DeepVisor 🚀

Digital Advertising Platform Project

A work-in-progress learning project with startup aspirations

Project Overview

DeepVisor is an in-development platform that aims to simplify digital advertising management across multiple platforms. As a college project focused on learning modern web development and API integration, it serves both as a practical education in software engineering and a potential startup concept.

Current Status: Early development with Meta API integration in progress.

Vision

The vision for DeepVisor is to create a unified dashboard where users can:

• Create and manage ad campaigns across multiple platforms
• View normalized performance metrics in one place
• Leverage AI for campaign optimization and budget allocation
• Generate client-friendly reports and analytics

Tech Stack

• Frontend: Next.js 15+ with Mantine UI
• Backend: Next.js API routes with Node.js
• Database & Auth: Supabase (PostgreSQL + Authentication)
• API Integrations: Meta Marketing API (in progress)

Current Implementation

Meta Integration (In Progress)

The current focus is on implementing Meta (Facebook & Instagram) ad campaign creation:

• ✅ OAuth authentication with Meta Business accounts
• ✅ Campaign builder UI with form validation
• 🔄 Campaign creation API integration
• 🔄 Strategy Pattern for flexible campaign parameters
• ⬜ Reporting and analytics

Code Architecture

We're implementing the Strategy Pattern for flexible campaign parameter building:

// Example of Strategy Pattern implementation
export async function createCreative(params: MetaCreativeParams): Promise<string> {
  // Get the appropriate strategy based on campaign objective
  const strategy = getCreativeStrategy(formData.objective);
  
  // Apply the strategy to get objective-specific parameters
  const creativeParams = strategy.buildCreativeParams(
    baseParams, 
    formData, 
    pageId,
    isSmartCampaign,
    creativeVariation
  );
  
  // API call with the parameters
  // ...
}

Future Development Plans

While currently focused on Meta integration, the project has ambitious plans for expansion:

System Architecture Vision

graph TD
    A[Client Application] --> B[API Gateway]
    
    B --> C[Campaign Service]
    B --> D[Analytics Service]
    B --> E[User Management]
    B --> F[Billing Service]
    B --> G[AI Optimization Service]
    
    C --> H[Meta Integration]
    C --> I[TikTok Integration]
    C --> J[Google Integration]
    
    D --> K[Data Lake]
    G --> K
    
    K --> L[Analytics Engine]
    L --> D

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Development Roadmap

┌─────────────────────┐    ┌─────────────────────┐    ┌─────────────────────┐
│     Current State   │ -> │  Near-Term Goals    │ -> │ Long-Term Vision    │
├─────────────────────┤    ├─────────────────────┤    ├─────────────────────┤
│ • Meta Integration  │    │ • Enhanced Smart    │    │ • Full AI-Powered   │
│   (in progress)     │    │   Campaigns         │    │   Media Buying      │
│ • Basic Campaign UI │    │ • Multi-Platform    │    │ • Budget Management │
│ • Strategy Pattern  │    │   Campaigns         │    │   Across Platforms  │
└─────────────────────┘    │ • Unified Analytics │    │ • Creative Testing  │
                           └─────────────────────┘    └─────────────────────┘

Multi-Platform Strategy

The architectural plan includes a Platform Adapter pattern to support multiple advertising platforms:

graph TD
    A[Campaign Creation UI] --> B[Platform Adapter Factory]
    B --> C[Meta Adapter]
    B --> D[TikTok Adapter]
    B --> E[Google Ads Adapter]
    B --> F[LinkedIn Adapter]
    
    C --> H[Meta Campaign Strategy]
    D --> I[TikTok Campaign Strategy]
    E --> J[Google Campaign Strategy]
    F --> K[LinkedIn Campaign Strategy]

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Smart Campaigns Feature Plan

Future versions aim to implement AI-powered campaign optimization:

graph LR
    A[Smart Campaigns] --> B[Audience Discovery]
    A --> C[Creative Testing]
    A --> D[Budget Optimization]
    
    B --> B1[Lookalike Generation]
    B --> B2[Interest Analysis]
    
    C --> C1[Multi-Variate Testing]
    C --> C2[Creative Elements Analysis]
    
    D --> D1[Budget Shifting]
    D --> D2[ROAS Optimization]

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Analytics Vision

The planned analytics system will unify data from multiple platforms:

graph TD
    A[Platform APIs] --> B[Data Ingestion Layer]
    B --> C[Data Normalization]
    C --> D[Unified Data Schema]
    D --> E[Combined Analytics]
    E --> F[Unified Dashboard]
    E --> G[Cross-Platform Insights]

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Learning Goals

This project serves as a practical application of:

1. Modern Web Development

   • Next.js framework with React
   • Mantine UI component library
   • Server-side rendering and API routes

2. Database Design

   • Supabase PostgreSQL schema design
   • Authentication and authorization
   • Data relationships for complex ad structures

3. External API Integration

   • OAuth flows and token management
   • Campaign creation via Meta Marketing API
   • Error handling and rate limiting

4. Design Patterns

   • Strategy Pattern for flexible parameter building
   • Adapter Pattern for platform-agnostic interfaces
   • Factory Pattern for strategy creation

5. System Architecture

   • Planning scalable systems
   • Designing for future expansion
   • API design best practices

Project Scope

As a learning project with startup potential, DeepVisor focuses on:

1. Technical Implementation: Building a functional system that integrates with ad platforms
2. Architecture Design: Creating a foundation that can scale with additional features
3. Software Engineering Practice: Following best practices for code organization and patterns
4. User Experience: Designing intuitive interfaces for complex advertising tasks

About the Developer

I'm a college student developing DeepVisor to enhance my software engineering skills while exploring the digital advertising market. The project represents both a learning journey and a potential business idea.

For questions or collaboration opportunities, feel free to contact me at yengnerb475@gmail.com.

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© 2023 DeepVisor - A Software Engineering Learning Project