Google Cloud AI Strategy & Architecture
A workload-first roadmap for Gemini, Vertex AI, data, integration, identity, and runtime decisions. We connect technical architecture to the operating result, delivery sequence, risk controls, and evidence of success.
Gemini & Vertex AI Systems
Custom assistants, agents, multimodal workflows, retrieval systems, evaluations, and application features built with Gemini and Vertex AI, grounded in trusted data and connected to the tools people already use.
Data Platforms & BigQuery
Practical data foundations for AI and analytics: ingestion, transformation, governed access, BigQuery modeling, semantic layers, reporting, and the pipelines that keep models connected to current business truth.
Cloud-Native Application Modernization
Modernize applications and agent workloads with Cloud Run, containers, APIs, event-driven services, and Google Kubernetes Engine when the workload calls for it, without forcing complexity where a simpler runtime will do.
Secure Enterprise Integration
Connect Google Cloud to Workspace, CRM, ERP, document systems, data stores, and operational tools with least-privilege identity, auditable handoffs, human approvals, and clear failure paths.
Managed Cloud AI Operations
Ongoing quality evaluation, cost and latency monitoring, security review, model and prompt updates, incident response, documentation, and workflow improvement after the first production release.