Maia is the AI Data Automation platform by Matillion. It autonomously creates, manages and evolves data products and pipelines for humans and AI agents at scale.

What is AI Data Automation?

AI Data Automation is a category of platform where AI agents take on the work of building, running and maintaining data pipelines. Instead of teams hand-coding each pipeline, AI agents do the work end-to-end under human governance.

How is Maia different from traditional ETL tools like Informatica or Alteryx?

Traditional ETL tools require manual pipeline development and ongoing maintenance. Maia uses AI agents to design, build and evolve pipelines automatically, and accelerates migration off legacy ETL platforms.

Which cloud data warehouses does Maia support?

Maia supports Snowflake, Databricks, Amazon Redshift and Google BigQuery, with pushdown processing inside the customer's own cloud data warehouse.

The AI Data Automation Platform

Maia removes manual data work as the limit on what your team can deliver. Autonomous AI agents build, govern, and evolve data products at scale - so your engineers focus on outcomes, and your business stops waiting on data.

See Maia in action

Trusted by the world’s most ambitious data teams

Always-on data team
‍at your fingertips

Maia Team is a set of expert AI agents that work continuously across the data lifecycle, mapped to real data team roles.

Data Quality

Shift-left data quality that validates and cleanses to keep integrations trusted and resilient

Data Engineering

Design, build and maintain data pipelines, end-to-end within a secure, governed architecture

Connectivity

Connect, extract and sync from anywhere, integrate everywhere - with APIs, secure authentication and reverse ETL

DataOps

Keep data flowing: pinpoint failures, automate debugging, RCA and remediation to own your SLAs

FinOps

Cut costs, spot bottlenecks: performance tuning and optimization to keep data pipelines under budget

Migration

Quick, automated migration from legacy ETL/ELT to transparent, testable, cloud-ready data pipelines

Automate manual
data work

AI demand is rising fast, but manual data work slows teams down-Maia automates the work so teams can shift from maintenance to building what matters and turning more ideas into business impact.

Without Maia

Hard-coded pipelines and brittle models

Manual backfills, fixes, and documentation

Weeks to deliver new datasets

Drowning in ad-hoc requests

No time for strategic work

Fragmented tech stack

With

Delegate pipeline creation and changes via prompt

Maintain documentation automatically

Embed agentic data engineers directly with analytics teams

Scale pipelines and teams without adding headcount

Fulfill data requests in hours, not weeks

Migrate and modernize legacy systems with AI

Abstract yellow background with rounded geometric shapes and soft gradients.

“Maia offers a glimpse into the future of data engineering. It’s intuitive, powerful, and feels like a real accelerant for how teams build with data.”

Smiling middle-aged man with gray hair and glasses wearing a navy blue polo shirt.

Sridhar Ramaswamy

CEO at Snowflake

Enable every data use case

Code conversion

Screenshot of Maia app showing a step-by-step pipeline creation interface on the left and a visual workflow diagram on the right that handles SCD types with components like NORM_EVENT_REF, RANK, LATEST, SCD Types, joins, and dimension events.

Migrate and optimize legacy code

SQL, SparkSQL, dbt and Python optimization and conversion

Data quality and data ops

User interface showing a data pipeline with boxes labeled Day Spine to Day Dimension and Month Spine to Month Dimension, and a commit changes panel on the right with branch 'void', modified changes for Data Spine plus Data Dimension and Kafka, and a commit message about updating date range and poll timeout.

Data cleaning and preparation

Git based data ops

CICD pipeline that includes auditability, auto-documentation and lineage

Legacy ETL migration

User interface of Maia showing a converted pipeline with components: Sample Data, Filter Audits, Rename Columns, and detailed conversion summary on the left.

Convert from Alteryx, Informatica, Talend and Qlik to the cloud

Reduce costs and modernize for AI

Data engineering and analysis

Screenshot of Maia analytics interface showing a healthcare analytics pipeline validation with components Encounter Fact, Patient Dim, Date Dim joined in Join Star Schema leading to Monthly Aggregate, alongside chatbot text explaining successful pipeline validation and metrics.

ETL/ELT development

Citizen data engineering for data analysts

Pipeline optimization for existing processes

AI-native foundation  
powering Maia

Maia Foundation provides enterprise data tools for any workload with built-in security, elastic scale, and centralized control.

Learn more

Data professionals love Maia

Maia automates manual data work for teams at global enterprises and fast-growing startups alike.

“Maia is helping us become an AI-ready organization by transforming how we build pipelines. In some cases we’ve seen pipeline build time go from 2 days to 10 minutes.”

John Tentomas

CEO Nature’s Touch

Hand holding a wooden crate filled with freshly picked blueberries and green leaves.

Roberto Lara

VP of Digital Transformation & Analytics

Precision Medicine Group

“The biggest impact of Maia is our data engineers embracing it and helping us work smarter, not harder.”

Ammad Baig

Director of Enterprise Data & AI Services

Precision Medicine Group

"Maia is like having an autonomous data engineering team in digital form. It handles everything from legacy ETL migrations to building complex, production-ready pipelines at machine speed, and the quality of the logic is something we can trust. It’s dramatically accelerating our workflow while reducing the manual overhead."

Global Head of Data Transformation

"With Maia, our teams no longer wait in queues for data. Business users are self-serving insights securely, and our engineering team is now focused on strategy, not support. It’s the first real GenAI platform that understands our data reality."

Chief Data Officer

Insurance Company

This is a reimagining of data engineering and ETL - we’re rethinking what’s possible. With Maia, our analysts can build and debug complex pipelines using natural language, whether in the UI or directly in SQL and Python. It’s incredible to see non-engineers creating production-ready workflows without relying on our dev team.

“Maia replaced thousands of hours of manual work, helped us de-risk audits, and let business teams generate insights in days instead of months. It’s the first AI investment that delivered value fast.”

Group CFO

Global Financial Services Firm

"Maia feels like having a team of junior data engineers who never sleep. We’re a small team, but with Maia we’re punching well above our weight."

Dan Adams

Global Analytics Manager @ Edmund Optics