Case Study: Building a No-Code AI Assistant for Proactive Customer Bonus Consultation

The Challenge: Scaling Personalized Customer Engagement

A company with over 1,000 clients faced a significant challenge: their customer bonus program was complex and unique to each client. Manually notifying every customer about new promotions, calculating their potential earnings, and explaining the intricate rules was an impossible task. This led to low engagement, customer confusion, and missed opportunities for both the clients and the business. The goal was to create an automated solution that could provide proactive, personalized, and clear advice on bonuses.

The Solution: A Custom-Built AI Assistant

As the AI Implementation Specialist on this project, I designed and single-handedly built an AI-powered assistant to solve this problem. Leveraging the no-code platform n8n, I created a robust workflow that integrated with a database to deliver real-time, personalized information to customers via a familiar interface like Telegram.

Here’s how the system works:

1. Trigger-Based Interaction: The AI agent is not just reactive; it proactively messages users based on specific triggers, such as the launch of a new promotion relevant to them, a period of inactivity leading to missed bonuses, or even technical issues with their account.

2. Personalized Bonus Consultation: When prompted, the AI can access the user’s data to provide a complete picture of their bonus status. It can answer questions like:

“What is my current bonus balance?”
“How many bonuses have I missed out on?”
“What’s the best strategy for me to maximize my bonuses right now?”

3. Dynamic Data Integration: The assistant connects to a database (demonstrated with Supabase and PostgreSQL, but designed to work with the client’s existing Vertica DB via API). It pulls specific data points like bonus balance, missed opportunities, active promotions, and last order date to generate its responses.

4. Intelligent Response Generation: Using an integrated AI agent node, the system crafts friendly, easy-to-understand messages.

For example:

“Hi John, there’s a new promotion from Manufacturer X! You can earn 200 extra points on your next purchase of $50 or more.”
“We noticed you haven’t placed an order in a while and have missed out on 150 potential bonus points. Let us help you get them back!”

Architecture and My Role

I architected the entire solution to be both powerful and maintainable. The core components include:

Workflow Automation: n8n was used to orchestrate the entire process, from receiving a user’s message to fetching data and sending a response.
Vector Database: Supabase was configured to store and efficiently retrieve information about promotions and user data.
AI & Memory: A PostgreSQL database provided the AI with memory, allowing it to recall past interactions and maintain context in a conversation.

My role was to translate the business need into a functional technical specification, design the system architecture, and build the complete end-to-end workflow. This involved everything from setting up the database connections and writing the logic in n8n to crafting the prompts that guide the AI agent’s behavior.

The result is a sophisticated AI system built without writing a single line of traditional code, showcasing the power of modern automation tools.

The Outcome

This project successfully automated a critical communication channel, transforming a complex and confusing bonus system into a clear and engaging customer experience. The AI assistant now serves as a personal consultant for each client, driving engagement, increasing participation in promotions, and ultimately adding significant value to the business and its customers.

The Challenge: Scaling Personalized Customer Engagement

The Solution: A Custom-Built AI Assistant

Architecture and My Role

The Outcome

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