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.

Your agentic enterprise data team

Decouple data engineering output from headcount. Maia is a fully functional data team that delivers trusted data products at scale.

Book a demo

Explore Platform

Trusted by 1,000+ data teams

The first data engineering team that
scales with demand, not headcount.

Think bigger

The platform beneath Maia

An agentic team and the enterprise platform it runs on

A coding assistant makes one engineer faster. An agentic data team needs more — the control layer to direct it, the knowledge to work your way and the tools to do the work.

Mission Control

Maia works through your data tasks autonomously — you step in with direction or approval where judgement matters.

Context Engine

Maia builds a living map of your data  — so it understands your business and  builds to your standards.

Foundation

A secure, high-performance execution platform with governance and lineage built in — every workload pushed down into your warehouse.

AI Data Automation Platform

Data sources

Structured

Databases, ERP, CRM, applications

Semi Structured

JSON, XML, logs,  events, APIs

Unstructured

Files, documents,  images, text

150+ connectors

Pre-built and custom  REST/API connectors

Maia Diagram — Left Connector (Column 2)

Maia team

Mission Control

Prioritize work

Run tasks in parallel

Monitor progress

Approve decisions

Auto-start  from backlog

Manage exceptions

Context Engine

Data intelligence

Business context

Governance guardrails

Maia's knowledge layer

grounded in your data, your business, and your standards.

Maia Foundation — Execution Platform

Maia Diagram — Right Connector (Column 4)

TRUSTED DATA PRODUCTS

Reliable, governed and  production-ready data  products

POWERS EVERY DOWNSTREAM USE

BI / Analytics

Dashboards, reports  and KPIs

Data Science

Explore, experiment  and model

AI Apps

GenAI, copilots and  intelligent apps

Operational Integration

Sync data back to  business systems

Learn more about the Maia Platform

Customer outcomes

Proven in production

What Maia unlocks

A different operating model for data engineering

Every engineer leads an AI team

One engineer directs a team of agents — and ships what used to take a whole team.

Maintenance doesn’t eat into delivery

Maia monitors, diagnoses, and proposes the fix — so your team keeps building.

Knowledge stays when people leave

Your standards live in Maia, not just in people's heads.

CONTAINS

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Message from our founder

See Maia work

One AI team. Every stage of delivery.

Maia teammates plan, build, test, run and fix data products while humans provide approval where judgement matters.

Plan

From request to a plan you approve

Maia turns a business request into a reviewable, step-by-step plan. Nothing changes until a human approves.

▤ Files ▾ O create-star-schema N

MaiaSelect Knowledge Layer ▾ ⊕ Edit context ✎ ↺ ⋮

Maia

Your team of expert AI agents

◍

Help me build a pipeline

Create a new data pipeline from scratch

◍

Summarize my pipeline

Understand your existing pipeline

▦

Set up a skill for me

Adapt Maia's responses to your workflow

Update this pipeline to take into account slowly changing dimensions (type 2 and 3) on core customer attributes

› Thought

Switch to plan mode

✓Maia wants to switch to plan mode

Implementing SCD Type 2 and 3 on customer dimensions requires understanding the current pipeline structure, identifying customer-related components, and determining the best approach for slowly changing dimensions.

› Performed 3 actions

Now I have a clear picture. The pipeline creates a star schema with SQL executors, and DIM_CST_PROF (the customer dimension) needs SCD Type 2 and Type 3 support on core attributes. Let me propose a plan:

✓Maia has a plan for your review

Add SCD Type 2 & 3 to the customer dimension

1Add 5 SCD columns to DIM_CST_PROF
2Type 2 (full history): seg_cd, ltv_idx, sts_flg
3Type 3 (keep previous): eml_ref, ph_hsh
4Seed one historical row (segment change)
5No change to FCT_TXN_LGR

Applying changes to DIM_CST_PROF…

✓Done — DIM_CST_PROF restructured for SCD Type 2 & 3. Review the diff before you commit.

