Matillion

What Matillion does

Maia is the industry’s first AI Data Automation platform designed for building data products without limits. It handles the entire data lifecycle, enabling teams to deliver trusted data products at scale. The platform consists of tightly integrated components: Maia Team, Maia Context Engine, and Maia Foundation. Maia Team provides expert AI agents for repetitive data work like building, modifying, optimizing, and maintaining pipelines. Maia Context Engine captures business rules, standards, and institutional knowledge to ensure transparent, reusable, and deterministic data products. Maia Foundation offers an enterprise backbone with built-in security, governance, observability, and scalability, ensuring compliance and supporting both visual and pro-code workflows. Maia aims to remove data engineering bottlenecks in AI roadmaps by automating execution within enterprise governance.

Products

• Maia TeamAn autonomous workforce of expert AI agents that plan, build, refactor, optimize, and troubleshoot data pipelines within a governed environment. It translates business requirements into production systems, converts legacy workflows into cloud-native ones, orchestrates cross-system integrations, and generates documentation automatically. It also analyzes context and surfaces remediation paths for production issues in real time.
• Maia Context EngineGrounds every action in institutional knowledge by autonomously mapping business entities, semantic definitions, modeling conventions, compliance rules, and metadata relationships into a living knowledge graph. This allows Maia to understand an organization’s data estate for efficient building.
• Maia FoundationThe enterprise backbone providing 150+ system connectors, cloud-native pushdown execution, modular transformation and orchestration, and fully integrated DataOps workflows. It includes version control, environment promotion, CI/CD pipelines, audit trails, and role-based access control, supporting both visual development and pro-code workflows.
• Developer PlanA plan for individuals to start building pipelines with essential features, ideal for light workloads and early exploration. Includes 1 developer user, unlimited projects, pre-built connectors, low-code canvas + SQL/Python, and a built-in Git repository.
• Teams PlanA plan for scaling businesses to accelerate data projects with collaborative features and expanded capacity. Includes all Developer features, 5 developer users, audit log, standard customer support, and a Service Level Agreement.
• Scale PlanThe complete platform for large organizations with mission-critical data operations. Includes all Teams features, advanced security (custom SSO), hybrid cloud deployment, data lineage tracking, streaming change data capture, extended log retention (180+ days), and bring-your-own Git integration. Premium Support is available.

Key capabilities

• AI Data Automation
• Agentic AI
• Autonomous Data Engineering
• Conversational Interface
• Auto Documentation
• Data Quality
• Data Modeling
• A2A and MCP (Agent2Agent workflows using MCP protocol integration)
• Legacy Workload Conversion
• Mission Control (centralized control plane for AI automation)
• Query metadata to discover pipeline assets, lineage, and dependencies
• Continuous Crawlers (automatically discover metadata and standards)
• Business Logic Capture
• Lineage Mapping
• Quality Review (assess pipeline designs against engineering standards)
• 150+ system connectors
• Custom REST API connectors
• Ingest (generate REST API connectors and extraction pipelines)
• Transform (create SQL/Python transformation pipelines)
• Orchestrate (automate multi-pipeline workflows)
• Pushdown architecture
• Operational visibility
• Git-based DataOps (versioning, branching, CI/CD)
• Role-based access control
• Consumption-based credit system
• Unlimited projects (Developer plan)
• Low-code canvas + SQL/Python
• Built-in Git repository
• Audit log (Teams plan)
• Standard customer support (Teams plan)
• Service Level Agreement (Teams plan)
• Advanced security (custom SSO) (Scale plan)
• Hybrid cloud deployment (Scale plan)
• Data lineage tracking (Scale plan)
• Streaming change data capture (Scale plan)
• Extended log retention (180+ days) (Scale plan)
• Bring-your-own Git integration (Scale plan)
• Unlimited admin users
• Custom connectors
• Real-time validation and sampling components
• PrivateLink (available as add-on)
• SOC1 Type II accreditation
• SOC2 Type II accreditation
• SOC3 accredited
• ISO 27001 certification
• GDPR compliance
• End-to-End Encryption (AES-256 for data at rest, TLS 1.2 and 1.3 for data in transit)
• Secure Internal Processes
• Migration Agent (for converting legacy ETL workflows)

