Architecture diagram

AI-Powered Personalized Upsell & Recommendation System

Architecture & Workflow Diagram Documentation

1. 🧠 System Overview

This system is an AI-driven personalization and upselling platform that dynamically adapts to user behavior, preferences, and interaction patterns.

It uses:

Multiple AI agents
Real-time tracking
Reinforcement learning
A centralized orchestrator
External communication tools
to drive personalized experiences and maximize upsell conversion.

2. 🧩 Core System Layers

🟦 1. User & UI Layer

Entities:

User
Frontend UI

Responsibilities:

User enters personal data (age, preferences, features)
User interacts with UI (clicks, scrolls, time spent, navigation)
UI tracks every action and sends it to Event Tracker

🟩 2. Tracking & Data Layer

Entity:

Event Tracker

Responsibilities:

Logs user behavior and interaction events
Stores:
- Clickstream data
- Engagement time
- Navigational paths
- Abandon signals
Feeds this data to agents and learning systems

🟨 3. AI Agent Layer

3.1 Personalization Agent

Generates personalized questions and interaction flows using:

User metadata
Historical behaviors
Interaction logs

Outputs:

Sends adaptive questions to UI
Shares context with BestBuy Assistant
Communicates with Orchestrator

3.2 BestBuy Assistant

This is the core upsell intelligence agent.

Connected to:

Personalization Agent
Event Tracker
Recommender Engine
Knowledge Base (RAG System)

Responsibilities:

Generates personalized upsell suggestions
Adapts communication tone & price strategy
Uses real-time behavior + historical data
Leverages RAG for personalized insights

🔵 4. Intelligence & Learning Layer

Feedback Learning & Reinforcement Engine

Capabilities:

Pattern detection in user behavioral data
Reinforcement learning for:
- Better upsell strategies
- Improved question selection
- Personalized engagement adaptation
Feeds optimized parameters back to both agents

🟣 5. Control & Orchestration Layer

Orchestrator

Responsibilities:

Controls communication between agents
Manages tool invocation
Decides which channel to use for user interaction
Synchronizes AI workflows

Connected to all major components:

Personalization Agent
BestBuy Assistant
Tools Layer

⚫ 6. External Tools Layer

These tools are responsible for interacting with the user:

Tool	Purpose
Calling Tool	Voice-based user communication
Email Tool	Email marketing & notifications
Notification Tool	In-app/system notifications
Other Tools	SMS, Chat, WhatsApp, Push systems

3. System Interaction Workflow

Main interaction flow:

User enters personal data
UI tracks behavioral interactions
Data flows into Personalization Agent
Personalization Agent creates personalized questions
Event Tracker logs behavior
BestBuy Assistant analyzes:
- User data
- Logs
- Knowledge base
- Recommender insights
BestBuy Assistant sends decisions to Orchestrator
Orchestrator triggers external tools
Learning Engine continuously improves the system

4. Mermaid Architecture Diagram

You can paste this into Markdown tools that support Mermaid (Notion, GitHub, Obsidian, etc.)

flowchart LR

U[User] --> UI[UI Layer]

UI --> ET[Event Tracker]

UI --> PA[Personalization Agent]
ET --> PA

PA --> BBA[BestBuy Assistant]

ET --> BBA
RE[Recommender Engine] --> BBA
KB[Knowledge Base - RAG] --> BBA

BBA --> ORCH[Orchestrator]

FL[Feedback & Reinforcement Learning Engine] --> PA
FL --> BBA
ET --> FL

ORCH --> CT[Calling Tool]
ORCH --> EM[Email Tool]
ORCH --> NT[Notification Tool]
ORCH --> OT[Other Communication Tools]

CT --> U
EM --> U
NT --> U
OT --> U