▦ Skills (1) ▾

Ask Maia, use / for commands, or @ to explore more

@ Plan Mode

(x)

⊕

Review ▾ Validate Schedule Run

Start

Create Schema

✓

DIM_CST_PROF

SCD 2 & 3 · +5 cols

DIM_PRD_CTL

DIM_GEO_LOC

DIM_TM_PRD

Wait For Dims

FCT_TXN_LGR

Build

Pipelines built to your standards

Maia builds the pipeline to your own standards — naming, layers and conventions applied automatically.

▤ Files ▾ T Sales Performance Metrics.tran N

MaiaSelect Knowledge Layer ▾ ⊕ Edit context ✎ ↺ ⋮

Maia

Your team of expert AI agents

◍

Help me build a pipeline

Create a new data pipeline from scratch

◍

Summarize my pipeline

Understand your existing pipeline

▦

Set up a skill for me

Adapt Maia's responses to your workflow

build a CRM pipeline

› Thought

?Maia needs your input

What would you like to build from your CRM data?

✓Sales performance metrics — win rates, deal velocity, revenue by rep

✓Customer segmentation — group customers by value, industry, behavior

✓Pipeline analytics — stage conversion, forecasting

✓Allow Maia to query the warehouse (read-only)

Discover available CRM tables

SELECT TABLE_NAME FROM "JOHN_SMITH"."INFORMATION_SCHEMA".TABLES

› Performed 4 actions

I queried your warehouse and found ACCOUNT, OPPORTUNITY and CONTACT in Snowflake, alongside your Salesforce and Customer 360 data. No existing pipeline matches — I'll build the transformations from scratch.

Building from your context, not a blank slate. I'm applying your GWF standards — the CRM_ table prefix, medallion layer suffixes, and .tran.yaml naming.

Building Sales Performance Metrics.tran — placing components…

✓Built Sales Performance Metrics.tran — 7 components, writing to CRM_SALES_METRICS. Review before you run.

▦ Skills (0) ▾

Ask Maia, use / for commands, or @ to explore more

@ Plan Mode

(x)

⊕

Review ▾ Validate Schedule Run

Load Opportunities

Load Accounts

Join Opps to Accounts

Calculate Deal Velocity

Sales Metrics by Owner

Calculate Win Rate

Write Sales Metrics

→ CRM_SALES_METRICS

Test

Proven correct before it ships

Maia writes real tests, builds the branching test pipeline, and runs it against your assertions.

▤ Files ▾ T failed_payment_rate failed_payment_rate.test N

MaiaSelect Knowledge Layer ▾⊕ Add context

Maia

Your team of expert AI agents

◍

Help me build a pipeline

Create a new data pipeline from scratch

◍

Summarize my pipeline

Understand your existing pipeline

▦

Set up a skill for me

Adapt Maia's responses to your workflow

Please create tests for this pipeline

Performed 5 actions

No tests exist yet for this pipeline. I'd like to clarify a few things before creating one:

Maia needs your input1/2

How would you like to set up test data for the PAYMENTS table?

Mock data (Recommended) — create a small set of test rows with known values

Clone existing data — zero-copy clone PAYMENTS into the test schema

Other

Maia needs your input2/2

What should the test verify about the output?

Row count — assert expected number of rows

Column names and types — assert the output schema

Data values — assert specific output values

Performed 6 actions

I've created a separate test pipeline — failed_payment_rate.test — so your original pipeline stays untouched. It spins up an isolated schema, generates mock payments, runs the pipeline, asserts the results, then tears the schema down.

Running test in an isolated schema…

✓All assertions passed — 3 rows (2 processors × 2 dates), schema matched, failure rates correct. Test green.

▦ Skills (0) ▾

Ask Maia, use / for commands, or @ to explore more

@ Plan Mode

Review ▾Validate ⊘Schedule ◷ Run ▶

Failed Payment Rate

Calculates the daily failure rate per payment processor. Groups payments by date and processor, counts total vs failed, then writes results to payment_failure_summary.

Failed Payment Rate Test

Creates mock PAYMENTS data with known failure rates, runs the pipeline in a temporary schema, then asserts: 3 output rows, correct failure rates, expected column schema.

