GCP

BtSavvy's posture on GCP

Google Cloud is the cloud where data-gravity is most often the deciding factor: organisations with material BigQuery investment, workloads that depend on Google's data and analytics primitives, or multi-cloud architectures that use GCP for data and another cloud for compute. It is also the cloud with the deepest first-party frontier-model integration: Gemini, Vertex AI, and the wider Google AI stack are first-class on GCP in a way no third party can match.

BtSavvy's GCP posture concentrates where GCP concentrates: data-foundation work on BigQuery and Dataplex, frontier-model engineering on Vertex AI, and observability through Cloud Operations. The platform is identical to AWS and Azure; the implementation surface is GCP-specific.

Which services carry GCP

All eight FORGE services apply on GCP. Three carry GCP-specific depth most often:

Data Foundation. GCP's data primitives are the firm's deepest GCP-specific surface: BigQuery as the analytical substrate, Dataplex for data products and governance, BigLake for cross-cloud data federation, BigQuery ML for in-warehouse ML.

Build. Application engineering on GCP-native runtimes: GKE for containers, Cloud Run for serverless containers, Cloud Functions for event-driven systems, Vertex AI for the full model lifecycle.

Cloud. Cloud platform engineering across GCP organisation hierarchies, IAM, VPC Service Controls, Cloud Asset Inventory, and Security Command Center.

FORGE on GCP

Factory on GCP produces the customised builder against GCP's APIs and an accelerator library calibrated against GCP primitives: Terraform modules for organisation hierarchies, BigQuery data-product patterns, Vertex AI pipeline construction, Dataplex governance configuration.

Observability on GCP uses Cloud Operations (Cloud Monitoring, Cloud Logging, Cloud Trace) as the substrate, with retrieval-quality, guardrail-trigger, and calibration-skew signals instrumented as Cloud Monitoring metrics from the first build.

Review on GCP produces signed gate logs against GCP-specific gates: organisation-policy promotion, IAM binding, Vertex AI model registration, Vertex AI endpoint deployment, BigQuery data-product publication.

Governance and Trust on GCP uses Organization Policy, Security Command Center, Audit Logs, and Dataplex governance as the policy-as-code substrate. EU AI Act and ISO/IEC 42001 conformance evidence is emitted continuously rather than assembled retrospectively.

Expert Intelligence on GCP uses Vertex AI for the model lifecycle: Vertex AI Model Garden for frontier-model access (Gemini, Anthropic, Meta, Mistral), Vertex AI Pipelines for orchestration, Vertex AI Vector Search for retrieval.

Engagement-pattern sketches

Representative patterns FORGE is built to deliver on GCP:

A BigQuery data-foundation establishment for an AI portfolio. BigQuery data products built under Data Foundation discipline (lineage, agent-ready retrieval, Dataplex governance) as the substrate for multiple downstream AI workloads.

A Vertex AI frontier-model deployment that emits EU AI Act conformance evidence continuously. A Gemini or Anthropic model deployment via Vertex AI Model Garden, with retrieval-quality and guardrail-trigger telemetry as live SRE signals, the EU AI Act Article 14 human-oversight controls enforced as a promotion gate via Organization Policy, and the full traceability genome bound from prompt to deployment.

A multi-cloud architecture with GCP as the data plane. BigQuery and BigLake as the cross-cloud data plane under FORGE Data Foundation discipline; downstream consumption from AWS or Azure compute under FORGE governance that crosses the cloud boundary cleanly.