What challenges did Rackspace face before AI implementation?

Enterprises struggle to provide accurate, real-time answers from vast and distributed internal documentation. Hallucinations and lack of context-awareness in traditional language models decrease trust in AI chatbots. Manual curation and responding to complex customer queries are time-consuming and error-prone.

What was the impact of the AI solution?

Deployed scalable, customizable chatbots for enterprise clients. Reduced error rates and hallucinations in chatbot responses. Boosted employee and customer satisfaction with faster, more accurate AI-powered support. Automated data integration and deployment processes using infrastructure-as-code tools.

Source

Microsoft

Rackspace delivers custom enterprise chatbot powered by generative AI

Use case typeConversational supportUpdated Jun 13, 2026

Rackspace implemented a personalized AI chatbot utilizing Azure OpenAI and Azure AI Search. Employing Retrieval-Augmented Generation (RAG), the solution enables users to query a knowledge base and receive natural language responses created by the GPT-4 model. The architecture integrates Azure Cognitive Search for retrieving relevant enterprise data with Azure OpenAI for generating responses. Deployment is managed via Azure App Service, ensuring scalable and secure access for enterprise clients. Terraform scripts automate resource deployment, including Azure Cognitive Search, Azure OpenAI, and storage. The knowledge base is hosted in Azure Blob Storage, indexed into Azure Cognitive Search for fast retrieval. The chatbot can be customized for different data sources and industries, and responses are tailored to enterprise needs. After deployment, chat records can be stored in Cosmos DB (if chat history is enabled). The approach reduces model hallucination, enhances response accuracy, and allows for rich, context-relevant information retrieval.

Organization: Rackspace
Industry: Tech & Comms
Location: Global
Published: December 2024

Reported outcomes

Strategic outcomes

New product / capabilityDeployed scalable enterprise chatbotsRisk & complianceReduced chatbot hallucinationsCustomer experience & trustImproved support accuracy and satisfactionSpeed & agilityAutomated deployment and data integration

Primary read

Use case focus

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1Retrieval-Augmented AI Chatbot for Enterprise Knowledge Base

Enterprises struggle to provide accurate, real-time answers from vast and distributed internal documentation.
Hallucinations and lack of context-awareness in traditional language models decrease trust in AI chatbots.
Manual curation and responding to complex customer queries are time-consuming and error-prone.

Integrated Azure Cognitive Search with Azure OpenAI (GPT-4) to enable retrieval-augmented generation for chat.
Automated resource deployment using Terraform.
Indexed internal data sources (via Azure Blob Storage) in Azure AI Search.
Custom chatbot deployed via Azure App Service and GPT-4 model.
Optional use of Cosmos DB for persistent chat history.

Technologies

Azure OpenAI Azure AI Search Azure Cognitive Search Azure App Service Cosmos DB

Deployed scalable, customizable chatbots for enterprise clients.
Reduced error rates and hallucinations in chatbot responses.
Boosted employee and customer satisfaction with faster, more accurate AI-powered support.
Automated data integration and deployment processes using infrastructure-as-code tools.

Architecture

Architecture: Knowledge base data is stored in Azure Blob Storage and indexed in Azure AI Search. GPT-4 (Azure OpenAI) interacts with users, queries AI Search for relevant content, and delivers results through a chatbot interface on Azure App Service. Terraform automates resources. Optionally, Cosmos DB stores chat histories.

Sources & evidence1

Groundedness: Unavailable

Primary source

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