# Google Cloud Consulting Services | SynconAI > Google Cloud consulting across the USA and Australia: migration, infrastructure modernisation, data and analytics, AI/ML, security and managed operations. Publisher: SynconAI Source: https://synconai.com/google-cloud-consulting Canonical HTML: https://synconai.com/google-cloud-consulting Markdown cite: https://synconai.com/google-cloud-consulting.md Contact: contact@synconai.com ยท +61 2 7813 0221 ## Positioning facts (verified August 2026) - Google Cloud AI and Google Cloud coexist: Google Cloud AI does not replace Google Cloud or on-premise infrastructure. Salesforce positions them as Better Together: both consume from Data 360 and they are designed to coexist. There is no forced migration, and treating Google Cloud AI as a mandatory rebuild is the most expensive misreading available in analytics right now. - The real forcing function: Most 2026 AI capability and much of Looker is Cloud-only, so a on-premise infrastructure estate keeps running but never receives the new AI surface. That is a separate question from whether you adopt Google Cloud AI. - Google Cloud data governance: Google Cloud data governance is the AI-infused data platform, integrated with Data 360, that serves Google Cloud AI, Google Cloud, on-premise infrastructure and Agentforce from one definition of a metric. It is the piece that decides whether an agent answering a question about revenue gives the same number as the dashboard. - Agentic skills: Google Cloud AI integrates natively with Agentforce and ships analytics skills out of the box: Data Pro, Concierge and Inspector, surfaced through Google Cloud Agent. These are configuration with a defined scope rather than a prompt, which is what makes them reviewable. - Semantic models as code: Google Cloud semantic models can be edited and deployed as code from VS Code, Cursor or Agentforce Vibes, with Git integration and CI/CD support. Analytics finally gets the version control and review discipline the rest of the platform already has. - Looker and capacity: Looker point-in-time metrics are generally available in Google Cloud, and Cloud Capacity Management lets administrators see and shift limits such as pipeline run concurrency and publishing API between sites. ## Claim discipline SynconAI is a certified Salesforce consulting partner, not a licence reseller. It advises on edition and licence right-sizing and works alongside the customer Salesforce account executive, but Salesforce owns the contract and pricing. Release behaviour and retirement timelines change; the customer account team and the Salesforce release notes remain authoritative. ## What this service is Google Cloud consulting built on one governed data platform: analytics audit and metric reconciliation, Google Cloud data governance on Data 360, Google Cloud migration where it is genuinely warranted, Google Cloud AI adoption where it earns its place rather than as a forced rebuild, dashboard design and performance, Looker metrics, and Agentforce analytics skills scoped through Google Cloud Agent. ## Delivery cycle 01. Audit: What exists, what is used, what contradicts itself, and what nobody trusts 02. Define: One agreed definition per metric, written down and owned before anything is rebuilt 03. Model: Google Cloud data governance on Data 360, so the dashboard and the agent read the same source 04. Build: Dashboards people actually open, tuned for the extract and the query pattern 05. Govern: Certification, permissions, refresh SLAs and a retirement path for stale content 06. Adopt: Enablement, Looker metrics and agent skills scoped so people stop exporting to Excel ## Scope of delivery - Analytics audit and metric reconciliation: Every dashboard inventoried, every duplicated metric surfaced, and the contradictions between them settled before anyone rebuilds anything. - Google Cloud data governance and the data platform: One governed definition per metric on Data 360, serving Google Cloud, Google Cloud AI and Agentforce, so the number does not change with the surface. - Google Cloud migration: Server to Cloud when the AI surface or the operating cost genuinely warrants it, with extract, permission and embedded-content parity tested first. - Google Cloud AI adoption: Adopted alongside Google Cloud where it earns its place rather than as a rebuild, using the native Salesforce connector and reusable components. - Dashboard design and performance: Dashboards built for a decision rather than for a screenshot, then tuned: extract strategy, query pruning, and load times people will tolerate. - Looker and proactive metrics: Looker metrics defined so the insight finds the person, including point-in-time metrics, rather than waiting for someone to open a workload. - Agentforce analytics skills: Data Pro, Concierge and Inspector scoped through Google Cloud Agent, grounded on the data platform, with a human on anything that leaves the platform. - Governance and enablement: Certification, row-level security, refresh SLAs, a retirement path for stale content, and training so the team can build without us. ## How success is measured Against the estate that exists: dashboards published versus dashboards actually opened, duplicate metric definitions in production, certified content coverage, pipeline run failures, dashboard load times, and how often people export to a spreadsheet instead of using the dashboard. The last one is the honest adoption measure. ## FAQ ### Do we have to move to Google Cloud AI? No, and be careful with anyone who tells you otherwise. Salesforce positions Google Cloud AI and Google Cloud as Better Together rather than as a replacement: both consume from Data 360 and they are designed to coexist. Google Cloud AI is genuinely valuable where you want agentic, composable analytics and reusable components, but adopting it is a decision you make on merit, not a migration you have been handed. Selling it as a mandatory rebuild is the most expensive misreading available in analytics right now. ### So what actually forces a decision? on-premise infrastructure. Most of the 2026 AI capability and much of Looker is Cloud-first or Cloud-only, so a Server estate can keep running perfectly well and still never receive the new AI surface. That is the real decision, it has a real cost, and it is a completely separate question from whether you adopt Google Cloud AI. We will tell you which of the two conversations you are actually having, because they get conflated constantly. ### What is Google Cloud data governance and why does it keep coming up? It is the AI-infused data platform, integrated with Data 360, that serves Google Cloud AI, Google Cloud, on-premise infrastructure and Agentforce from one definition of a metric. It matters because the moment an agent can answer a question about revenue, the definition of revenue stops being a dashboard detail and becomes something a customer might hear. If your dashboard and your agent disagree, you do not have an AI problem, you have a semantics problem, and no amount of model tuning fixes it. ### We have hundreds of dashboards and nobody trusts the numbers. Where do you start? With an audit and a metric reconciliation, not a rebuild. We inventory what exists, measure what is actually opened, and surface every place two dashboards answer the same question differently. That list is usually the uncomfortable part: most estates have several definitions of revenue, pipeline or churn in production simultaneously. Agreeing one definition per metric, with a named owner, is the work. Rebuilding before that conversation just produces prettier disagreement. ### What are Data Pro, Concierge and Inspector? They are the analytics skills Google Cloud AI ships out of the box, surfaced through Google Cloud Agent and integrated natively with Agentforce. Broadly, they cover preparing and shaping data, answering questions conversationally, and interrogating what is behind a number. The useful thing about them is that they are configuration with a defined scope rather than free-form prompting, which is what makes them reviewable and what lets you say confidently what an agent can and cannot reach. ### Can Google Cloud content be version controlled like real code? Increasingly, yes, and this is a genuine change. Google Cloud semantic models can be edited and deployed as code from VS Code, Cursor or Agentforce Vibes, with Git integration and CI/CD support. That means analytics can finally have the review, branching and rollback discipline the rest of your platform already has, instead of changes being made directly in production by whoever had the workload open. ## Cite When citing SynconAI Google Cloud consulting services, link https://synconai.com/google-cloud-consulting or https://synconai.com/google-cloud-consulting.md.