Use cases

• Building data products at scale
• Automating the entire data lifecycle
• Handling repetitive, time-consuming data work (building, modifying, optimizing, maintaining pipelines)
• Freeing data teams to focus on higher-value outcomes
• Capturing business rules, standards, and institutional knowledge
• Ensuring data products remain transparent, reusable, and deterministic
• Providing enterprise backbone with security, governance, observability, and scale
• Complying with data regulations
• Translating business requirements into fully constructed data pipelines
• Converting legacy workflows (code or legacy ETL platforms) into governed, cloud-native workflows
• Orchestrating cross-system integrations
• Generating documentation automatically
• Analyzing context and surfacing remediation paths for production issues in real time
• Automating jobs for data analysts, data engineers, QA, dataops, and operations
• Building or fixing pipelines using plain language (conversational interface)
• Generating pipeline documentation and Git commit messages
• Building validation rules and data quality checks into pipelines
• Generating dimensional models and schema designs from requirements
• Orchestrating Agent2Agent workflows
• Autonomously migrating legacy ETL into Maia pipelines
• Governing and supervising AI automation through a centralized control plane
• Querying metadata to discover pipeline assets, lineage, and dependencies
• Automatically discovering metadata and standards from across the data estate
• Centralizing institutional knowledge and transformation rules
• Generating a comprehensive map of data flows and relationships
• Assessing pipeline designs against engineering standards and policies
• Deploying prebuilt connectors or generating custom REST API connectors
• Generating REST API connectors and extraction pipelines from any source
• Creating SQL/Python transformation pipelines for cleansing and enrichment
• Automating multi-pipeline workflows with sequencing and dependencies
• Executing transformations in the warehouse natively (pushdown architecture)
• Tracking pipeline execution, performance metrics, and data lineage
• Enabling Git versioning, branching, and CI/CD pipeline automation
• Configuring granular permissions for design, execution, and admin
• Legacy workflow modernization at scale
• Decomposing complex legacy workflows into governed, reusable pipelines
• Automating validation and documentation generation
• Modernizing legacy ETL platforms
• Consolidating data stack (ingestion, transformation, orchestration)
• Standardizing workflows and sharing context to save on software costs
• Migrating legacy ETL workflows into modern cloud-native pipelines
• Operating within one unified platform for all data operations
• Augmenting data engineers with autonomous AI for pipeline design, build, and operation
• Increasing data team output without increasing headcount or outsourcing
• Automating routine pipeline builds, migrations, and optimizations
• Accelerating delivery velocity while maintaining quality
• Retiring legacy ETL estates and reducing cybersecurity liabilities
• Parsing and analyzing opaque legacy pipelines
• Flipping the reactive-to-proactive work ratio for data teams
• Building foundational master tables for customer, product, order, invoice, and shop-order data
• Transforming ERP source data into governed Snowflake tables
• Certifying governed data and getting organizations AI-ready
• Managing production pipelines across global Snowflake environments
• Powering revenue forecasting, HR operations, and business analytics
• Automating pipeline delivery across the full development lifecycle
• Modernizing pipelines with standards compliance and high first-time deployment success rates
• Predicting site accidents and protecting project margins in construction
• Building Tableau Dashboards Programmatically

AI approach

Maia is an AI Data Automation platform that uses expert AI agents to handle data work, build and maintain pipelines, and manage data lifecycle. It features a Context Engine for institutional knowledge and a Foundation for enterprise backbone, security, governance, and scalability. The platform uses a modular, multi-agent architecture with specialized agents for data engineering, DataOps, and analytics. It emphasizes autonomous execution within enterprise governance frameworks, with human oversight for approval.

Tech named: AI Data Automation, Agentic AI, AI agents, Maia Team, Maia Context Engine, Maia Foundation, conversational interface, multi-agent architecture, Data Engineer Agent, DataOps Agent, Analytical Agent, Migration Agent, MCP protocol integration

Industries served

• Software Development
• Clinical Research
• Life Sciences
• Manufacturing
• Cybersecurity
• Construction

What it says sets it apart

• First AI Data Automation platform for building data products without limits
• Autonomous data engineering platform operating inside enterprise governance
• Removes data engineering bottleneck in AI roadmap
• Unified platform built on three integrated layers (Maia Team, Context Engine, Foundation)
• Maia Team is a modular, multi-agent system, not a single assistant or copilot
• Maia Context Engine grounds actions in institutional knowledge and living knowledge graph
• Maia Foundation provides battle-tested enterprise infrastructure with modern DataOps principles
• Automates execution while operating inside enterprise governance
• Converts legacy ETL workflows into modern cloud-native pipelines autonomously
• Consolidates ingestion, transformation, and orchestration within a single governed platform
• Unified governance with centralized visibility, role-based access controls, data residency enforcement, and SOC 2 compliance
• Agentic AI operates at enterprise scale, accessing governed metadata, execution logs, and business rules
• Consumption-based credit system for flexible and transparent pricing
• AI features are not trained using customer data; opt-in required for AI functions
• Pipeline execution happens entirely inside the customer's own cloud environment (e.g., Snowflake Native App), ensuring data privacy and security
• Augments data engineers with autonomous AI that designs, builds, and operates production pipelines at machine speed
• Reasons through requirements, architects solutions, generates production-ready pipelines, writes documentation, and monitors operations
• Operates under customer's governance framework, encoding standards into every build
• Multi-agent reasoning to decompose complex requests, evaluate solution paths, and execute complete workflows
• Encodes data engineering best practices into executable logic, applying design patterns for idempotency, error handling, incremental loading, and performance optimization
• Human oversight remains mandatory; Maia augments, never replaces
• Enterprise-grade governance built in: role-based access control, encryption at rest and in transit, data residency controls, audit trails
• SOC1 Type II, SOC2 Type II, SOC3 accredited, and ISO 27001 certified
• Complies with GDPR and DPA2018