Ready to see what Maia Foundation means for your BigQuery environment?
Enjoy the freedom to do more with Maia on your side.
BigQuery runs in seconds. Your pipeline work still runs in quarters.
Maia Foundation automates the construction and governance of the pipelines that feed Google BigQuery.
Autonomous agents build and maintain the work while engineers review and approve.
Autonomous agents build and maintain the work while engineers review and approve.
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Powering data products at scale
Trusted by 1000+ of the world's best enterprises and their data teams.
SEE IT IN ACTION
The engineering work that feeds BigQuery itself
Every AI feature for BigQuery in 2026 helps engineers write SQL faster. Maia works on a different part of the problem: autonomous agents construct and maintain the pipelines, the Context Engine governs them, and execution pushes down into BigQuery.
Your engineers review the pipelines; they don't author them.
Writing pipelines was never where the backlog lived
Every AI feature on BigQuery this year helps engineers write SQL faster. Gemini Code Assist, dbt's AI features, the coding tools in your engineer's IDE: all of them speed up authorship.
None of them touch the work around the pipeline, which is where most of the delivery time actually goes. The review, the governance, the schema drift, the promotion window. Maia works on that part.
- Without MaiaWith Maia
Engineers hand-build every pipeline into BigQuery from a blank canvas
Schema drift detected, rebound, and committed for engineer review automatically- Schema changes break downstream work; manual fixes consume sprint timeSchema drift detected, rebound, and committed for engineer review automatically
- Governance retrofitted at the end of the project, if the team gets thereEngineers review and approve changes rather than authoring from scratch
- Backlog grows faster than the team can absorb itMaia operates across hundreds of pipelines simultaneously, so the backlog shrinks rather than grows
- CDAOs manage scarcity instead of delivering data products at scaleCDAOs test every hypothesis and deliver any data product the business asks for
What Maia does on Google BigQuery
Three layers, each with a single job, that together shift your team from pipeline authorship to pipeline oversight.
1
Maia Team
Autonomous agents take business intent and the structure of your source data, then produce orchestration and transformation logic that runs against BigQuery. Engineers review and approve the output rather than writing it from scratch.
2
Context Engine
Maia maintains a continuously-updated model of your schemas, lineage, and governance rules, keeping automation inside the lines your governance team has drawn without someone having to enforce it manually each sprint.
3
Maia Foundation
Transformations compile to SQL and run directly inside Google BigQuery, with no external compute and no data leaving the warehouse. The cost, performance, and security of BigQuery stay exactly as they were.
The results speak for themselves
Outcomes from Maia customers running enterprise data migrations.
2/3 reduction
in legacy ETL migration effort per pipeline
Days to hours
turnaround on individual pipeline conversions
Engineering capacity
freed and reinvested into data product delivery
Featured Customer Story
From days to hours: How St. James’s Place cut ETL migration effort by two-thirds with Maia
Platform modernization stalls when legacy ETL migrations consume the engineering capacity needed to drive it forward. For St. James's Place (SJP), one of the UK's leading wealth management businesses, manually rewriting and validating pipelines was taking
days per job. In a proof of concept with Maia, the AI Data Automation platform, SJP cut that effort by roughly two-thirds - freeing engineers to focus on higher-value work and accelerating their path to a consolidated, modern data platform.
“Maia reduced ETL migration effort by around two-thirds, taking work from days to hours… Platform consolidation will help us to reinvest in the team, to enable us to build out more on the SAP and AI roadmap of tomorrow”
KM
Kelly Maggs
Director for Data Architecture Platform and Engineering.
