Google Cloud GenAI Integration

Teams prototype quickly in notebooks but stall when moving to governed enterprise environments—latency, cost, safety, and evaluation gaps appear late.

Reference patterns for prompt routing, grounding with retrieval, evaluation datasets, canary releases, and cost controls. Emphasis on separating experimentation sandboxes from customer-facing serving paths.

Certified implementation patterns reduced time-to-production for new GenAI features by standardizing the boring infrastructure: logging, redaction, quotas, and rollback.