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 Agents function as an always-on digital workforce.  They translate business requirements into fully constructed  data pipelines, convert legacy workflows (code or legacy ETL platforms) into governed, cloud-native workflows, orchestrate cross-system integrations, and generate documentation automatically.

When production issues arise, they analyze context and surface remediation paths in real time.

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Autonomous Data Engineering

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

Conversational Interface

Ask Maia to build or fix pipelines in plain language.

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.

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.

Transform

Create SQL/Python transformation pipelines for cleansing and enrichment.

Orchestrate

Automate multi-pipeline workflows with sequencing and dependencies.

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 modernisation at scale

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

ENTERPRISE LIFE SCIENCES

Lauching net-newAI data products 

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

GLOBAL PHARMACEUTICAL ORGANIZATION

Project-wide  refactoring

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

REGULATED ANALYTICS PLATFORM

Production incident first responce

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

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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