MATIH Enterprise Platform

From Intent to Insights

MATIH is a cloud-agnostic, Kubernetes-native platform that unifies Data Engineering, Machine Learning, Artificial Intelligence, and Business Intelligence into a single conversational interface. Ask questions in natural language, get data-driven insights in seconds.

What This Documentation Covers

This documentation is the definitive guide to the MATIH Platform, organized as a 20-chapter reference covering every layer of the system. Whether you are a data engineer connecting data sources, a BI developer building dashboards, an ML engineer training models, or a platform administrator managing tenants, you will find comprehensive guidance here.

Platform at a Glance

Quick Navigation

Getting Started

• Introduction to MATIH - Vision, capabilities, and user personas
• Installation & Setup - Local development and cloud deployment
• Quickstart Tutorials - Your first query, dashboard, and model

For Developers

• Architecture Deep Dive - System design and service topology
• AI Service - Agent orchestrator, text-to-SQL, RAG
• Frontend Architecture - Workbench components, state management

For Platform Engineers

• Kubernetes & Helm - Cluster topology, chart structure
• CI/CD & Build System - Pipeline stages, deployment workflow
• Observability - Monitoring, tracing, alerting

For Administrators

• Security & Multi-Tenancy - Authentication, RBAC, tenant isolation
• Tenant Lifecycle - Provisioning, DNS, ingress
• Platform Services - Config, notifications, billing

Documentation Status

This documentation reflects the current state of the MATIH Platform as of February 2026. Content is continuously audited against the actual codebase to ensure accuracy. Sections marked as Planned indicate features that are designed but not yet fully implemented.

How to Read This Book

Part I (Chapters 1-3): Platform foundation - architecture, security, and design philosophy.

Part II (Chapters 4-5): Getting started - installation, configuration, and hands-on tutorials.

Part III (Chapters 6-8): Control Plane - identity management, tenant provisioning, platform services.

Part IV (Chapters 9-11): Data Plane core - query execution, data catalog, pipelines.

Part V (Chapters 12-14): AI/ML platform - conversational analytics, ML operations, knowledge graphs.

Part VI (Chapters 15-16): Frontend - workbench architecture and user experience guides.

Part VII (Chapters 17-19): Infrastructure - Kubernetes, CI/CD, observability.

Part VIII (Chapter 20): Reference - API docs, ADRs, glossary, and appendices.