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Case Study

JD Wetherspoon – AI Chatbot

JD Wetherspoon is one of the UK’s largest pub and hotel chains, operating more than 800 venues and employing thousands of staff across the UK and Ireland. With such a large workforce, efficient access to internal resources like HR documents, pub menus and standard operating procedures is critical.

 

As the long-term digital partner of JD Wetherspoon, Filter suggested developing a smarter way for employees to find and access essential information. The result was a custom AI ChatBot powered by advanced AWS technologies, designed to improve efficiency and upgrade the overall employee experience.

AI AWS Internal Systems Bespoke Development Hospitality
A cartoon robot icon with a smiling expression on a bright cyan square against a blue background. The robot features a white head with blue eyes and a "W" marking on top, representing a friendly digital assistant or chatbot.

Introduction

Internal ChatBot for Document Retrieval

JD Wetherspoon needed a more intuitive system for staff to access critical internal resources. The existing solution, myLibrary, relied on basic search functions that made it difficult for employees to find what they needed. With thousands of documents to navigate, this often led to wasted time and frustration.

 

Filter developed a tailored AI ChatBot that understands natural language queries and retrieves precise information. Built on AWS infrastructure and seamlessly connected to myLibrary, the solution has transformed how staff engage with internal systems.

Relationship Length

Long-term digital partner

Focus

The aim of this project was to strengthen talent acquisition for leading UK hospitality brand JD Wetherspoon by creating a more efficient, user-friendly solution for employees engaging with internal systems

Problem Problem

  • Over 1,500 internal documents meant finding information could be time-consuming
  • Search tools often returned results that didn’t match employees’ intent
  • Staff spent significant effort navigating the document library to locate the right information

Solution Solution

  • AI ChatBot built with AWS Bedrock and a vector database
  • Seamlessly integrated into the existing myLibrary platform
  • Access Control Lists (ACLs) restrict results to each user’s role and region
  • Multilingual model powered by Amazon Translate

Result Result

  • Employees quickly access the right information
  • Provides faster, more accurate responses to natural language queries
  • A scalable foundation that improves efficiency across the business

A collage of various documents and forms, each labeled with different operation codes and topics, such as pub security, allergen controls, and processing vouchers, is displayed. The text is primarily black on a white background, arranged in a scattered, overlapping layout.

The Challenge

The JD Wetherspoon internal library included over 1,500 documents covering everything from employment policies to operational standards. The legacy search tools were slow, lacked contextual understanding and could not tailor results effectively.

This created inefficiencies for staff and managers alike. The organisation needed a new approach that would not only deliver faster, smarter results but also respect specific permissions and regional compliance needs.

Our Approach

Filter designed and built a custom ChatBot platform using AWS Bedrock to leverage large language models for natural language understanding. A high-performance vector database was implemented to index and retrieve documents accurately and efficiently.

Access Control Lists (ACLs) ensure staff see only the information relevant to their position and country, improving both usability and data security. The ChatBot also uses a multilingual model powered by Amazon Translate, with documents tagged using ISO language codes as metadata so responses can be served in the user’s preferred language. The system was carefully integrated into the existing JD Wetherspoon myLibrary platform to provide a familiar user experience.

Throughout the project, rigorous testing and iterative refinements were carried out to ensure the ChatBot delivered context-aware, reliable answers. This included developing logic to serve tailored responses based on user roles and locations.

A laptop displaying a business website dashboard with organized navigation menu and content sections. The interface shows multiple columns of links and a blue-highlighted interaction panel on the right side against a light blue background.
A smartphone and laptop displaying the WordPress admin dashboard interface with content management features, showing how the platform offers consistent user experience across different devices.

Results

The new AI-driven ChatBot enables JD Wetherspoon employees to locate detailed, relevant information in seconds.

Managers and staff can access documents specific to their region without combing through lengthy search results. Queries to ChatBot can be kept quite concise but the more details are added, the more tailored the response will be to the original request. Information in the response can be collated from several sources to give the best-suited answer; moreover, the context of the previous conversation will also be considered.

This has reduced frustration, saved valuable time and improved confidence in using internal tools. It also set a new benchmark for how AI and machine learning can support day-to-day operations within large hospitality businesses.

Why This Story Matters

This project highlights how smart use of AI and AWS technologies can solve practical business problems.

By building a scalable, role-aware ChatBot, Filter helped JD Wetherspoon transform internal workflows and empower employees with quick, reliable access to the information they need.

For organisations struggling with information overload or inefficient systems, this case study shows how tailored digital solutions can drive meaningful operational improvements and support a better employee experience.

Three smartphone screens displaying a messaging or chat application interface with blue headers. The screens show different conversation views with text messages and interaction options, arranged side by side against a light blue background.

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