project_matrix.sh

$ cat project.txt

AI ADS RECOMMENDS

AI-powered advertisement recommendation system. Uses LLM agents and vector embeddings to surface the most relevant ads for each advertiser and context.

$ ls -la project/

AI ADS RECOMMENDS

A recommendation platform that analyzes advertiser data and user context to select optimal ads through multi-agent LLM orchestration.

Nuxt FastAPI Python PostgreSQL OpenAI

The Challenge

Traditional ad systems lack semantic understanding of ad-user-context relationships, leading to low relevance and wasted spend.

Our Solution

A multi-agent backend evaluates ads against context, with vector embeddings for semantic retrieval and PostgreSQL persistence, served through a Nuxt chat-style interface.

Key Features

Multi-agent LLM orchestration with OpenAI
Vector embeddings for semantic ad matching
Chat-based advertiser interaction
Multi-advertiser account support
Recommendation ranking pipeline
Dockerized full-stack development environment

Technical Architecture

Frontend

  • framework: Nuxt 4
  • stateManagement: Pinia
  • styling: Tailwind CSS
  • testing: N/A

Backend

  • runtime: Python
  • framework: FastAPI + Celery
  • database: PostgreSQL
  • caching: Redis

Deployment

  • hosting: Docker
  • ci_cd: GitHub Actions
  • monitoring: N/A

Results & Impact

Semantic matching improves ad relevance over keyword rules

Multi-agent supervision keeps recommendations on-context

Vector retrieval scales to large ad inventories

Containerized stack streamlines local development