Accurately predicting the intent of customer support requests is vital for efficient support systems, enabling agents to quickly understand messages and prioritize responses accordingly. While different approaches exist for intent detection, maintaining separate client-specific or industry-specific models can be costly and impractical as the client base expands. This work proposes a system to scale intent predictions to various clients effectively, by combining a single generic model with a per-client list of relevant intents. Our approach minimizes training and maintenance costs while providing a personalized experience for clients, allowing for seamless adaptation to changes in their relevant intents. Furthermore, we propose a strategy for using the clients relevant intents as model features that proves to be resilient to changes in the relevant intents of clients -- a common occurrence in production environments. The final system exhibits significantly superior performance compared to industry-specific models, showcasing its flexibility and ability to cater to diverse client needs.

In my last post about Bot Framework, I wrote an overview about how you can create a weather bot.. in this post, I'm starting a series where I'll go deep into each part of building a bot using the Bot Framework.. In this post, I'll deep-dive into Language Understanding Intelligence Service (LUIS), and how we can use it to understand the user message. LUIS is an API built by Microsoft and introduced as a part of Microsoft Cognitive Services (A set of intelligence services).. it's a Machine Learning API that allows you to create an app with some intents and entities and then train it on some utterances and then start using it in your apps or websites and most importantly… your bots! In this post, I'll guide you to create a LUIS app focusing on the bots scenarios, let's start! Well, this the most important part of building an intelligent service than can help you understanding the language, what's your intents?