Service providers, coaches, and consultants often spend 5 to 10 hours a week on initial client calls that do not convert. Manually vetting leads, coordinating calendar slots, and tracking down consultation fees is an inefficient process that locks up valuable time.
To solve this operational bottleneck, we built a smart intake system that handles these tasks automatically. This application uses an AI agent to conduct initial conversations, ask qualifying questions, process payments, and route qualified leads directly to a calendar without manual intervention.
Moving from Manual Calls to Automated Intake
This project is a conversational qualification bot paired with a scheduling and payment gateway. It is designed for high-volume service providers, such as fitness consultants, business coaches, or specialized advisors.
The system pre-qualifies incoming leads by interacting with them in real-time. It filters out unqualified prospects and collects a payment commitment or deposit upfront. This ensures that only serious, qualified prospects can secure a block on your calendar, solving the problem of abandoned meetings and uncompensated introductory calls.
System Breakdown: Features, Data, and Logic
App Features
The application provides conversational AI intake, dynamic lead segmentation, secure payment collection, and direct calendar syncing. It also includes an administrative dashboard that is restricted to approved team members.
Data Model
The foundation relies on a relational database architecture to maintain strict data consistency.
- The account table stores user profiles, tags, and category assignments.
- The booking table manages scheduled consultation sessions.
- The fz_payment_record and fz_recurring_payment tables track Stripe transactions and subscription states.
- Chat logs and tool usage are stored natively in the fz_conversation, fz_message, and fz_tool_usage_record tables.
Foreign keys link bookings and payments directly to the user account, ensuring no orphaned data exists if a transaction fails. (Editor Note: Add relevant Data Model documentation link).
AI
We configured the AI Agent to act as a Senior Fitness Consultant. The agent uses dynamic prompts based on user responses to collect necessary intake information. It is programmed to check calendar availability within specific timeframes (e.g., 9 AM to 4 PM SGT). Once a lead is successfully qualified, the agent updates the chat status to awaiting_booking_confirmation and generates a booking_id to transition the user to the checkout phase.
Backend Logic
We use Actionflows to process deterministic business rules and manage external webhooks.
- The bookExperience workflow creates a pending booking record and updates the user's tags and email.
- Payment workflows, such as StripeRecurringPaymentDeduction and StripeRefund, parse incoming webhook details. They use branching logic to check if a subscription is active or if a refund succeeded, automatically updating the database status to reflect the real-time financial state.
- The updateCategory workflow segments the user based on the AI's qualification criteria.
Integration
The application integrates with Stripe to process consultation fees and hold deposits. It connects to Calendly via API queries (calendly-invitees, calendly-getEvent) to sync available time slots and register the scheduled events. Additionally, standard webhooks can be configured to send data to Zapier or Make, updating CRMs like HubSpot or Notion.
Design
The frontend includes a clean chat interface for prospects and a management dashboard for the internal team. Visual elements can be quickly mapped from UI generators like Cursor or Lovable. Conditional views dynamically alter the layout, showing administrative tools only to users with the correct roles.
Technical Highlights
To securely segregate data between clients and staff, the system relies on strict Role-Based Access Control (RBAC) via the fz_permission_role table. Furthermore, the payment and booking Actionflows execute as ACID-compliant operations. This ensures that a calendar slot is never confirmed unless the corresponding payment record is successfully written to the database.
Resource Planning and Deployment Time
Building a multi-role, AI-integrated booking platform from scratch usually takes months and requires $15,000 to $30,000 in engineering resources to handle the complex payment and scheduling logic safely.
By utilizing Momen, a working version of this qualification system can be architected and deployed in approximately 30 to 50 working hours. The platform operates on a predictable, resource-based pricing model. This eliminates the cost of external backend hosting and the need to manually configure complex server cron jobs for payment syncing.
Explore the Qualification System
This project serves as a practical example of combining conversational AI with secure, transactional backend workflows. It demonstrates how to handle third-party APIs, relational databases, and role-based permissions in one unified system.
To understand how the agent logic and payment workflows operate together, you can create a new project. Clone it directly into your Momen workspace to inspect the database schema, modify the Actionflows, and test the integration mechanics for your own business.
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