Run the model
Choose API, regional, local, or multi-model serving based on quality, latency, cost, privacy, and support.
AI infrastructure / the system behind the assistant
AI infrastructure is more than a GPU and a model.
A business AI system needs a model path, retrieval or data layer, identity, storage, network, logs, evaluation, backups, cost controls, and an operator. We help choose and deploy the smallest infrastructure that can run the workflow reliably.
Model servingData and identityMonitoringClient-owned path
Pattern demonstration / infrastructure varies
What sits behind the answer
The user sees a reply. The system has a whole path.
Infrastructure becomes useful when every layer supports the workflow and the person responsible for it.
Keep the data useful
Handle files, metadata, embeddings, logs, backups, versions, and access without creating a second hidden archive.
See the system work
Monitor failures, latency, spend, retrieval quality, provider behavior, updates, and the events a human needs to investigate.
Infrastructure decisions
Choose the operating burden deliberately.
Use managed building blocks
Client cloud and provider services can reduce physical operations while preserving a documented account and data path.
Keep sensitive layers close
Retrieval, storage, or inference can sit in a client-controlled environment when the requirement justifies it.
Plan for the actual load
Users, context, concurrency, source updates, actions, backups, and support determine infrastructure more than a model brand.
A business infrastructure review
Start with the workflow diagram.
Map the user path
Show interface, identity, question, source, model, tool, output, approval, and record of what happened.
Measure the workload
Use questions, documents, users, latency, concurrency, update frequency, and expected actions to size the system.
Choose the ownership boundary
Decide which accounts, providers, regions, hardware, logs, and support paths the client will own or approve.
Write the operating plan
Record backups, updates, monitoring, evaluation, incidents, costs, credentials, and the person who keeps it running.
Infrastructure is the part of AI that appears when the demo ends and people start depending on the result.
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.
Do we need a GPU?
Not always. A provider API or client cloud can be the simpler path. Hardware follows the measured workload and the actual data constraint.
Can you design infrastructure for an agent?
Yes. The scope can include identity, connectors, model serving, retrieval, queues, logs, evaluations, backups, and approved actions.
Do you operate the infrastructure forever?
The client owns the agreed accounts and operating path. Annual support can cover updates, monitoring review, fixes, and priority response when requested.
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 system path
What has to run behind the AI workflow?
Tell us the users, data, model preference, region, actions, and support capacity. We will map the smallest viable infrastructure.