Data Foundation
AI is only as good as the data it reasons over. If your data lives in spreadsheets, personal drives, and disconnected SaaS tools — your AI ceiling is very low. We build the foundation that removes that ceiling.
There is a predictable pattern in companies that try to implement AI and fail. It's not the model. The models work. It's the data. Specifically: it's unstructured, siloed, un-governed, and inconsistently formatted — which means any AI working with it will produce outputs that are plausible but wrong.
The Google Cloud data stack — BigQuery, Looker, and the native Workspace integrations — gives you a clean foundation. We architect it, load it, govern it, and connect it to the AI layer so when you're ready to deploy Vertex AI or Gemini agents, there's something worth connecting them to.
"Garbage in, garbage out hasn't changed just because the model is smarter. The model will confidently hallucinate from your messy data."
BigQuery is where your data lives when it grows up. Serverless, scalable, and deeply integrated with Gemini and Vertex AI. We design the project structure, dataset organization, and access controls from scratch — or clean up what exists.
Looker Studio is fine for quick dashboards. Looker (the platform) is for organizations that need a single source of truth for metrics — where every team uses the same definition of "revenue" or "active customer." We set up the LookML models and the dashboards your leadership team will actually use.
Your CRM, your ERP, your billing system, your Workspace activity data — all flowing into BigQuery automatically. We design and build the pipelines using native Google tooling and modern ELT patterns. No brittle custom scripts.
Workspace activity data — login events, Drive access, email metadata, Meet usage — flows into BigQuery natively. This powers security analytics, productivity insights, and AI training data all from the same source.
Vector embeddings for unstructured content. Document schemas for Vertex AI agent grounding. Clean, labeled datasets for fine-tuning. The data architecture that makes production AI possible — not just a demo.
Executive
Board metrics come from BigQuery, not a spreadsheet that someone updated last Tuesday. No more pre-meeting reconciliation calls.
Finance
P&L, headcount, and cash flow dashboards that update automatically. Month-end close takes hours, not days.
Sales
CRM data in BigQuery. Looker dashboards that show real pipeline, actual conversion rates, and rep performance without a weekly CSV export.
Operations
Every operational workflow measured. Cycle times, error rates, and throughput visible — so you're improving processes with data, not instinct.
Engineering
Usage data, feature adoption, and error rates all in BigQuery. Product decisions made from actual user behavior.
AI Teams
Labeled, structured, version-controlled datasets ready for Vertex AI. No more data prep sprint before every model experiment.
If three or more of these are true, start with the data foundation — before any AI implementation.
We'll assess your current data architecture, identify the gaps that will block AI implementation, and outline what a clean foundation looks like for your stage and stack.