Name the approved set
List the tools and plans staff may use for business work and keep the list current.
Governance / acceptable use policy
An AI policy works when people can follow it. Not when it reads like a warranty.
Write the policy as a decision route: which tools are approved, what data may enter them, what users may share, who reviews outputs, what counts as an incident, and what changes when the rules are broken.
Approved toolsData classesUser rulesReview route
Pattern demonstration / same workflow, synthetic material
Fixed-scope setupClient-owned accountsDraft-and-review firstFull handover
What a useful policy contains
The shortest policy that answers the real questions wins.
Staff do not need a technical essay. They need to know what they may paste, what they may draft, and what to do when they are not sure.
Set the data boundary
Define what may be entered: public material, internal policies, client files, personal data, and never-enter classes.
Make review visible
State who checks outputs, how errors are reported, and how the policy changes.
The policy boundary
The workflow drafts the policy. The business owns the rules.
Enforcement stays human
Consequences and disciplinary decisions remain with management and any applicable process.
Legal review stays expert
The policy is reviewed by legal or HR before adoption, including any jurisdiction-specific duties.
Versioning is part of it
Every change has a date, owner, summary, and acknowledgement record.
A first policy workflow
Start with the tool set and the data boundary.
Inventory the tools
List the AI tools already in use and the business purpose of each.
Draft the route
Write approved tools, data classes, user rules, review, and incident steps in plain language.
Run the review
Have legal, HR, IT, and a sample of users test the policy against real scenarios.
Publish and maintain
Issue the version, collect acknowledgements, and set a review owner and date.
The best AI policy is the one staff can follow without calling IT.
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.
Can it write the whole policy?
It can prepare a draft from your approved facts and current tool set. The final policy and adoption process stay with your business.
How long should the policy be?
Short enough to follow: approved tools, data boundaries, user rules, review, incidents, and consequences.
What happens when a new AI tool appears?
The policy should name who reviews new tools and how the list gets updated before adoption.
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 tool set
Which AI tools does your team already use?
Tell us the tools, the data they touch, and the review owner. We will draft the first policy around your actual use.