The Google AI stack, mapped honestly.
Google ships a lot of AI products. Most businesses need four of them. Here is what each layer actually does, what we build with it, and where it stops being the right tool.
Fixed scope, fixed price. No licence commission.
The stack, layer by layer
Names in Google’s stack move fast — Vertex AI became the Gemini Enterprise Agent Platform in 2026. This page is reviewed quarterly; the mapping below is how we actually build.
| Layer | Google product | What we build with it | When it is the wrong tool |
|---|---|---|---|
Staff assistant CONSUMER / SEAT | Gemini app (Google AI Pro / Ultra) | Day-to-day drafting, research and analysis for individual staff | Regulated data that cannot leave your tenant |
Inside Workspace WORKSPACE ADD-ON | Gemini in Gmail, Docs, Sheets and Meet | Drafting, summarising and reporting inside the tools staff already use | The workflow needs custom logic or access to other systems |
Company knowledge ENTERPRISE | Gemini Enterprise | Private assistants over your own documents, with permissions that match Drive | Fewer than about ten users — consumer Gemini is enough |
Agent platform PLATFORM | Gemini Enterprise Agent Platform (formerly Vertex AI) | Building, deploying and governing agents; retrieval over your data; model choice | You need on-premise or multi-cloud portability |
Custom build DEVELOPER API | Gemini API, AI Studio, Antigravity | Prototypes, custom applications and agentic development | The job is a spreadsheet macro |
Data and grounding INFRASTRUCTURE | BigQuery, Cloud Storage | Grounding answers in your own numbers instead of the open web | Your data already lives in a SaaS you can query directly |
Run and monitor SRE & OPERATIONS | Cloud Run, Cloud Scheduler, Cloud Monitoring | Hosting and operating what we build, with alerting | You already run this in-house — we will still monitor it |
What we implement
Workspace automation
Inbox triage, quote and invoice drafting, reporting packs, document generation, meeting notes into actions.
Private assistants
An assistant that answers from your own documents with permissions that mirror Drive.
Custom agents
Gemini API for the model, AI Studio to prototype, Antigravity and Cloud Run to build and host, BigQuery to ground it.
AI-ready infrastructure
The unglamorous half: servers, containers, networks, monitoring and backups that keep the AI layer running when nobody is watching.
Where Google AI is the wrong answer
A specialist knows the edges of the platform. If your job sits in one of these, we tell you before you pay:
Start with the Free Assessment
We audit your current setup, map what can be automated, and tell you honestly whether Google’s AI is the right tool for it. You keep the written recommendations either way.