Read Payments

Flag Failed

Aggregate by Date & Processor

Calculate Failure Rate

Rename Columns

Write summary

Start

Create Test Schema

Create Mock Payments

Run Pipeline

✓

Assert Row Count

✓

Assert Output Values

Join Assertions

Drop Test Schema

Run

One pane of glass across the estate

Operate every pipeline in production from a single view. Drill into any run and Maia tells you exactly what happened.

Your activity

Saved filters▾

Full history▾

Maia

Matillion ETL

Pipeline

Project ▽

Status ▽

Environment ▽

Artifact version

Triggered by

Started at

Duration

Feature_Store_Setup

Fraud_Detection

Demo

Success

snowflake_summit_2026_spin_workshop_schema_162

089b5083-ba82-4807-ae59-3cc56cb12076

Jordan Blake

16:07:06

2026-06-23

~7s

run_customer_segmentation

spin/finance/segmentation

Demo

Success

host

8a6bf959-6f9d-4e1c-b702-d7ee7ee3f94c

Alex Rivera

15:32:52

2026-06-12

~42s

run_customer_segmentation

spin/finance/segmentation

Demo

Success

host

07260228-e0a6-4b74-8201-eb3ad5ec3f5c

Alex Rivera

15:30:22

2026-06-12

~44s

run_customer_segmentation

spin/finance/segmentation

Demo

Failed

host

bc438bf8-13f0-424f-a971-d97501cbbb1a

Alex Rivera

15:29:47

2026-06-12

~13s

run_customer_segmentation

spin/finance/segmentation

Demo

Failed

host

db67a137-92d6-4537-989f-55b8fcd1dca9

Alex Rivera

15:28:58

2026-06-12

~19s

run_customer_segmentation

spin/finance/segmentation

Demo

Failed

host

70eeb588-3977-476a-adcf-c6e15cc69b3c

Alex Rivera

15:28:30

2026-06-12

~17s

run_customer_segmentation

spin/finance/segmentation

Demo

Success

host

957add18-7e1f-4da2-8e7c-5553a45bf678

Alex Rivera

15:24:19

2026-06-12

~16s

Back to pipeline run history

Pipeline run details Failed

spin/finance/segmentation/run_customer_segmentation.orch.yaml

Troubleshoot with Maia. Review root causes and see recommended fixes.

Summary of issue(s)

The pipeline is failing because a target column SEGMENT_CD is expected but not present in the data being processed. This error occurs during query execution with variables in Snowflake. [MLUserError] Target column SEGMENT_CD is not present in the data. Issues in detail — Issue 1: Missing target column SEGMENT_CD …

Project

Demo

Started

2026-06-12 at 15:29:47

Completed

2026-06-12 at 15:30:00

Source

Designer

Environment

host

Artifact version

bc438bf8-13f0-424f-a971-d97501cbbb1a

Duration

~13s

Show more Copy details

Steps with errors

Pipeline

Component

Started

Duration

Row count

Message

Root cause analysis

spin/finance/segmentation/run_customer_segmentation.orch.yaml

Here's what was found in the pipeline.

Summary of issue(s)

The pipeline is failing because a target column SEGMENT_CD is expected but not present in the data being processed. This error occurs during query execution with variables in Snowflake.

[MLUserError] Target column `SEGMENT_CD` is not present in the data.

Issues in detail

Issue 1: Missing target column SEGMENT_CD in data

Category: Data Fixable in pipeline: Yes — update the pipeline to either provide the missing column or adjust the query/transformation logic.

Fix

From alert to a reviewable fix

When an incident fires, Maia traces the root cause, proposes the exact diff, and waits for your approval.

MMatillion

Threads

Direct messages

Channels

# general

# engineering

# data-eng-alerts 1

# data-platform

# product

# releases

# data-eng-alertsMaia pipeline health · production · automated alerts only

Wednesday, 7 May

Maia APP 7:22 AM

🔴 Schema drift — campaign_lead_enrichment

Column renamed in the Salesforce source broke the pipeline reference — run failed. Downstream pipelines on hold. Fix task created in Mission Control.

