Keep the inference route local
A client may need documents and model requests to remain inside a physical or network boundary that external APIs cannot meet.
On-premise AI / physical control
On-premise AI is a data-path decision. The hardware comes second.
On-premise makes sense when a business has a real requirement for local inference, physical control, procurement separation, or restricted network paths. We assess the workload first, then specify a deployment that someone can operate after installation.
Local inferenceMeasured workloadHardware separateOperating plan
Example only / architecture varies
Why teams choose on-premise
Local control has a cost as well as a benefit.
A serious on-premise decision includes power, cooling, updates, model performance, support, backups, and failure response.
Fit a hard environment
Some organizations have approved hardware, network, or vendor rules that make a local deployment easier to govern than a new cloud path.
Own the work after install
Someone must patch, monitor, back up, evaluate, and replace the system. Local control is not the same as zero maintenance.
The questions before the GPU
A server is not an operating model.
How many real users?
Concurrency, response time, context size, document volume, and model choice determine the workload.
What must the model answer?
Test the client's questions before promising that a local model will match a frontier provider.
Who owns the incident?
Define updates, backups, rollback, physical access, monitoring, and escalation before calling the setup finished.
From constraint to deployment
Prove the workload before you buy the room.
State the no-external requirement
Separate a true physical or network requirement from a general preference for privacy or ownership.
Build a representative test set
Use real questions, documents, tables, images, users, and expected response times to define the job.
Compare local and regional options
Price hardware, operations, provider alternatives, latency, support, storage, and failure recovery as one decision.
Write the runbook
Record installation, access, model updates, backups, monitoring, evaluation, and the person who can operate it.
The point of on-premise is not owning a GPU. It is satisfying a real boundary without creating an unmaintainable machine.
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
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.
Do you sell the hardware?
Hardware is scoped separately and should follow a measured workload, signed scope, and deposit. The first service is architecture and deployment, not inventory.
Can an on-premise system use an external model?
Only if the approved data path permits it. If no document content may leave the environment, the model and retrieval path must be designed accordingly.
Is on-premise always more private?
It can provide stronger physical control, but privacy still depends on accounts, access, logs, backups, support paths, and the people who operate the system.
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 boundary
What must stay on your premises?
Tell us the network, data, workload, and support constraint. We will help determine whether on-premise is justified.