MarketingOS_AI

An AI marketing agency in a box that creates, publishes, and optimizes content across social platforms automatically.

Comment

MarketingOS AI

Inspiration

Small businesses often struggle to maintain a consistent online presence because marketing requires multiple skills—strategy, copywriting, graphic design, video creation, publishing, and performance analysis. Hiring an agency or a dedicated marketing team is expensive, while using multiple disconnected tools makes the workflow inefficient.

I wanted to build a single platform where a business owner could describe their campaign once and let AI assist with the entire marketing workflow.

That idea became MarketingOS AI.

What it does

MarketingOS AI is an AI-powered marketing operating system that helps businesses plan and manage marketing campaigns from one dashboard.

A user can:

  • Create marketing campaigns
  • Define campaign goals and target audience
  • Manage brand information
  • Generate AI-assisted campaign strategies
  • Organize creative assets
  • Preview publishing workflows
  • View campaign analytics
  • Manage multiple campaigns from a single workspace

The application is designed as a foundation for an end-to-end AI marketing platform where multiple AI agents collaborate to automate marketing tasks.

How I built it

The application was built using a modern full-stack architecture.

Frontend

  • Next.js 15 (App Router)
  • TypeScript
  • Tailwind CSS
  • shadcn/ui
  • v0 for rapid UI prototyping

Backend

  • Next.js Server Actions
  • API Routes
  • Prisma ORM

Database

The application uses Amazon Aurora PostgreSQL as the primary database.

Aurora stores:

  • Users
  • Workspaces
  • Campaigns
  • Brand information
  • Campaign metadata
  • Publishing plans
  • Analytics records

Aurora PostgreSQL was chosen because the project requires a scalable relational database with strong consistency, SQL support, and seamless integration with Prisma.

The application is deployed on Vercel, allowing rapid deployment and server-side rendering with Next.js.

Challenges I ran into

The biggest challenge was integrating multiple AI services into one workflow.

Some third-party APIs required paid plans or production access, making it difficult to demonstrate every planned feature during development.

Examples include:

  • AI video generation
  • Meta Ads publishing

Instead of removing these modules, I designed the application so the interfaces and workflow are already integrated, making it straightforward to enable them once production API access is available.

Another challenge was configuring Amazon Aurora PostgreSQL networking and securely connecting the database with Prisma and Vercel.

What I learned

This project helped me gain practical experience with:

  • Building scalable applications using Next.js 15
  • Designing reusable component systems
  • Using Prisma with Amazon Aurora PostgreSQL
  • Deploying production-ready applications on Vercel
  • Database modeling for SaaS applications
  • Structuring AI-powered workflows
  • Planning cloud-based application architecture

I also learned how to balance product design, backend engineering, and cloud infrastructure within a single project.

Future Improvements

The long-term vision for MarketingOS AI includes:

  • Fully automated AI-generated image creation
  • AI video generation
  • Meta Ads campaign publishing
  • Multi-agent orchestration
  • Learning-based campaign optimization
  • Real-time analytics synchronization
  • Support for additional social media platforms
  • Team collaboration and approval workflows

The goal is to evolve MarketingOS AI into a complete AI-powered marketing operating system for startups, agencies, and small businesses.

Built With

Share this project:

Updates