TL;DR
AI is no longer just about chatbots—it can provide real, tangible support. This blog explores how an AI-powered mental health assistant was built using Momen, leveraging multi-agent collaboration, real-time data retrieval, and automated workflows.
A Momen user, Eric, built this assistant to offer personalized crisis intervention. The app retrieves user medical history, symptoms, and emotional state to generate real-time AI-driven support—all while running seamlessly in the background.
Watch the video breakdown:
🛠 How It Works
1️⃣ User Input & Data Retrieval
The user submits an inquiry about their health or emotional state.
The system retrieves relevant medical history, past diagnoses, and emotional feedback from the database.
2️⃣ AI-Driven Analysis
Vector search is used to match symptoms with existing intervention strategies.
AI cross-references symptoms with predefined crisis plans to suggest the most relevant response.
3️⃣ Personalized Intervention
The system analyzes the user’s condition in real-time and formulates a tailored intervention strategy.
AI recommendations evolve based on user interactions, improving accuracy over time.
🔧 How It Was Built
🖌 Generating UI with Loveable
Eric used Loveable to generate a UI based on a Product Requirements Document (PRD). Loveable provided an auto-generated UI, which was then refined in Figma before being built in Momen.
🏗 Structuring the Database in Momen
A structured database was essential for AI-driven interactions. The app's database stores:
User medical history & medications
Journal entries & emotional feedback
Predefined crisis intervention plans
Momen's backend ensures fast retrieval and scalability, handling complex user data efficiently.
💻 Building the UI with Momen's Drag and Drop Editor
Eric quickly builds out the UI and binds the data in the database to make it interactive.
🤖 AI Agents & Actionflows
Unlike a basic chatbot, this app operates through 7 AI agents, each handling a specific function:
Classification Agent → Determines if input is a casual chat or a medical concern.
Symptom Agent → Matches symptoms with crisis intervention strategies.
Chat Agent → Engages users in casual conversations while maintaining context.
Summary Agents (x4) → Summarize user interactions to ensure context-aware responses.
Each AI agent runs in Momen’s Actionflows, ensuring seamless automation.
✨ Key Highlights
✔ Multi-Agent Collaboration → AI agents work together to retrieve and analyze user data.
✔ Real-Time Context Awareness → The assistant pulls the latest user data for informed responses.
✔ Seamless Backend Execution → Everything runs in the background, ensuring a smooth experience.
✔ Scalable & Cost-Effective → Built in under 10 hours using Momen’s no-code platform.
🚀 Try It Yourself!
Eric’s project will be soon launched as a Momen template! Clone it to explore the setup and customize it for your own AI-driven applications.
🔗 Check out the template in Momen → Momen editor
📺 Watch the full breakdown → How to build an AI mental health assistant
📝 Read more on the blog → A deep dive of the agentic workflow
Top comments (0)