AI on Google Cloud, Where the Data Lives.
Google Cloud's edge in AI is data gravity and ML heritage — Vertex AI and Gemini sitting right next to BigQuery, where so much analytical data already lives. We build AI on Google Cloud that exploits that proximity, turning your warehouse into a launchpad for analytics-driven AI rather than a place data has to leave.
Bring the Model to the Data, Not the Reverse
Google Cloud's distinctive strength for AI is the gravity of BigQuery. An enormous amount of the world's analytical data already lives in BigQuery, and Google Cloud lets you build and run AI right next to it — querying it, training on it, even invoking models directly from SQL through BigQuery ML. When your data already sits there, building AI on Google Cloud means bringing the model to the data rather than dragging the data to the model, which is faster, cheaper and operationally simpler.
Layered on that is Google's deep machine-learning heritage. Vertex AI provides a mature, unified platform for the full ML lifecycle, Gemini brings frontier multimodal models, and the underlying infrastructure reflects an organization that has been doing large-scale ML longer than almost anyone. For data-rich, analytics-driven AI — forecasting, segmentation, recommendation, anything that lives close to a warehouse — Google Cloud is frequently the most natural fit.
We build AI on Google Cloud to lean into exactly these strengths. We keep computation close to BigQuery to exploit data gravity, use Vertex AI for the lifecycle where it earns its keep, and bring Gemini in where frontier capability is needed — all architected for cost and scale. The result is analytics-driven AI that treats your warehouse as the asset it is, rather than as an obstacle data must be exported from before anything useful can happen.
What We Build on Google Cloud
Our GCP Build Approach
1. Data & Use-Case Mapping
We map where your data lives in BigQuery and the analytics-driven use cases closest to it, so we build AI that exploits the data gravity already in your favor rather than fighting it.
2. Service Selection
We choose between BigQuery ML, Vertex AI and Gemini for each use case — SQL-native models for analytics teams, Vertex for custom lifecycles, Gemini for frontier capability — matching tool to need.
3. Architecture & Cost Design
We design to keep compute near the data, minimize movement and egress, and right-size training and serving, so the system exploits Google Cloud's economics rather than working against them.
4. Build & Operationalize
We build the models and stand up proper MLOps on Vertex — pipelines, registry, monitoring — so the AI moves from experiment to dependable production rather than stalling in a notebook.
5. Integrate & Optimize
We integrate the AI back into your analytics and applications so the business can act on it, then optimize against real usage and hand over an operable, documented system.
Why Data Gravity Should Drive the Decision
Data gravity is one of the most underrated forces in cloud architecture. Large datasets are expensive and slow to move, and they exert a pull on everything around them — the compute, the tooling, the rest of the system tends to gather where the data already is. For AI, which is fundamentally about computing over data, this matters enormously. Building your AI next to your data is faster, cheaper and simpler than repeatedly hauling that data somewhere else to process it.
This is precisely why Google Cloud is so compelling when your analytical data lives in BigQuery. Rather than exporting data to train and infer elsewhere — incurring egress, latency and duplication — you build the AI where the data already sits. BigQuery ML lets analysts run models in SQL without moving anything; Vertex AI trains and serves close to the warehouse; the whole architecture respects the gravity instead of fighting it. The savings and the simplicity compound across every model you build.
We make data gravity a first-class consideration in how we architect on Google Cloud. We resist designs that shuttle large volumes of data around unnecessarily, keep computation close to where data lives, and exploit BigQuery's native ML capabilities where they fit. For data-rich organizations, this approach turns the warehouse from a passive store into the active center of an AI capability — which is exactly what Google Cloud is built to enable.
Turn Your BigQuery Warehouse Into an AI Engine
Many organizations have invested years and significant money into a BigQuery analytics capability, and that investment is a far stronger AI foundation than they realize. The clean, governed, query-ready data that powers their dashboards is exactly what predictive and generative AI need. The step from analytics to AI on Google Cloud is short precisely because the hard part — assembling trustworthy data in one place — is already done.
We help organizations take that step. We build forecasting, segmentation, recommendation and propensity models on the warehouse data they already trust, and we bring generative capability through Gemini grounded in that same data. Because everything is built close to BigQuery, the AI is fast to develop, cheap to run and easy for analytics teams to understand and extend — it is an evolution of the analytics capability rather than a foreign system bolted alongside it.
If your data center of gravity is BigQuery, or Google Cloud is your platform, we bring the depth to build AI that exploits that position fully. You get analytics-driven AI — predictive and generative — that lives where your data lives, runs efficiently because of it, and turns the warehouse you already built into an engine for predictions the business can act on.
Frequently Asked Questions
Custom machine learning on Vertex AI, generative and multimodal AI on Gemini, and SQL-native models in BigQuery ML — all architected to exploit the data gravity of BigQuery. We focus especially on analytics-driven AI like forecasting, segmentation and recommendation that lives close to your warehouse.
Two things: data gravity and ML heritage. So much analytical data lives in BigQuery that building AI right next to it is faster and cheaper than moving data elsewhere. And Google's long machine-learning heritage shows in Vertex AI and Gemini. For data-rich, analytics-driven AI, it is frequently the most natural fit.
It lets you build and run machine-learning models directly in BigQuery using SQL, without moving data or standing up separate infrastructure. It is powerful for analytics teams who can apply forecasting, classification or clustering to warehouse data with the SQL they already know, keeping everything where the data lives.
BigQuery ML is ideal for analytics teams applying ML to warehouse data in SQL. Vertex AI is for custom models that need the full lifecycle — bespoke architectures, careful tuning, production MLOps. Many organizations use both, and we help you choose the right tool for each use case.
Moving large datasets is slow and incurs egress charges, and AI computes over data repeatedly. Building AI next to your data — rather than exporting it each time — cuts cost and latency substantially. When your data is in BigQuery, Google Cloud lets you respect that gravity, which is a real and recurring saving.
Yes. We build applications on Gemini's frontier multimodal models — text, image and more — with retrieval and grounding in your own data so responses are accurate and relevant. Built on Google Cloud, those applications can sit close to your BigQuery data for grounding rather than shipping it elsewhere.
A large one. The clean, governed data powering your dashboards is exactly what AI needs, so the step from analytics to AI is short. We build predictive and generative AI on that existing foundation, turning your warehouse investment into an engine for forecasts, recommendations and decisions the business can act on.
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