Foreseen AI
This project was initiated to solve specific technical and domain challenges. Building a full‑stack AI solution integrating forecasting, recommendation and AI assistant features with a scalable data pipeline for DTC brands.
About This Project
Building a full‑stack AI solution integrating forecasting, recommendation and AI assistant features with a scalable data pipeline for DTC brands.
Developed microservices around Snowflake and Supabase for data ingestion and storage, integrated retrieval‑augmented generation with modern embedding models, and built sleek dashboards using React and Tailwind CSS.
Provided brands actionable insights and automation, accelerating decision‑making and improving customer engagement.
Full-stack Developer
2024
Public
Personal
Technology Stack
Project Story
Building a full‑stack AI solution integrating forecasting, recommendation and AI assistant features with a scalable data pipeline for DTC brands.
Developed microservices around Snowflake and Supabase for data ingestion and storage, integrated retrieval‑augmented generation with modern embedding models, and built sleek dashboards using React and Tailwind CSS.
Provided brands actionable insights and automation, accelerating decision‑making and improving customer engagement.
Insights & Takeaways
Highlights
- Case study content natively baked into the project dataset.
- Clear storytelling built around the specific problems faced and the technologies used.
Challenges
- Strict focus on performance and maintainability.
- Selecting standard tools to ensure scalability: React, TypeScript, Tailwind CSS, Supabase, Snowflake, Python
- Developed microservices around Snowflake and Supabase for data ingestion and storage, integrated retrieval‑augmented generation with modern embedding models, and built sleek dashboards using React and Tailwind CSS.
Lessons Learned
- Provided brands actionable insights and automation, accelerating decision‑making and improving customer engagement.
