Intent Detection – the building block of Conversational AI
10 Jun 2025
3 min read
It goes without saying that, to be effective, it’s essential that customer service agents are able to understand what customers want. For people, that is a skill that is inherent, but for AI Agents, it’s a skill that requires training or programming.
Understanding what customers want is essential for effective customer service. While people naturally possess this skill, AI Agents require training or programming to achieve it. In this article, we introduce the concept of intent detection and explain how it works within the broader field of natural language processing (NLP), which enables AI systems to interpret and respond to human language.
Intent detection is a core task in natural language processing (NLP) and is crucial for recognizing what the user wants from their input. Technologies like DigitalGenius rely on intent detection to accurately identify user intentions and improve customer interactions. If a chatbot or AI Agent cannot recognize what users really want, it cannot be effective.
To explore how AI has developed to be able to ‘understand’ language over time, take a look at our blog here.
Here we will explore at a high level how our intent detection works, how it enables the DigitalGenius platform to recognize user intent, and what this means for AI-driven customer service.
What Is Intent Detection in Natural Language Understanding?
At its core, intent detection is about deciphering what a customer truly wants when they send a message by analyzing the input text or query provided by the user. It’s the process of identifying the purpose or goal behind a customer’s communication.
For instance, if a customer writes, “I haven’t received my package yet,” the intent is likely to inquire about the order’s delivery status. The system processes the input text to classify it into one of several user intents, and this classification helps determine the user's intent for an accurate response.
Why Is Intent Detection Important?
Implementing intent detection offers several benefits:
Efficiency: Automated understanding of customer queries reduces the need for manual sorting, speeding up response times. Intent detection enables the system to respond quickly and accurately to customer queries.
Accuracy: By accurately identifying intents, businesses can provide precise answers, enhancing customer satisfaction.
Scalability: As customer interactions increase, intent detection allows businesses to manage higher volumes without compromising service quality.
Data Insights: Analyzing intent data helps businesses identify common issues, informing improvements in products or services.
Monitoring the performance of intent detection is crucial for ensuring helpful and accurate responses to the customer's needs.
Arguably the most important one of these is accuracy. We’ve all had experiences with chatbots where you ask a question and the chatbot seizes on one keyword you’ve used and gives you information that is relevant to that keyword, but misses the true meaning.
For instance, if you ask “Do I have to pay for shipping when I return an item?” a badly configured bot might interpret that as just “Do I have to pay for shipping?” and give an answer about delivery costs. Or it might put more importance on the word “return” and give an answer related to returns, but not answering the question you had. Providing the right context and following the correct steps in intent detection can help avoid these issues.
How is Intent Detection trained?
We can’t speak for every provider out there, but because we have a relatively narrow focus (i.e. e commerce) in what we do, we can quite quickly map out most of the “intents” that a customer would reach out with.
Training an intent detection model requires a labeled dataset containing examples of user queries and their corresponding intents. For instance, a labeled dataset for e commerce might include queries about payments, password resets, or order status, each tagged with the appropriate intent class. Here’s an example:
{"text": "I forgot my password", "intent": "password_reset"}
The model learns to classify queries based on this data.
Think about your own business. What proportion of your queries could be bucketed into these categories: Order Status, Returns, Product Information, Product Quality, Change Order? It’s probably anywhere between 50-90%. These are just some of the Intent categories we have built, but you can quickly see how we are able to map out the key intents.
Slot filling is another important task, where the model extracts key information (slots) from queries, such as payment details or password requests. This is especially common in e commerce applications, where slot filling helps automate customer service workflows.
To train the model, we use code to build and train on the labeled dataset, monitoring progress and classification accuracy throughout the process. Tracking these metrics ensures the model continues to learn and improve at the intent detection task.
For providers with wider industry focus, they would need to do this on a bigger scale or develop specific models for different industries. All of which is entirely possible.
Of course, every business is unique and can get questions outside of the norm. If we haven’t anticipated a question you get a lot, then we can build that as a custom intent for you based on a sample of customer messages you’ve received.
Confidence in the Intent Detected
As people, we don’t always communicate clearly so sometimes we have trouble understanding each other. AI Agents are no different, and can sometimes get the wrong end of the stick.
Ongoing evaluation of the model's performance is necessary to ensure reliable intent detection.
Therefore it’s important to have the right level of confidence in the Intent that has been detected. For DigitalGenius, we will assign a confidence to our prediction of Intent, and if our AI is not confident enough it will avoid making a wrong prediction. It is important to evaluate the results and notice any patterns or issues in the model's predictions, as this helps improve performance and ensures accurate intent detection.
This is to avoid situations where the AI has made the wrong assumption and begins having a conversation that ultimately makes no sense.
So you've detected an Intent, now what?
Once the intent has been detected, we then make a decision about what we do with that conversation. The system can route the conversation to the appropriate handler or perform specific actions based on the detected intent. You can also set rules or preferences to determine how the system responds to different intents. We then use our Flow Builder to build agents that take specific actions and responses based on that intent. This can be automating the conversation and resolving it through AI, or it could be passing it over to a human agent – based entirely on your business rules or preferences.
We would also categorize this intent and use that as a data point for you. Doing this across all conversations then gives you a level of analysis to see if particular topics keep coming up from customers. This could allow you to spot if a particular carrier was struggling with deliveries.
Want to see it in action?
The easiest way to see our intent detection in place is to take a look at one of our customers’ chat widgets and play around with it.
But if you want to see how it would apply to your own business, we can do an analysis of your existing tickets to show you how our intents could work with your business. Just book some time with our team here.
If you are interested in learning more about the future of intent detection and how upcoming advancements could benefit your business, reach out to us for more information.
