Data
Every stalled AI program traces back to the same place: data nobody trusts, spread across systems that disagree. This is the unglamorous work that makes everything downstream possible, and it is where most of the measurable return lives.
Your analysts wait days for questions that should take minutes, and half the answer is caveated because two systems disagree. We activate your data estate on one governed platform — BigQuery at the center, with the governance, lineage and access controls that let you point agents at it without holding your breath.
The warehouse contract renews next year and the number has gone up again, while the workloads on it have not modernized since it was signed. We move data estates onto Google Cloud's data platform — from on-premises or from another cloud — and we tell you honestly which workloads are worth moving first and which should stay where they are.
Both plays carry the same entry point: a scoped assessment that produces a migration plan and a cost model you can take to your CFO.
A workshop to identify where the value is. An assessment to plan the work properly. A pilot to prove it in your environment before you commit. Each one is a defined piece of work with a deliverable, not an open-ended statement of work.
Book a working sessionThe other practices
Gemini Enterprise inside the tools your team already uses, grounded in your own data.
Customer-facing agents, and the platform that lets the tenth one ship faster than the first.
Landing zones done properly the first time, and the enterprise platforms nobody wants to move.
Autonomous detection and response, and the controls that let an AI program ship.
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