Back PageA Hospitality Company: Transforming Customer Support with an AI-Powered Knowledge Assistant
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Accelerating Information Access Through Conversational AI
As the company continued to improve its operations, customer service teams faced increasing pressure to respond quickly and accurately to user inquiries. To address this challenge, They partnered with Insignia to build a GenAI-powered chatbot that delivers fast and contextual responses by connecting enterprise knowledge into a single conversational interface.
Sector and Service
Hospitality
Generative AI
Challenge
Reducing Response Time While Preserving Answer Accuracy
As the volume of guidelines and operational data increased, retrieving accurate information became increasingly complex. Manual search across documents and databases slowed response times and introduced inconsistency in customer support interactions.
They needed a solution that could streamline access to knowledge, minimize dependency on manual lookup, and ensure that answers remained aligned with up-to-date data sources.
They needed a solution that could streamline access to knowledge, minimize dependency on manual lookup, and ensure that answers remained aligned with up-to-date data sources.
Strategy
Centralizing Enterprise Knowledge Through a GenAI Chatbot
Insignia designed an AI-driven knowledge assistant that consolidates unstructured documents and structured data into a single intelligent system.
Guideline PDFs and PostgreSQL data were ingested into a GenAI application hosted on AWS EC2. Semantic understanding was handled through vector embeddings stored in Qdrant. This allows the system to retrieve relevant information based on meaning rather than keywords alone. Redis was introduced to optimize caching and improve response speed, then AWS Bedrock was used to generate accurate and context-aware answers from foundation models.
Users interact with the platform through a conversational chatbot interface, in which queries the GenAI layer to deliver clear and relevant responses in real time.
Guideline PDFs and PostgreSQL data were ingested into a GenAI application hosted on AWS EC2. Semantic understanding was handled through vector embeddings stored in Qdrant. This allows the system to retrieve relevant information based on meaning rather than keywords alone. Redis was introduced to optimize caching and improve response speed, then AWS Bedrock was used to generate accurate and context-aware answers from foundation models.
Users interact with the platform through a conversational chatbot interface, in which queries the GenAI layer to deliver clear and relevant responses in real time.
Outcome
Faster, More Consistent Customer Support Interactions
With the AI-powered chatbot in place, the company significantly improved how information is accessed and delivered within customer service workflows. Users can now obtain precise answers quickly, and support teams benefit from consistent responses grounded in approved guidelines and centralized data. The solution reduced manual search effort, improved response efficiency, and established a scalable foundation.
A hospitality company strengthened its customer service operations with faster access to information and more consistent answers.
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