Cloud · GCP · Adopt
Practical Generative AI on Google Cloud, From First Experiment to Production
Most businesses know AI matters but aren't sure how to move past a proof of concept. We help identify high-value use cases and build production-grade generative AI solutions on GCP, including Vertex AI and Gemini.
GCP Has the AI Infrastructure — We Build What Runs On It Reliably
Google Cloud provides some of the deepest AI infrastructure of any platform — Vertex AI's access to Gemini and other foundation models, purpose-built machine learning infrastructure, and the data processing scale that comes from the same infrastructure Google uses internally. But infrastructure alone doesn't get you from a demo to a production system people depend on. That gap requires solid use case discovery, integration with your actual data and workflows, and the monitoring and governance a live system needs.
We approach GCP-based generative AI the way we approach any infrastructure decision: start with the business problem, then build the solution — including the GCP infrastructure to run it reliably — around that.
What Generative AI on GCP Covers
Use Case Discovery
Identifying high-value, technically feasible use cases and assessing data readiness
Solution Design
Architecture spanning Vertex AI, Gemini models, or fine-tuned models depending on the use case
Integration
Connecting AI solutions to your existing applications and data sources, including BigQuery
Deployment Infrastructure
Scalable, secure infrastructure for hosting and serving models on GCP
Data Pipeline & Governance
Reliable data pipelines and access controls supporting the AI system long-term
Who This Is For
- Businesses with a clear problem but unsure whether or how generative AI applies
- Teams with a proof of concept that never made it to production
- Organizations with data in BigQuery or on GCP that isn't yet organized to support AI reliably
- Companies wanting to move on AI without taking on unmanaged compliance or reliability risk
Common Questions
Do you build custom models, or use Vertex AI's foundation models?
Most engagements use Vertex AI's foundation models, including Gemini, via API or fine-tuning, since building a model from scratch is rarely justified. We recommend the right approach based on your specific requirements.
Can this integrate with our existing BigQuery data?
Yes — integration with BigQuery and broader GCP data infrastructure is a common and often advantageous starting point for GCP-based AI solutions.
How do you handle the risk of AI generating incorrect output?
Through retrieval-grounded design, evaluation frameworks, and human-in-the-loop review for higher-stakes use cases, depending on how the output is used.