Blueprint and governance
The use case list, model choices, region, grounding sources and evaluation metrics are agreed with the product owner, data protection and the cloud platform team before the first pipeline is written.
Platform implementation
Google Vertex AI
We build on Google Vertex AI to turn Gemini and your own models into governed services, with pipelines, evaluation and a network perimeter in place before users arrive.
Vertex AI covers generative and classic machine learning alike, so the first step is deciding which use cases need Gemini, which need a trained model and which need neither. Google Cloud has regions in Dammam and Doha, and Vertex AI endpoints are mostly regional, so we confirm which models and features each region offers before design, alongside the Saudi PDPL and NDMO standards where they apply. Gemini models accept Arabic prompts and documents, which opens Arabic summarisation and search use cases.
Delivery uses Vertex AI Pipelines for repeatable training and batch jobs, Model Registry to version models, and Model Garden to try open and partner models next to Gemini. Grounding runs through Vertex AI Search or your own retrieval over BigQuery, and the Gen AI evaluation service compares prompts and models on agreed metrics before release.
Google Vertex AI and related marks are trademarks of Google LLC.
How we implement Google Vertex AI
Implementation, customisation, deployment and ongoing support, with governance your steering committee can follow.
Perimeter and keys
VPC Service Controls around Vertex AI and BigQuery, customer-managed encryption keys and private access to endpoints.
Agents that call your systems
Agents built with Vertex AI Agent Builder that call your APIs with narrowly scoped permissions and cite their sources.
Classic machine learning
Forecasting, churn or fraud models trained with BigQuery ML or custom training and served on Vertex AI endpoints.
Hypercare and managed support
Gemini model versions are retired on published dates and Vertex AI features change often, so we schedule model upgrades, rerun evaluations before switching, and track latency, quota, cost per request and pipeline failures.
Signs Vertex AI fits your stack
Vertex AI suits organisations whose data already sits in BigQuery or elsewhere on Google Cloud, that want Gemini alongside open models, or that need one platform for generative AI and predictive models.
- Data in BigQuery or Cloud Storage with clear ownership
- A Google Cloud organisation with projects and networking in place
- Use cases sorted into generative and predictive
- Evaluation metrics agreed with the business
Plan Google Vertex AI with Hosn Al Tomoh
Share your current systems, compliance themes and integration map. We will return a phased plan, a risk register and a governance model for your stakeholders to review.