Xether AI - Marketing & Product Website

Overview

The marketing and product website for Xether AI, an enterprise-grade platform for automated data lifecycle management. This site serves as the primary public-facing interface for the platform.

Purpose

This website is not the application dashboard or user interface. It is the marketing site that:

• Explains what Xether AI is and what problems it solves
• Showcases core capabilities and features
• Provides pricing information
• Directs users to sign up or request demos
• Communicates the value proposition to ML engineers, data engineers, and enterprises

What Xether AI Is

Xether AI is not a model. It is not a dashboard toy.

Xether AI is a data infrastructure layer built for teams that train, deploy, and maintain machine learning systems in production. The platform focuses on data as a first-class asset, providing reproducibility, auditability, scalability, and automation across the entire data pipeline.

It is designed to feel like GitLab does for code, but applied to datasets and data pipelines.

Core Features Highlighted

• Dataset Management & Versioning: Git-like version control for datasets
• Automated Data Pipelines: Modular, composable stages for ingestion, cleaning, validation, transformation, and augmentation
• AI-Powered Data Operations: ML models for outlier detection, anomaly detection, and synthetic data generation
• Synthetic Data Engine: Privacy-preserving, controlled synthetic data generation
• Enterprise Orchestration: Event-driven, fault-tolerant pipeline execution
• Observability & Auditability: Full lineage tracking and compliance-ready audit trails

Tech Stack

• Framework: Next.js 16 (App Router)
• Library: React 19.2
• Language: TypeScript
• Styling: Tailwind CSS
• Animations: Framer Motion
• Components: Shadcn/UI (Radix UI primitives)
• Data Fetching: TanStack Query (React Query)
• API Communication: REST (to main backend)

Frontend Architecture

Responsibilities

• Explain what Xether AI is and what problems it solves
• Showcase core capabilities and features
• Provide pricing information
• Direct users to sign up or request demos
• Communicate value proposition to ML engineers, data engineers, and enterprises

Communication

• Backend API: REST only
• No direct storage access: All data flows through backend APIs
• Authentication: OAuth2 flow via backend

Design Philosophy

The website follows these principles:

• Professional: Enterprise-grade, not flashy
• Minimalist: Clean, focused design
• Technical but readable: Speaks to engineers without jargon overload
• No hype language: Honest, clear communication
• Similar tone to: GitLab, HashiCorp, Stripe documentation

Getting Started

Prerequisites

• Node.js 20.9 or later
• npm or pnpm

Local Development

1. Clone the repository

2. Install dependencies:

   npm install

3. Configure environment variables:

   cp .env.example .env.local

   Edit .env.local with your backend API URL and credentials.

4. Run the development server:

   npm run dev

5. Open http://localhost:3000 in your browser

For detailed setup instructions, see docs/SETUP.md.

Documentation

• Setup Guide - Installation and configuration
• API Integration - Backend integration guide
• Theme System - Dark/light mode implementation
• Tasks - Development progress tracker
• Contributing - Contribution guidelines

Deployment

The project is optimized for deployment on Vercel.

Related Components

• Backend: Main API and authentication service
• Main Pipeline: Data processing engine
• Docs: Developer documentation and API references

License

Licensed under the MIT License.