Gathering and analyzing client requirements is traditionally a manual, time-consuming process prone to miscommunication. Customers often provide raw, scattered thoughts that lack technical structure, making it difficult to define a clear scope.
Manually parsing these unstructured customer inputs to formulate actionable requirements slows down product iteration, service delivery, and business scaling. When details are lost in translation, development cycles inevitably suffer.
To address this, we built an AI Requirement Analysis app using Momen. This project automates the collection, structuring, and analysis of user needs. By combining an integrated AI bot with a visual backend, it transforms rough ideas into structured user stories, acceptance criteria, and actionable layouts.
The Core Challenge: Structuring Unstructured Ideas
This project showcases an AI-powered needs analysis bot designed to extract and organize structured requirements from raw user inputs. It targets the messy "idea phase" of product development or client onboarding.
The system solves the problem of unstructured data intake. Instead of a team member manually reading through a brainstorm document, the AI parses the text, asks clarifying questions, and ensures that customer requirements are accurately translated into actionable next steps.
Businesses like development agencies, SaaS platforms, consulting firms, and product managers can use this type of application to automate client evaluation. Momen enables builders to launch this app quickly by providing a unified platform for the database, AI agent orchestration, and frontend interface. It focuses on structural flexibility and speed, allowing non-technical founders to build professional-grade AI workflows visually without relying on fragmented third-party integrations.
Behind the Scenes: Core Features and System Architecture
App Features
The application acts as an end-to-end requirement pipeline.
- Data Management & Structuring: It takes a rough requirement input and uses AI to generate a structured requirements list.
- Clarifying Questions: The app automatically generates follow-up questions, allowing users to update answers and refine the scope.
- Automated Generation: It translates refined needs into formal user stories, acceptance criteria, and suggested page layouts.
- Authentication & History: Users have secure logins to save, revisit, and track their analysis history.
- Payments & Notifications: The system supports optional gating for premium consulting reports and alerts users when analysis is complete.
- APIs: Native API connections to advanced LLMs parse context and generate these structured outputs.
How It's Built With Momen
Data Model
The foundation of the app is a relational data model built visually to store customer information and analysis parameters.
- Accounts & Roles: System-default tables (account, fz_permission_role) handle secure user access.
- Analysis Records: The analysis table tracks the core session, storing summaries, status (has_analyzed), and the relation to the user account.
- Structured Requirements: The structured_requirement table stores individual parsed needs, linked directly to the parent analysis session.
- Clarifications: The clarifying_question table logs generated questions and user answers.
- Actionable Specs: The user_story table tracks roles, goals, and benefits. Each story connects to an acceptance_criterion table for detailed conditions. A separate page_layout table maps suggested UI structures back to the analysis.
AI Agent Configuration
The core intelligence relies on a "Project Analysis Assistant" AI Agent. We assign the agent a specific system role (e.g., Momen expert and product architect).
Instead of generating raw conversational text, the agent references the structured database for context. We configure the output format as Custom JSON. This ensures the AI returns clean, structured data arrays that the database can immediately ingest and sort into requirements, stories, and layouts.
Backend Logic
Workflows, known as Actionflows in Momen, orchestrate the multi-step parsing process.
When a user submits a rough idea, an Actionflow captures the frontend input and triggers the AI request. Once the AI returns the JSON response, the backend logic processes it to insert or update records in the structured_requirement and clarifying_question tables. Additional Actionflows handle data maintenance, such as updating user answers, formatting text for display, and securely deleting analysis sessions when requested.
Integration
The application connects to standard LLMs seamlessly through Momen’s native AI configuration panel. This handles the complex parsing logic without requiring custom API webhooks or external middleware services.
Design and UI
The interface is constructed using Momen’s drag-and-drop UI builder, focusing on a clear, step-by-step user journey.
- Login View: A standard authentication layout directing users to their workspace.
- Analysis Dashboard: The left sidebar manages history, displaying previous sessions and offering a "Try New Idea" trigger.
- Main Stepper: The central view uses a Stepper to guide the user through three phases: Entry (inputting the raw idea), Refinement (a split view showing generated features alongside clarifying questions), and Analysis (a selector view toggling between generated User Stories and Page Layouts).
- Processing States: A conditional full-screen mask and animation trigger during AI execution to provide clear system feedback.
Technical Highlights
This architecture demonstrates significant modularity, as database tables and Actionflows are separated by function. It provides real-time capability by binding frontend components directly to the database, ensuring that as soon as the AI populates a user story, the UI updates. Finally, the native relational database ensures scalability across thousands of saved analysis sessions.
To see the exact configuration process, read the documentation: How to Build an AI Needs Analysis Project.
Development Time and Resource Breakdown
Development Time
A working MVP of this AI requirement analysis bot can be built in approximately 24 hours (roughly 0.5 days for data modeling and backend logic, and 2.5 days for frontend design). Because Momen relies on visual components and native AI integration, the process bypasses traditional backend setup and API endpoint coding.
Cost Analysis
Building an application of this complexity with traditional development would require hiring frontend and backend engineers, setting up database hosting, and managing external API connections. By utilizing Momen, these fragmented costs are consolidated into predictable platform fees and standard LLM usage costs, requiring only a basic platform plan to handle multiple Actionflows and AI requests.
Explore and Clone the Live Project
You can interact with the fully functional application and inspect its underlying architecture directly.
To understand exactly how the custom JSON output maps to the relational database, we highly recommend cloning this project into your Momen workspace. Exploring the cloned application allows you to look under the hood at the specific AI prompts, data model configurations, and Actionflow setups.
If you are willing to view this project in the Momen editor, you can also check it here.
Top comments (0)