Project Introduction
Meetings can often feel like a chaotic whirlwind, especially for those in customer-facing roles, with the endless cycle of note-taking, action item extraction, and schedule management becoming overwhelming. Imagine having an AI agent that automates these trivial tasks, allowing you to focus on what truly matters. This AI-powered meeting assistant, developed with Momen, streamlines the management of meeting notes and action items through intuitive interaction. It automatically extracts key tasks from meeting transcripts and seamlessly syncs them with users' Google Calendars.
By alleviating the burden of manual data entry and task tracking, this assistant helps teams avoid getting lost in the shuffle of frequent meetings. With the automation of extraction and scheduling, teams can remain focused on their core business objectives without being bogged down by administrative tasks.
Discover how to build an AI meeting assistant with Momen that efficiently manages your meeting notes and schedules action items, transforming your meeting experience.
Why Choose Momen for Your AI Agents?
Momen offers an integrated AI agent builder, enabling you to create full-stack AI applications. Moreover, with RAG and tools invocation, your AI agents can retrieve accurate information from your own data and interact with the external world by making decisions and using tools such as backend workflows, APIs, or even another agent.
Additionally, your agent's workflow is highly customizable. You can place your AI agent wherever you want within your business workflow, or set up a team of agents to work for you.
What sets Momen's AI development framework apart is that it outputs data in well-organized JSON formats. LLMs understand code better than text. For most AI agent builders, you may need to structure your output manually to achieve accurate results. However, with Momen, this is already done for you, allowing you to focus on your core business logic.
Core Features and Functions of the AI Meeting Assistant
Features & Highlights
Integrated LLMs: Built-in integration with OpenAI LLMs eliminates manual API integration, saving time and effort.
Data management: Store all message content for easy debugging and optimization, enhancing the reliability of your AI.
API integration: Integrate Google Calendar API for seamless task management and scheduling.
Structured output: Automatically output meeting notes in well-organized JSON formats, facilitating accurate agent interactions.
Step-by-Step Guide to Building with Momen
Building your AI meeting assistant with Momen involves several key steps, from designing your database structure to configuring AI settings and integrating APIs.
1. Data model
The structure of your database dictates user interaction and functionalities in your web app. To start building your project, the first thing to consider is the structure of your database.
If you think of your whole data setting as a house, data model will be the architecture of this house. It determines how your data is organized and related, and will be reflected as your functionalities. Below is the data model of this AI meeting assistant.
Besides the system table that already exists, we need to create 4 extra tables:
- Meeting: This table is for storing the input meeting content.
- Conversation: This table will store all the tasks of different users from one meeting.
- User: Stores the tasks of different assignees.
- Task: The analyzed meeting tasks will be output and stored here. You can add fields like title, description, start/ending data etc.
2. AI Configuration
The AI configuration in this case involves prompt setting and output setting. Once you setup your model and inputs, define your AI's role, goals and constraints.
An important part of this configuration is the structured output. Custom outputs will output data in JSON format, which will help AI better understand the content. Since we already created the table and defined the fields, this data will be stored in the task table.
You can test your AI in the right debugging panel to see if it meets your expectations.
3. Backend Logic
Backend logic is what handles the functions of your application. This is called Actionflow in Momen. In this AI assistant, we have two important Actionflows:
- Once the meeting notes are input, AI will analyze and output structured data in JSON format. Then this data will be stored in the database, including task details, assignees, data, duration etc.
- The second Actionflow is to create Google Calendar tasks. The extracted tasks will be stored in your Google Calendar.
4. Integration
In this case we integrated Google Calendar API. You can find your tokens, client ids etc in the Google Developer site. Here we choose POST for modifying data in Google Calendar. Learn more details about API configuration in our doc.
5. Design
Once our data and logic is done, we can look into our frontend design.
UI
The UI design of this project is rather simple. You'll need one input box for inputting your meeting notes. A list to display the output of AI. One button for triggering AI actionflow and one for triggering updating Google Calendar.
Action Configuring
One thing to note that in this case, Actionflows are triggered in the frontend, which means by actions like clicking. So we need to configure the action of triggering actionflow for these two buttons.
Value Proposition and Commercial Potential
As remote meetings become more commonplace, the demand for effective AI-driven tools is growing. Building your internal tool with Momen not only streamlines operations but also opens up market opportunities.
For those considering monetization, options include setting up an account system with Momen’s login features or integrating a membership model with payment processing.
Cost Efficiency of Building Your AI Agent
Time
Building an AI agent with Momen is not only time-efficient but also cost-effective. In just 16 hours, you can create a fully functional agent, significantly less than traditional coding methods, which can take up to 56 hours.
Here’s a breakdown of the estimated time for each task:
- Data model design and setup: 2 hours
- Frontend setup: 1 hour
- Understanding API and importing to Momen: 1 hour
- Configuring task analysis agent: 2 hours
- Creating an AI analysis Actionflow (createTask): 2 hours
- Creating an Actionflow to call the API and add events to Google Calendar (createGoogleCalendarEvents): 5 hours
- Testing and debugging: 1–3 hours Compared to other no-code tools, Momen stands out as the only platform that offers built-in capabilities for agent building, including Retrieval-Augmented Generation (RAG), tools invocation, and structured output. For instance, if you were to build a similar project using Bubble, it would take approximately 32 to 40 hours. This is primarily because Bubble lacks built-in agent-building features. You would need to rely on plugins to connect to the OpenAI API, manage the preprocessing of data into JSON format, and store the returned results in a database before displaying the information on the frontend. Momen simplifies this entire process, allowing you to focus on developing your AI solution without the added complexity.
Cost
For a demo project like this, it's free to build in Momen. Our free plan offers 1 AI customization, 1 API and 1 Actionflow. If you want to monetize your AI apps, you can go with Pro plan ($85/month) which supports payment and permission control.
Project Preview
One of our users, Cole Medin, who is also a Youtuber, tried this tool and made a video about it. Check out Cole's video for a detailed walkthrough of this project and see Momen in action. See the video here.
Start to build your AI agents with Momen today: https://momen.app/ai
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