Choose for the question
Compare local and hosted models on the client's own questions, context length, languages, documents, latency, and acceptable error.
Open-source AI / models, tools, and ownership
Open source is a component choice, not the deployment plan.
Open models and tools can give a business more control, flexibility, and choice. They also leave you responsible for evaluation, updates, security, performance, licenses, and support. We connect the open-source choice to a real workflow and a testable operating plan.
Model choiceReal workloadLicense reviewOperating plan
Pattern demonstration / model choice varies
What open source can change
Choice is useful when someone can carry it.
The business benefit comes from matching the model and tooling to the workload, not from using an open label as a substitute for testing.
Own more of the path
Open interfaces, runtimes, retrieval layers, and tools can make the architecture easier to inspect or move.
Plan for the work after install
Updates, vulnerabilities, licenses, evaluation, storage, monitoring, and support remain part of the system.
The hidden workload
Free software still has an operating cost.
Evaluate the answer
A model that looks impressive in a demo may fail on the client's tables, long documents, languages, or edge cases.
Check the license
Model, runtime, plugin, and tool terms need review before a commercial deployment is bundled or redistributed.
Own the change
Someone must monitor dependencies, update safely, retrain users, and roll back a version that breaks the workflow.
From enthusiasm to a system
Prove open-source AI on the work that pays for it.
Name the job
Choose the workflow, sources, users, output, and failure cost before choosing a model or runtime.
Build a comparison set
Use representative questions and expected answers to compare open, hosted, and API-backed options fairly.
Review the boundaries
Check license, data path, access, provider terms, monitoring, backups, and the support person.
Deploy the smallest useful version
Start read-only or draft-only, record failures, train the administrator, and expand only after the test set holds.
Open source can change who controls the stack. It does not remove the work of making the stack useful.
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.
Is open-source AI always private?
No. Privacy depends on where the application, retrieval layer, model, logs, backups, and support paths run.
Can you install Ollama or another runtime?
We can assess and configure approved tools as part of a scoped deployment. The client's workload and the tool's current terms decide whether it belongs.
Do we need to fine-tune an open model?
Usually not for a first document or workflow assistant. Retrieval, source quality, permissions, and evaluation often matter more than fine-tuning.
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 model question
What does open source need to do for your business?
Tell us the workflow, questions, data boundary, and team that will operate the system. We will test the choice against the real job.