Use what is already there
Check the current plan, identity, Gmail, Drive, Docs, Calendar, and admin controls for the task.
Comparison / Google Workspace AI versus private deployment
Google Workspace already has AI built in. It may still not hold the boundary you need.
We compare Google Workspace AI with a private AI deployment by workspace fit, data path, admin controls, connected sources, workflow depth, and ownership. Current vendor terms are reviewed before a recommendation.
Workspace fitData pathAdmin controlsWorkflow gap
Pattern demonstration / same workflow, synthetic material
Fixed-scope setupClient-owned accountsDraft-and-review firstFull handover
Where the Workspace decision gets real
The assistant is easy. The boundary is not.
Google Workspace AI can be the practical route when the existing environment, accounts, and current terms cover the workflow.
Review the data path
Compare what prompts, files, and connected sources do under the current terms and where they go.
Test the workflow gap
Run the recurring job and compare output, source visibility, permissions, approvals, and system actions.
The comparison boundary
Do not assume the product name means the control.
Current documentation wins
Retention, provider path, admin controls, and regional behavior are checked against the current official terms.
Permissions stay explicit
Shared drives, delegated mailboxes, external sharing, and connected apps are reviewed before trust.
The client decides
Whether Workspace AI, a private build, or a mix is right remains a decision for the client with its advisers.
A practical Workspace comparison
Put the real workflow through both routes.
Map current use
List users, files, connected sources, prompts, outputs, and the recurring workflow.
Review current terms
Check official Google documentation and the client's agreement for data path and controls.
Test the workflow
Run the same questions in the available route and measure sources, access, and gaps.
Scope the private gap
If the managed route is insufficient, define a private workflow with client-owned accounts and a fixed test set.
Google Workspace AI can be the right answer. The gap appears when the work needs a different data path or workflow.
Why trust Pristine3D?
We build and operate production software.
Pristine3D Ltd builds and operates live digital products, and we run private AI workflows internally as part of our own operations. We scope around your real workflow: the documents you own, the questions your team asks, and the access boundary you approve. Based in Lagos, Nigeria, we work remotely with clients worldwide.
We already run live products
Based in Lagos, Nigeria, Pristine3D currently builds and operates smartcards.ng, venu.ng, photoshoot.ng, and ugc.ng in production.
We start with the actual workflow
One input, one output, one test set, and one person who owns the result. We scope a real workflow instead of a transformation programme.
The boundary stays visible
Cloud, model, storage, and messaging accounts stay in your name. The chosen data path, access rules, test record, documentation, and training are part of the agreed scope.
Pricing / fixed scope
Know the starting numbers before you ask.
The final quote follows the workflow. Infrastructure and model bills stay on your accounts.
One Workflow
Starting from$3,500 / ₦2.5m
2 to 3 weeksOne workflow for a small team, with training and handover.
See the one-workflow packageFirm-wide
Starting from$7,500 / ₦8m
4 to 6 weeksMultiple sources, roles, integrations, and admin handover.
See the deploymentAnnual support
Starting from$3,000 / ₦1.5m
per yearStandard care for one delivered workflow. Optional. Larger deployments and active monitoring are separately scoped.
See supportArchitecture review from $500. Standard annual support is $3,000 / ₦1.5m per year for one delivered workflow. New workflows, integrations, active monitoring, and infrastructure are separately scoped; infrastructure, model, storage, and messaging bills stay on client accounts. Full pricing and what changes the quote
Straight answers
Common questions.
Is Google Workspace AI private?
The current position depends on plan, configuration, and terms. We review official documentation and the client's agreement before concluding.
Can Workspace AI handle a full workflow?
It can handle connected drafting and search tasks. Queues, approvals, fixed outputs, and system actions may still need a private workflow.
Can you recommend Workspace AI instead of a build?
Yes. A useful review identifies when the existing route is sufficient, not when a new deployment should happen for its own sake.
Own your knowledge base
The model is not the product. The knowledge base is.
Documents, the retrieval index, access rules, and the workflows built around them are the asset, and they compound. We deploy so the knowledge base stays yours: on your accounts, in the environment you choose, under access rules your team defines. The model behind the answers is a connector, so the knowledge base moves with you, not with a vendor.
Your corpus, your index
The document store, metadata, and retrieval setup live on accounts you own. No vendor holds the corpus.
Models are swappable parts
Change the model provider, move regions, or go local without rebuilding the knowledge base or the workflow.
The knowledge base is the alpha
Every improvement to the corpus improves the answers, and the improvement stays with you, not with a vendor.
Keep exploring
Related setups.
Start with the workflow
What does Workspace AI still not solve?
Tell us the recurring task, the Workspace environment, the data classes, and the review owner. We will map the gap before recommending a build.