AI Recommendation Backend

This project is a AI recommendation backend built using BuildShip to illustrate how personalized decision logic can be implemented as a clean, API-driven workflow.

The system exposes a REST API endpoint that accepts user preferences and contextual information, then processes this input through a recommendation layer designed to simulate AI-driven personalization. The workflow returns structured output including recommended items, reasoning behind the suggestions, and a confidence score.

The project demonstrates:

Recommendation-style backend workflow design

Structured input and output schemas for personalization logic

API-first architecture using BuildShip

Rapid prototyping of AI-style recommendation systems

This implementation is intentionally delivered as a demo / proof-of-concept, focusing on clarity, reusability, and clean architecture rather than production integrations. It reflects a common pattern used in product recommendation systems, internal tooling, and decision-support applications.