, reliable and up-to-date answers to complicated questions — no matter the time of day.Llama 3.1 405B is at the heart of the solution. Brain4Data chose the Llama model for its multilingual capabilities and strong performance in nuanced, semantic understanding — all delivered without fine-tuning. The team also knew the chatbot would benefit from an open-source model with broad community support.

THEIR APPROACH

Streamlining development with OCI tools

The chatbot is built on OCI using OCI Generative AI and a RAG workflow. When a guest asks a question, the system gathers relevant context from Roatel’s document repository and injects it into a custom-engineered system prompt that is passed to the Llama model, which generates a response in the guest’s preferred language. Behind the scenes, Oracle’s Database 23ai Vector Search identifies the most relevant document chunks from large volumes of unstructured data, and Cohere’s embedding model supports semantic understanding.

THEIR SUCCESS

Streamlined operations and elevated guest experiences

With OCI, Llama and their FRED assistant platform, Brain4Data developed a fully operational chatbot in just two months. Today, the chatbot handles the majority of customer queries for Roatel. The result is reduced workloads for service staff, operational efficiencies for the business and enhanced experiences for customers. With its multilingual AI chatbot, Roatel is now set to scale its micro-hotel network across Europe.

80% of customer service requests are handled by Llama-powered AI
100% multilingual — the agent converses in the customers’ language

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