In today’s fast-paced digital world, we are drowning in information. Whether it’s customer reviews, support tickets, or social media comments, manually reading and sorting through every piece of text is a recipe for burnout.
What if you could build a "brain" that reads your data for you and organizes it instantly?
With no-code platforms like Momen, you don't need to be a software engineer to build sophisticated AI tools. In this tutorial, we will show you how to build an AI Content Classifier that automatically reads user feedback, determines the category, senses the urgency, and detects the sentiment—all in seconds.
What Can You Build With This?
While our example focuses on customer feedback, the logic we’re building today can be expanded to many other scenarios:
- HR Departments: Automatically sort job applications by department and experience level.
- E-commerce: Flag negative product reviews that require immediate follow-up.
- Real Estate: Categorize incoming leads based on budget and preferred location.
- Content Creators: Sort YouTube or blog comments into "Questions," "Praise," or "Spam."
Let's Get Started: A Step-by-Step Tutorial
Before we dive in, you can follow along with the Official Documentation and the Step-by-Step Video Tutorial.
Step 1: Preparing the "Digital Filing Cabinet" (Data Model)
First, we need a place to store the information. In Momen, we create a table (we’ll call it ticket). This table needs four columns:
Field Name
Type
Note
description
Text
Original feedback provided by the user.
category
Text
AI-determined category (e.g., Billing Issue, Technical Support).
urgency
Text
AI-determined priority level (High, Medium, Low).
is_positive
Boolean
Sentiment analysis (True for positive/neutral, False for negative).
Step 2: Training Your AI Agent
This is the "brain" of your app. Go to the AI tab in Momen and create an agent named Agent_feedback.
- Structured Output: This is the secret sauce. Instead of the AI just "chatting" with you, we tell it to fill out a specific form (JSON). We define the fields for category, urgency, and sentiment. This ensures the AI always gives us clean data that fits perfectly into our table.
Step 3: Creating the Workflow (Actionflow)
Now we connect the dots using an Actionflow. This is like a flowchart that tells the app: "When the user clicks the button, do X, then Y."
- Trigger: It starts when the user types something.
- AI Node: It sends that text to our AI Agent.
- Database Node: It takes the AI’s "answers" and saves them directly into our ticket table.
Step 4: Building the User Interface
You don't need a degree in design. On your page, simply drag and drop:
- A Text Input box (for users to type their feedback).
- A Button (to submit).
- The Connection: Set the button to "Run Actionflow" and point it to the workflow we created in Step 3.
Testing Your Creation
Now for the fun part! Switch to Preview mode. Type something like: "I've been trying to log in for an hour and it keeps giving me an error!"
Click the button, then check your database. You should see a new entry:
- Category: Technical Support
- Urgency: High
- Sentiment: False (Negative)
Ready to Try It Yourself?
You can explore the exact project we built here: Project Access Link.
Building with AI no longer requires years of coding knowledge.
With tools like Momen, if you can imagine the logic, you can build the solution.
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