What is the Maia platform?

Maia is an AI Data Automation platform that turns data requests into production-ready pipelines. It pairs autonomous AI agents with a continuously updated knowledge layer and a cloud-native runtime to design, build, run and maintain data pipelines under human governance.

What are Maia Team, Context Engine and Maia Foundation?

Maia Team is the agentic AI workforce — expert AI agents that build and maintain data pipelines. Context Engine is the knowledge layer that gives those agents an accurate, continuously updated understanding of the customer's data and business logic. Maia Foundation is the runtime, connectivity and orchestration layer underneath, covering connectors, transformations, pushdown execution, DataOps and access control.

What is pushdown architecture?

Pushdown architecture means data transformations execute inside the customer's own cloud data warehouse rather than on Maia infrastructure. Customer data never has to leave the customer environment for processing, which reduces the security surface area and uses the customer's existing compute.

How many connectors does Maia support?

Maia includes more than 130 pre-built connectors covering databases, applications, files, event streams and unstructured sources, plus custom REST API connectors when a pre-built one isn't available.

MAIA PLATFORM

AI Data Automation starts here

Maia is the first autonomous data engineering platform, automating execution while operating inside enterprise governance.

Maia removes the data engineering bottleneck in your AI roadmap.

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Trusted by the world’s most ambitious data teams

How Maia works

Maia operates as a unified platform built on three integrated layers

Maia Team

Maia Team functions as an always-on digital workforce. It translates business requirements into fully constructed data pipelines; converts legacy workflows (code or legacy ETL platforms) into governed, cloud-native workflows; orchestrates cross-system integrations; and generates documentation automatically.

When production issues arise, it analyzes context and surfaces remediation paths in real time.

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Autonomous Data Engineering

Automate jobs for data analysts, data engineers, QA, dataops, and operations.

Flowchart of a CLV pipeline process with steps like data validation, transformation, error handling, and notifications.

Conversational Interface

Ask Maia to build or fix pipelines in plain language.

Maia interface showing a one-hot encoding pipeline with steps on bronze data, encoding status, and output transactions.

Auto Documentation

Generate pipeline documentation and Git commit messages.

Data Quality

Build validation rules and data quality checks into pipelines.

Additional features

Data Modeling

Generate dimensional models and schema designs from requirements.

A2A and MCP

Orchestrate Agent2Agent workflows using MCP protocol integration.

Legacy Workload Conversion

Autonomously migrate legacy ETL into Maia pipelines.

Mission Control

Govern and supervise AI automation through a centralized control plane.

Maia Context Engine

Maia Context Engine grounds every action in institutional knowledge - including the tribal knowledge that rarely makes it into documentation. It autonomously maps business entities, semantic definitions, modeling conventions, compliance rules, and metadata relationships into a living knowledge graph that the agents rely on when making decisions.

Instead of brute-forcing ambiguous schemas, Maia understands your organization’s data estate to build efficiently.

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Maia Context Engine

Query metadata to discover pipeline assets, lineage, and dependencies.

Knowledge Graph Explorer shows nodes Customers, Opportunity, Campaign, and Lead with arrows indicating relationships.

Additional features

Continuous Crawlers

Automatically discover metadata and standards from across your data estate.

Business Logic Capture

Centralize institutional knowledge and transformation rules.

Lineage Mapping

Generate a comprehensive map of data flows and relationships.

Quality Review

Assess pipeline designs against engineering standards and policies.

Maia Foundation

Underneath autonomy is battle-tested enterprise infrastructure, built around modern DataOps principles.

Maia Foundation delivers 150+ system connectors, cloud-native pushdown execution, modular transformation and orchestration, and fully integrated DataOps workflows. Version control, environment promotion, CI/CD pipelines, audit trails, and role-based access control are embedded directly into execution, not layered on after the fact.

It supports visual development and pro-code workflows side by side, allowing teams to operate with both speed and control.

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Connectors

Deploy 130+ prebuilt connectors or generate custom REST API connectors.

Ingest

Generate REST API connectors and extraction pipelines from any source.

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Transform

Create SQL/Python transformation pipelines for cleansing and enrichment.

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Orchestrate

Automate multi-pipeline workflows with sequencing and dependencies.

Dashboard showing an orchestration pipeline that runs only on Wednesdays with steps and descriptions.

Additional features

Pushdown architecture

Execute transformations in your warehouse natively.

Operational visibility

Track pipeline execution, performance metrics, and data lineage.

Git-based DataOps

Enable Git versioning, branching, and CI/CD pipeline automation.

Role-based access control

Configure granular permissions for design, execution, and admin.

Autonomous execution, proven in production

Real production outcomes, from teams scaling output without scaling headcount

CLINICAL RESEARCH  DATA ENVIRONMENT

Legacy workflow modernization at scale

Decomposed complex legacy workflows into governed, reusable pipelines. Validation and documentation auto-generated.

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ENTERPRISE LIFE SCIENCES

Lauching net-new AI data products 

Autonomously built new production-grade feature pipelines for AI use cases. Integrated ingestion, transformation, orchestration, and validation automatically.

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GLOBAL PHARMACEUTICAL ORGANIZATION

Project-wide refactoring

Updated legacy reporting fields across dependent production pipelines. Maia traced data dependencies, applied schema updates, and regenerated validation tests.

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REGULATED ANALYTICS PLATFORM

Production incident first response

Detected schema drift, traced lineage, and proposed validated remediation before commit. Maintained compliance and audit integrity.

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Why enterprise data leaders choose Maia

A structural shift in how data teams scale

Multiply engineering leverage 

Up to 90% reduction in manual engineering effort.   

Teams deliver more without increasing operational load.

Modernize legacy workflows autonomously

Legacy workflows converted into governed, cloud-native pipelines.  

Technical debt shrinks while delivery continues.

Deliver AI without a backlog 

Pipelines built automatically. Data quality enforced continuously.  

AI initiatives move forward without a queue.

Expand who can build 

Natural language interfaces lower the barrier to data engineering.   

Business users move from requesters to contributors.

Governed by design

Matillion is committed to the security of your data with ongoing risk assessment, centralized asset management inventory, and cryptography and key management. Security features include Single Sign-On (SSO), Multi-Factor Authentication (MFA), and Role-Based Access Control (RBAC).

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