Chapter 1: Introduction to MATIH
A comprehensive introduction to the MATIH Enterprise Platform -- its vision, the problems it solves, the capabilities it delivers, the personas it serves, and the technology stack that powers it. This chapter establishes the conceptual foundation for every subsequent chapter in this documentation.
Learning Objectives
- Understand the Intent to Insights paradigm and why it matters for modern data organizations
- Identify the five systemic problems in the modern data stack that MATIH addresses
- Navigate the six capability pillars: conversational analytics, data engineering, BI, ML, governance, and operations
- Recognize the six primary personas and their journey maps across platform workbenches
- Map the technology stack across backend, frontend, data, ML, and infrastructure layers
- Use the terminology glossary as a reference throughout the rest of this documentation
Details
- No prior knowledge of the MATIH Platform required
- Familiarity with cloud computing concepts is helpful but not required
- Ch. 2: Architecture Deep Dive
- Ch. 3: Security and Multi-Tenancy
- Ch. 4: Installation Guide
- Ch. 5: Quick Start
Welcome to the MATIH Enterprise Platform documentation. This chapter provides a thorough introduction to the platform, from its founding vision to the detailed technology choices that bring that vision to life. Whether you are a business analyst evaluating the platform, a data engineer preparing to integrate it, or a platform administrator planning a deployment, this chapter gives you the context you need to proceed confidently.
1.1What is MATIH?
MATIH is a cloud-agnostic, Kubernetes-native platform that unifies four traditionally siloed disciplines -- Data Engineering, Machine Learning, Artificial Intelligence, and Business Intelligence -- into a single system with a conversational interface at its core.
The platform's founding premise is captured in three words: Intent to Insights. A user expresses an intent (a question about their data, a request for an analysis, a hypothesis to test), and the platform transforms that intent into insights (validated, visualized, and contextualized answers) through a fully automated pipeline of AI agents, query engines, and visualization services.
MATIH is not a single application. It is a platform of platforms, composed of:
| Dimension | Details |
|---|---|
| Control Plane | 10 Java/Spring Boot 3.2 microservices managing identity, tenants, configuration, billing, auditing, and observability |
| Data Plane | 14 polyglot microservices (Java, Python, Node.js) executing data queries, AI orchestration, ML workflows, pipeline management, and BI operations |
| Frontend | 8 purpose-built React/TypeScript/Vite workbench applications, each designed for a specific persona |
| Infrastructure | 55+ Helm charts, multi-cloud Terraform modules (Azure, AWS, GCP), fully Kubernetes-native |
| AI Engine | LangGraph multi-agent orchestrator with text-to-SQL generation, RAG, and WebSocket streaming |
| Observability | Full-stack monitoring with Prometheus, Grafana, Loki, Tempo, and OpenTelemetry |
1.2Chapter Structure
This chapter is organized into nine sections. Each section can be read independently, but reading them in order provides the most complete understanding.
| Section | Description | Best For |
|---|---|---|
| Vision and Mission | The founding principles, the conversational paradigm, design philosophy, and strategic roadmap | Everyone -- start here |
| Problem Space | The five systemic problems in the modern data stack and the value MATIH delivers | Decision makers, architects, evaluators |
| Platform Capabilities | Deep dive into each of the six capability pillars with technical details | All technical roles |
| User Personas | Six detailed persona profiles with day-in-life workflows and journey maps | Product managers, UX designers, all users |
| Technology Stack | Every technology in the platform, organized by layer, with decision rationale | Developers, platform engineers, architects |
| Key Terminology | Comprehensive glossary of 100+ terms organized by domain | Everyone -- reference throughout |
| Architecture Preview | High-level architecture teaser connecting this chapter to the deep dive in Chapter 2 | Architects, senior engineers |
| Getting Started Guide | Role-based reading guide: what to read next based on who you are | Everyone -- read last |
1.3The MATIH Difference
Why Another Data Platform?
The modern data stack has given organizations excellent individual tools: Airflow for orchestration, dbt for transformations, Trino for federated queries, Metabase for BI, MLflow for model tracking. MATIH does not aim to replace these tools. It aims to unify the experience around them and add a conversational AI layer that makes the entire stack accessible to every user in the organization.
The three structural differentiators are:
1. Conversation as the Primary Interface
Most platforms add a chatbot as a secondary feature on top of a traditional UI. MATIH inverts this: the conversational AI interface is the primary interaction model. Every capability in the platform -- from data ingestion to model deployment -- is designed to be accessible through natural language. The workbench UIs exist as power-user tools for fine-grained control, but the conversational path is always the default.
2. Self-Hosted and Cloud-Agnostic
MATIH runs on any Kubernetes cluster, on any cloud provider, or on bare metal. The platform uses no proprietary cloud services in its core architecture. Organizations maintain full control over their data and infrastructure. Cloud-specific integrations (Azure Blob Storage, AWS S3, GCP Cloud Storage) are implemented as pluggable adapters behind clean interfaces.
3. True Multi-Tenancy at Every Layer
Multi-tenancy is not an afterthought. It is woven into every service, every database schema, every API endpoint, and every infrastructure component. Each tenant gets its own Kubernetes namespace, database schemas, DNS zone, TLS certificate, resource quotas, and NGINX ingress controller.
1.4How This Chapter Connects
This introductory chapter establishes the foundation for everything that follows:
- Chapter 2: Architecture builds directly on the design principles and separation of concerns introduced here, expanding them into detailed service interaction diagrams, data flow patterns, and deployment topologies
- Chapter 3: Security implements the multi-tenancy and access control concepts described in the capabilities section, with JWT token structures, RBAC models, and network policies
- Chapters 4-5: Installation and Quick Start deploy the technology stack documented here, using the Helm charts and Terraform modules described in the technology stack section
- Chapters 6-14: Service Deep Dives provide implementation-level detail for each service referenced in the capabilities and technology sections
- Chapters 15-20: Frontend, Kubernetes, CI/CD, and Observability cover the operational aspects previewed in this chapter
1.5Prerequisites
No prior knowledge of the MATIH Platform is required for this chapter. The content is written to be accessible to anyone with a general understanding of software systems. The following background will enhance your understanding but is not strictly necessary:
| Topic | Why It Helps | Where It Matters |
|---|---|---|
| Cloud computing basics | Understanding deployment models and infrastructure automation | Technology Stack, Architecture Preview |
| SQL fundamentals | Appreciating the text-to-SQL capability | Conversational Analytics, Data Engineering |
| Kubernetes concepts | Understanding the deployment and isolation model | Multi-Tenancy, Observability |
| Machine learning workflow | Following the ML lifecycle capabilities | ML Platform, ML Engineer persona |
| Microservices architecture | Understanding the service decomposition | All sections |
1.6Reading This Documentation
Conventions
Throughout this documentation, the following conventions are used:
| Convention | Meaning |
|---|---|
monospace text | Code, commands, file paths, configuration keys, and service names |
| Bold text | Terms being defined, emphasis, or important notes |
| Italic text | First use of a term, or emphasis in narrative context |
service-name (port) | A MATIH service with its default port number |
{tenant_id} | A placeholder for a tenant-specific value |
| Tables with "Planned" markers | Features that are designed but not yet implemented in the current release |
Component Indicators
Each page in this documentation includes an Implementation Status indicator:
Cross-References
Links to other sections of this documentation follow the pattern [Chapter Title](/chapter-path). External links are clearly marked. Code references point to actual files in the MATIH codebase.
Let us begin with the Vision and Mission that drives the MATIH Platform.