Give it the right context
Connect approved documents, APIs, tools, and systems through scoped credentials and defined purposes.
Agent deployment / tools, permissions, and production
An agent is not production because it can call a tool.
We deploy bounded AI agents around a real workflow: approved sources, connectors, identity, permissions, retries, logs, evaluation, human approval, and a clear owner. The goal is a useful system that can be monitored and operated, not a demo with unrestricted access.
Approved toolsAccess boundariesEvaluation setProduction runbook
Pattern demonstration / actions vary by scope
What production adds
The action is the smallest part of the deployment.
A dependable agent needs a boundary around every action, source, identity, failure, and human decision it can touch.
Limit what it can do
Set user, project, tool, spend, egress, approval, retry, and rollback boundaries before an action is enabled.
See when it fails
Log runs, evaluate outputs, trace source and tool use, detect stale or failed connectors, and give someone ownership.
Agent safety is specific
A general promise is not a control.
Authenticate the user
The agent should know which person, team, project, or service is asking and what that identity may access.
Require approval where it matters
Sending, writing, deleting, paying, publishing, or changing records needs an explicit decision boundary.
Test the behavior
Use known tasks, bad inputs, permissions, tool failures, and rollback scenarios before calling the agent ready.
From prototype to operating agent
Deploy the boundary before the autonomy.
Name the job and action
Choose the workflow, input, output, tools, users, and the action that would create value.
Map identity and data
Define sources, credentials, permissions, logs, egress, retention, and the data the agent must never see.
Build the test set
Evaluate useful tasks, unsupported requests, tool errors, permissions, retries, and human approval behavior.
Run and hand over
Deploy in client-owned infrastructure where agreed, monitor the runs, train the administrator, and document the operating path.
An agent becomes a service when the client can see what it did, stop what it is doing, and explain who allowed it.
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.
Can an agent act without approval?
Only where the action is low-risk, explicitly scoped, tested, and accepted by the client. High-impact actions remain behind human review.
Which agent frameworks do you use?
The workflow and client environment decide. We can configure approved harnesses, connectors, retrieval, and automation tools without making the tool the offer.
Can you monitor agent performance?
Yes, through run logs, fixed evaluation tasks, source and tool traces, correction records, and failure alerts appropriate to the 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 action
What should an agent do, and what must it never do?
Tell us the workflow, tools, users, data boundary, approval point, and failure cost. We will scope the deployment around it.