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Data Foundations on Google Cloud
AI is only as good as the data under it. We build the BigQuery foundations, pipelines, and governance that make grounded AI reliable.
The problem
AI initiatives stall on messy, ungoverned data that no one fully trusts.
The outcome
A governed data foundation with reliable pipelines, clear lineage, and the access controls that let AI use your data safely.
What we build
- BigQuery warehouse, modeling, and analytics
- Batch and streaming pipelines with Dataflow, Pub/Sub, and Eventarc
- Hub-and-spoke data sharing via Analytics Hub and BigQuery
- Terraform-managed environments with audit events and lineage
- Access governance and sensitive-data controls
- The retrieval-ready layer that grounds AI features
Technical foundation
BigQueryDataflowPub/SubAnalytics HubCloud Storage
Typical deliverables
- Modeled, documented BigQuery foundation
- Production pipelines with monitoring
- Data governance and access model
- Readiness assessment for AI grounding
Example use cases
- Unifying operational and analytical data
- Secure cross-team or cross-org data sharing
- Preparing data for retrieval and grounded AI
Governance & security
Access is governed at the column and row level where needed, with lineage and cataloging so teams know what data is, where it came from, and who can use it.
Start here
Scope a Data Foundations on Google Cloud engagement.
Tell us what you are trying to ground in AI. We will tell you the honest path to production.