Google Cloud + Caelus: Data Products & GenAI
Accelerating value through BigQuery, Vertex AI, data product patterns & responsible GenAI delivery.
Architecture & Execution Pillars
Composable pillars drive rapid experimentation to governed production on Google Cloud.
BigQuery & Data Products
Ingestion, transformation & semantic modeling for analytics & ML reuse.
GenAI & Vertex AI
Model selection, retrieval augmentation, evaluation & deployment orchestration.
MLOps & DataOps
Pipelines, model registry, feature store & drift monitoring across environments.
Security & Responsible AI
Policy guardrails, data classification, explainability & bias evaluation patterns.
Cost & Performance
Slot optimization, storage lifecycle policies & model cost efficiency dashboards.
Adoption & Enablement
Playbooks, enablement sessions & co‑delivery model.
Unified GCP Data & GenAI Blueprint
Blueprint aligning ingestion, transformation, vector / feature management and model deployment with observability & governance.
- Data ingestion & pub/sub streaming + batch pipelines
- BigQuery semantic models & federation
- Feature store & embeddings with Vertex AI
- RAG / LLM orchestration & evaluation harness
- Centralized monitoring, lineage & cost analytics
Reusable Assets & Tooling
Reducing friction & lead time from concept to production through templates.
Value Sprint Playbook
2–4 week discovery: value model, blueprint slice & adoption plan.
GenAI RAG Starter
Embeddings, retrieval adapters & evaluation harness.
MLOps Templates
CI/CD pipelines, bias checks, drift detection & promotion workflows.
Impact Benchmarks
Representative improvements from Google Cloud programs.
3
Faster Use Case Throughput
40
Governance Maturity Lift
27
Run-Rate Cost Reduction
500
Enterprise Clients
Plan a GCP AI Value Sprint
Assess current landscape, surface priority use cases & define a sequenced 90‑day roadmap.