Data management
made effortless
Enjoy the freedom to do more with Maia on your side.
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.
Maia removes the data engineering bottleneck in your AI roadmap.
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.
When production issues arise, it analyzes context and surfaces remediation paths in real time.
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.
Instead of brute-forcing ambiguous schemas, Maia understands your organization’s data estate to build efficiently.
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.
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.
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 modernization at scale
Decomposed complex legacy workflows into governed, reusable pipelines. Validation and documentation auto-generated.
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.
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.
REGULATED ANALYTICS PLATFORM
Production incident first response
Detected schema drift, traced lineage, and 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.
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.
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.
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.
Business users move from requesters to contributors.
Maia is the operating system for delivering data at AI scale.
Our latest resources
September 21, 2026
•
Reports
AI or Obsolete
The gap between CDOs moving now and those still waiting is compounding daily. The constraint on AI value was never the models; it's how fast you can supply them with governed, AI-ready data. This executive guide shows how the CDOs already ahead get there: a proven pilot, a funded business case, and a board-ready plan to scale, in 90 days.
September 23, 2026
•
Blog Post
Why 80% of AI POCs Never Reach Production
80% of AI POCs stall before production — not from bad use cases, but an unresourced supply side. Here's the five-stage leakage chain, and how to close it before your board notices.
Changelog
Data management
