How It Works
We perform a deep technical assessment of your Google Cloud environment — mapping existing GCP services, Vertex AI access and quotas, data estate readiness, IAM posture, and current AI spend across your projects.
Every high-ROI Vertex AI use case is identified, scored against ROI and feasibility, and matched to the right Gemini model from the Model Garden — giving you a clear, funded activation sequence.
You receive a detailed Vertex AI architecture blueprint — covering agent architecture, Vertex AI Search indexing strategy, BigQuery ML integration plan, cost model, and a phased implementation roadmap.
What's Included
Comprehensive review of your Google Cloud projects, existing Vertex AI deployments, API quotas, networking configuration, and current AI service spend across your GCP organisation.
Evaluate your data sources — BigQuery, Cloud Storage, AlloyDB, Firestore — against what Vertex AI agents need to ground Gemini responses in accurate, fresh enterprise data.
Identify and rank every high-value AI agent opportunity across your business, with effort estimates, expected ROI, and a recommended implementation sequence tied to Vertex AI capabilities.
Determine which Gemini model — Flash, Pro, or Ultra — is right for each use case based on latency, cost, context window, and multimodal requirements from the Vertex AI Model Garden.
Model token costs, compute requirements, and GCP infrastructure spend for your prioritised use cases so you can budget accurately before committing to development.
Assess your GCP IAM configuration, VPC Service Controls, data residency posture, and Vertex AI safety settings against enterprise AI security best practices and regulatory requirements.
Who It's For
Organisations with Vertex AI access and budget approval but no production AI agents deployed — you need a defensible GCP AI roadmap before spending on development.
Technical leaders who must present a credible, board-ready Vertex AI strategy with phased milestones, resource requirements, and measurable ROI projections grounded in your actual GCP environment.
Engineering and architecture teams evaluating whether to build on Vertex AI or Azure — you need a technical assessment that maps your current GCP footprint to AI agent capabilities.