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Automating Public Safety Customer Support with an AI-Driven Multilingual FAQ Chatbot

Automating Public Safety Customer Support with an AI-Driven Multilingual FAQ Chatbot
Home   /   Case Studies   /   Automating Public Safety Customer Support with an AI-Driven Multilingual FAQ Chatbot
Automating Public Safety Customer Support with an AI-Driven Multilingual FAQ Chatbot - Clarion Case Study

The customer is a public safety organization based in Riyadh, Saudi Arabia. Its website provides service-related information and FAQs that users need to access quickly. The organization wanted to simplify access to this information and improve the overall digital customer experience.

The Challenges:

An AI-driven multilingual FAQ chatbot was developed using Azure AI services, Llama 2, and Microsoft Copilot Studio. It automatically retrieves and presents service and FAQ information in both Arabic and English, streamlining the support process.

  • Slow query resolution caused by complex site navigation across dense web pages.

  • User frustration and a high number of complaints about the slow response.

  • Heavy reliance on human support staff to handle repetitive, low-level inquiries.

  • Need for precise and timely information in both Arabic and English languages.

The Solution:

  • Built an AI-driven FAQ chatbot for automated information retrieval.

  • Supported customer interactions in Arabic and English.

  • Used Azure Custom Question Answering and Azure Conversational Language Understanding.

  • Implemented Llama 2, Microsoft Copilot Studio, Semantic Kernel, C#, and Python.

  • Enabled responses using existing website service and FAQ content.

The Business Outcomes:

  • Delivered instant information retrieval, eliminating manual website navigation for users.

  • Improved user experience by serving accurate, bilingual answers in Arabic and English at the point of need.

  • Increased self-service engagement, leading to higher overall citizen satisfaction scores.

  • Lowered agent workloads by automating answers to basic queries, saving staff time for higher-priority tasks.

Frequenty Asked Questions

How does Microsoft Copilot Studio simplify automated support delivery in the public safety sector?

Microsoft Copilot Studio provides an enterprise environment to build, deploy, and update conversational AI assistants using low-code tools. Organizations in the public safety sector adopt Copilot Studio because it:

  • Accelerates chatbot deployment across web channels without requiring custom frontend builds.

  • Orchestrates AI model logic via Semantic Kernel to maintain accurate response pipelines.

  • Scales support capacity automatically during high-traffic periods without extra agent overhead.

  • Simplifies knowledge base updates as site policies and public safety guidelines change.

What role do Azure Conversational AI services and Llama 2 play in building multilingual chatbots for the public safety sector?

By utilizing the power of Azure Custom Question Answering, Azure Conversational Language Understanding, and Llama 2, the chatbot is capable of managing context efficiently in English and Arabic. The public safety departments appreciate this particular technology because:

 

 

  • Azure Conversational Language Understanding grasps user intent across varied phrasing and dialects.

  • Azure Custom Question Answering indexes source site content to surface precise, verified answers.

  • Llama 2 provides natural language generation to craft conversational responses.

  • Python and C# integration with Semantic Kernel ensures stable data processing across backend systems.

How does Microsoft Copilot Studio simplify automated support delivery in the public safety sector?

Microsoft Copilot Studio provides an enterprise environment to build, deploy, and update conversational AI assistants using low-code tools. Organizations in the public safety sector adopt Copilot Studio because it:

  • Accelerates chatbot deployment across web channels without requiring custom frontend builds.

  • Orchestrates AI model logic via Semantic Kernel to maintain accurate response pipelines.

  • Scales support capacity automatically during high-traffic periods without extra agent overhead.

  • Simplifies knowledge base updates as site policies and public safety guidelines change.

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