Size the job
Document volume, users, latency, model size, and offline needs set the hardware floor.
Infrastructure / self-hosted AI
The answer usually starts with a normal server, not a GPU rack.
Document search, drafting, and retrieval workflows can run on modest hardware or a client cloud VM. Larger local models need more memory and compute. We scope the infrastructure to the workflow, not to the trend.
Server or VMStorage and backupModel runtimeAccess controls
Pattern demonstration / same workflow, synthetic material
Fixed-scope setupClient-owned accountsDraft-and-review firstFull handover
What the buyer actually needs to decide
Infrastructure follows the workflow.
A one-source internal assistant and a full local model deployment have very different hardware, hosting, and support requirements.
Plan storage and refresh
Original files, the index, backups, and source updates need defined locations and owners.
Protect the path
Authentication, permissions, logging, and the support route are part of the deployment.
The infrastructure boundary
The setup is scoped to the client's actual requirement.
Most retrieval starts small
Many document workflows run well on a modest VM or small on-prem server.
Local models are heavier
Running a model locally needs enough CPU, RAM, or GPU for the model size and concurrency.
Client accounts and billing
Infrastructure, model, storage, and messaging bills stay on the client's accounts.
A first infrastructure decision
Start with the constraint, not the hardware catalog.
Name the requirement
Decide residency, offline use, latency, users, and data volume.
Choose the model route
Pick API, regional cloud, or local model after comparing the workflow needs.
Scope the server
Size compute, memory, storage, backup, and networking to the actual workload.
Document the path
Record hosting, providers, access, backups, updates, and the support route.
The right infrastructure question is what the workflow needs, not what the model can do.
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.
Do we need a GPU?
Not for every workflow. Document retrieval and many drafting tasks run without one. Local large models usually need one.
Can we use our existing server?
Often yes, if it has enough compute, memory, storage, and a supported runtime. We check before promising.
Who maintains the infrastructure?
We deploy and hand over with a runbook. Annual support can cover monitoring, updates, and normal care.
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 constraint
What must the deployment stay inside?
Tell us the residency, offline, hardware, or budget constraints. We will scope the smallest infrastructure that does the job.