Failed component

extract_crm_contacts

Error

Column 'contact_email' not found in source

Classification

Schema drift — column renamed

Maia confidence

High — deterministic schema diff

Mission Control

Monitor, review, and act on tasks

All projects ▾Last 7 days ▾⌕ Search tasks…

Backlog 3

Add row count assertion — stg_lead_enrichment

maia/dq-rowcount-e4a1b3f2d1

Document pipeline logic — nightly_customer_sync

maia/docs-pipeline-c2b8a9e4c7

Implement safe rerun checkpoint — campaign_lead_enrichment

maia/checkpoint-f7d3d1a8f2

In progress 0

Needs attention 1

Fix schema drift — campaign_lead_enrichment

maia/schema-fix-20260507c5e9b3

Completed 3

Fix schema drift — campaign_lead_enrichment

maia/schema-fix-20260507

completed just now

Resolve null handling — src_crm_contacts

maia/null-fix-a3c1

e2b4d9 · completed 2026-05-06

Add freshness alert — mart_campaign_targets

maia/freshness-b8f2

f7a1c4 · completed 2026-05-05

Update retry config — salesforce_lead_sync

maia/retry-d4e7

a3d6f1 · completed 2026-05-04

Fix schema drift — campaign_lead_enrichment

View Pipeline✕

Schema drift detected — campaign_lead_enrichment · 07:22 AM

Thinking…

Thought▸

Classification

Schema drift — column rename

Confidence

High — deterministic schema diff

Column change

contact_email → email_primary

Branch

maia/schema-fix-20260507

What Maia will change

Update column reference in extract_crm_contacts from contact_email to email_primary, then re-run the extract.

The transformation layer and all downstream logic are untouched. This is a single-line change.

Downstream pipelines on hold (3)

stg_lead_enrichment — write blocked, holding last clean extract

mart_campaign_targets — refresh blocked, depends on staging

lead_segment_refresh — scheduled 09:00, on hold

All three will release automatically once the extract completes successfully.

✓Fix approved. Extract re-running on maia/schema-fix-20260507 — 3 downstream pipelines released.

Featured enterprise stories

Trusted by  teams already changing how  data engineering gets done

“So many people talk about AI helping generate code faster. All that’s true, but it’s really the full life cycle where you start to see those gains.”

Jason Mulvin

Director of Enterprise Data, Sophos

Read Case Study

reduced hours

10-15 → 2-5

pipeline delivery, end to end

migrations

800+

pipelines in migration

success

95%

first-time deployment success

“The big productivity numbers you hear about AI can actually be real.”

Kelly Mags

Divisional Director, Data Architecture, St. James's Place

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efficiency gain

1,300%

in sentiment analysis

reduction

2/3

in ETL migration effort

reduced hours

4,000 → 16

annual analysis, done end to end

“Maia handles the hard and repetitive work quickly, allowing experienced staff to focus on more strategic issues. It enhances their role instead of taking it away.”

Mark Hume

Head of Data, Balfour Beatty

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Retired

1,300

Informatica pipelines retiring

reduced mins

4,000 → 16

to parse opaque legacy logic

strategic

20% → 80%

reactive work flipped to strategic

“Can you take a lean team like ours and have the output of a bigger team? The answer is yeah, we’ve been able to do that. That’s pretty cool.”

Jamie Tanner

Director of Corporate Data & Analytics,
National Safety Apparel

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Time reduction

2 months → 2 weeks

to build foundational master tables

Faster

Under 3 days

actual hands-on build time

Unified truth

One

source of truth, replacing silos

“The agentic framework is allowing our engineers to be more strategic and focus on domain expertise while delegating repetitive tasks.”

Ammad Baig

Director of Data & AI Services, PMG

Read Case Study

Time reduction

2 days → 30 min

to understand & document a pipeline

High-velocity

40k

daily schema variations auto-resolved

Automatable tasks

25-30%

engineering tasks targeted for automation

Our latest resources

The CDO’s Agentic Advantage

The next 12 months are decisive. While competitors deploy AI at scale, most enterprises remain stuck in human-scale data engineering – constrained by capacity, not capability.