Google Cloud Partner NetworkGoogle calls it the front door for AI at work. Getting through that door is a deployment problem: connecting your systems, building the agents worth building, and governing what they can reach. That is the part we do.
A Google Cloud focused bench, certified on Gemini Enterprise deployment and agent development.
Delivered for government and enterprise, and for the ventures we build ourselves
The platform
Not a chat assistant with a new name. Google ships six things as one platform, and each of them is a decision someone has to make properly during deployment.
The frontier models underneath, the same ones Google runs its own products on.
People in finance or marketing can analyse information and orchestrate agents themselves, without queuing for engineering time.
A ready taskforce for specialist jobs, deep research and data science among them, extended with agents built for your own workflows.
Grounded in your systems wherever they live. Google publishes connectors for around thirty third-party sources, from Workspace and Microsoft 365 through to Salesforce, SAP, ServiceNow, Jira and Slack. Anything not on that list, including whatever you built yourselves, is reachable through Model Context Protocol or a custom connector.
andServiceNow
Visualise, secure and audit every agent from one place, rather than one unmonitored pilot per team.
Agent2Agent for agents that delegate to each other, Model Context Protocol for tools and data. We have shipped an MCP endpoint on a governed public sector platform, so external assistants reach the same approved metrics as everything else.
From the floor · Google Cloud Next ’26
The theme this year was clear. AI agents in production. Not experiments. Production.
How do you secure them, make them compliant, monitor them, govern them at enterprise scale. Every conversation circled back to this.
It also made me think about the whole "AI bubble is about to burst" narrative. I have been working in this space for over a decade, with enterprise customers every day, and the untapped potential I see is enormous. We are genuinely just getting started. As agents get smarter their applications will only grow: systems that can reason, decide and act on their own.
If anything slows this down it will not be demand. It will be hardware. If we cannot build enough infrastructure to handle the compute these models need, AI will not burst. It will just get more expensive and harder to access.
That is a supply problem, not a hype problem.
Dr Adnan AkbarChief Technology Officer, BayseianWhat we deploy
Gemini Enterprise is the platform. This is the work that decides whether it lands.
Gemini Enterprise deployed across the organisation and connected to the data people already work in, so it is useful on day one rather than after a project.
Agents built for the workflows that actually cost you time, using the Agent Development Kit, then connected to the systems that hold the work so they can finish a task rather than describe one.
Access scoped to what each person can already see, Model Armor configured against prompt injection and data leakage, and activity logged so an auditor can follow it.
Where teams point it
Customer service, marketing, security operations and IT support are where most organisations start, because the work is high volume, already system-bound, and already checked by someone. None of these need a year-long programme.
The agent answers from your own policies and product docs, pulls the customer record, updates the ticket, and drafts the reply for a human to send.
Access requests, onboarding checklists and policy questions, answered and actioned inside the systems that own them.
Account history, open opportunities and last quarter's numbers, gathered before the call rather than during it.
Order and invoice exceptions triaged against the record, with anything irreversible held for a person.
Incoming issues classified, routed and enriched with the history that makes them diagnosable.
Briefs and drafts grounded in your brand guidelines and past campaigns, not in whatever the model remembers.
Inside a run
Four steps, with the governance checks sitting inside the ones where they happen rather than bolted on at the end. Open a step to see what it involves.
The agent searches the sources you connected rather than its own training data, so the answer is anchored in your content as it stands today.
The model breaks the request into steps and selects the tools it needs. This is where an agent stops being a chat box: it is choosing actions, not phrasing.
Reversible internal actions run unattended. Anything irreversible or externally visible stops for a human, until evaluation history says otherwise.
Delegates over A2A
Every subagent inherits the entitlements of the person who asked, and is registered centrally rather than standing alone.
Every run is reconstructable months later: who asked, what was retrieved under whose entitlements, what the agent did and what a human approved.
Marked steps are the governance checks: access scoping, Model Armor, approval and audit.
How we deliver
Typically eight to twelve weeks from scoping to something running in production, then we operate it. We build and run the marketing agents for a sovereign-backed giga-project and the platform behind Tekniti, so staying past go-live is how we already work.
1 to 2 weeks
Pick the workflows worth automating, and agree what a good result actually looks like before anything is built.
2 to 4 weeks
Connect the data sources, set the access rules, and stand up the first agent against real content.
2 to 4 weeks
Evaluate against real cases with the people who do the job, and fix what the evaluation exposes.
Ongoing
We stay on it after go-live: monitoring in production, evaluating as the data shifts, and adding agents while the pattern holds. We run systems we have built, we do not hand them over and leave.
Selected work
Knowledge trapped in documents, agents acting inside operational systems, one governed view across agencies that could not share a spreadsheet. Gemini Enterprise is a new platform for a problem we have delivered against repeatedly, including under public sector scrutiny.
All delivered on our own stack rather than on Gemini Enterprise. Same problem, different platform.
Working out where Gemini Enterprise fits?
Book a scoping callThe writing
Applied practice rather than a summary of Google's documentation: what to connect first, what to govern and in which order, which workflows are worth an agent at all.
Gemini Enterprise FAQ
Yes for deployment work, since we are configuring your tenant rather than reselling licences. If you are still deciding, we can run the scoping conversation first and tell you which workflows would justify it, which is a shorter and cheaper way to find out than a pilot.
The Gemini app gives individuals a capable assistant. Gemini Enterprise adds the parts an organisation needs: agents grounded in your own systems, a place to build and register them, retrieval scoped to what each person may already see, and one view of every agent running. The models are the same. The difference is everything around them.
They act. An agent can retrieve from your systems, call tools, update a record and complete a step, which is the whole reason the platform exists. Whether a given agent is allowed to act unattended is a decision you make per action. We hold anything irreversible or externally visible behind a person until the evaluation history justifies otherwise.
It will, sometimes, so the question worth asking is whether being wrong is recoverable. We sort every action by whether it can be undone and whether anyone outside the organisation sees it. Reversible and internal, such as updating a ticket field, can run unattended. Anything else keeps a human in the loop, and every run leaves a record you can reconstruct months later.
Agents inherit the permissions people already have, so an agent cannot surface a document its user could not open. We configure Model Armor against prompt injection and data leakage, and we check the permissions in each source system before connecting it, because permission-aware retrieval faithfully reproduces whatever access model it finds.
No. Gemini Enterprise connects to systems where they already are, including Microsoft 365, SharePoint, Salesforce, SAP and ServiceNow. Some workloads do benefit from moving, and where residency or classification rules apply we work inside them rather than around them, but a migration is not a precondition for getting value.
Yes, and this is more interesting than it sounds. Gemini Enterprise supports open agent protocols, so agents built on other frameworks can be registered and can delegate work to each other. We have built governed platforms whose data layer is reachable by external assistants through a single approved interface, so the same rules apply no matter which client is asking.
It starts with the workflow, not the model. We look at where work is repetitive, where information is scattered, and where handovers lose time, then pick the smallest thing worth automating properly. Typically eight to twelve weeks from scoping to something running in production, then ongoing.
Get started
Tell us the workflow that costs your team the most time. We come back within two business days with what an agent can realistically take on, what it needs access to, and how we would govern it.
