Keep work in the right place
Personal tasks, group work, project context, and organization-level material should have different boundaries.
Team agent workspace / technical startups
Give the team one place to work with agents.
A technical startup may need more than a document assistant. We deploy a private workspace where people and agents can work in personal, group, and project scopes, with approved tools, identity, limits, and a handover your team can operate.
Personal scopesShared projectsApproved toolsAdmin handover
Pattern demonstration / not a startup deployment
Why a startup outgrows separate accounts
The context is scattered before the company is large.
The product is an owned operating environment for people and agents, not a subscription with a new logo.
Approve what can connect
Connect only the tools, skills, repositories, and channels the team has chosen, with secrets and egress limits visible.
Make the system survive people
An administrator can manage identity, spend, backups, updates, and access when a founder, employee, or contractor changes.
What this is and is not
A workspace deployment is a real system.
A client-owned environment
Identity, model-provider choice, secrets, spend limits, regions, backups, and the deployment repository are scoped for handover.
A multi-user operating model
Personal, group, organization, and project scopes make collaboration possible without treating every context as public.
Unlimited autonomy
Native RAG, WhatsApp, local inference, formal certification, and unrestricted connectors are separate design and scope decisions.
From architecture to handover
Build the boundary before the agents multiply.
Map people and scopes
Define who needs personal, group, project, and administrator access, and what should happen when access changes.
Choose the runtime
Select the client-owned cloud, region, model provider, harness, identity path, and storage approach that fit the workload.
Test real internal tasks
Run a fixed set of tasks, inspect tool behavior, measure spend and egress, and record known limitations.
Transfer the keys
Hand over the repository, accounts, credentials, runbook, backups, update path, and administrator training.
The reason to deploy a private workspace is not to make agents sound impressive. It is to make the team's context, access, and operating rules explicit.
Starting points
Buy the result, not an undefined amount of AI.
All infrastructure, provider, storage, and messaging bills remain on client accounts.
One Workflow
Starting from$3,500 / ₦2.5m
2 to 3 weeksOne workflow for a small team, with training and handover.
- One source or connector
- Up to 10 users
- Private chat or approved team channel
- Answers or drafts with sources
- Training and handover
Firm-wide
Starting from$7,500 / ₦8m
4 to 6 weeksMultiple sources, roles, integrations, and admin handover.
- Multiple sources and workflows
- Permissions and admin setup
- Team training and testing
- Runbook and credential handover
- Optional action workflows
Annual support
Starting from$3,000 / ₦1.5m
per yearStandard care for one delivered workflow. Optional. Larger deployments and active monitoring are separately scoped.
- Quarterly health and dependency review
- Bug fixes within delivered scope
- Priority business-hours response
- Documentation and configuration updates
- No new workflows, integrations, or infrastructure bills
Architecture 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
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.
Straight answers
Common questions.
Is this just a private RAG chatbot?
No. A basic document assistant can be one workflow. A workspace adds identity, personal and shared scopes, approved tools, spend controls, secrets, and administrator ownership.
Does it include local model inference?
Not automatically. Local inference, hardware, and performance need a workload test and a separate architecture decision.
What does the startup own at handover?
The deployment repository, client accounts, credentials, configuration, test record, runbook, backups, and known limitations, subject to the agreed scope.
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 operating model
Where are your team's AI tools breaking down?
Tell us the team size, current tools, desired scopes, and the first internal task that should run inside a client-owned workspace.