Match the task
Compare the model's quality, speed, and size against the workflow's documents and output needs.
Model decision / Llama
Llama is a model family, not a product. The business value is in the workflow around it.
Open-weight Llama variants can run on client-controlled infrastructure for search, drafting, and structured extraction. We confirm the current variant, licence, hardware, and quality before deployment, and keep the documents and retrieval index on the client's side.
Open-weight modelClient-owned pathSource citationsLicence review
Pattern demonstration / same workflow, synthetic material
Fixed-scope setupClient-owned accountsDraft-and-review firstFull handover
When Llama makes sense
Control is the reason, not open source itself.
Llama is useful when the buyer wants an open-weight model on their own infrastructure, but the decision still depends on task, hardware, and licence.
Scope the hardware
Confirm compute and memory for the chosen variant, users, and latency.
Keep the boundary
Documents, retrieval, permissions, and logs stay on the client's environment.
The model boundary
The model is a component, not a compliance guarantee.
Current terms win
The licence and allowed use are confirmed against the official text before deployment.
Open weight is not automatic quality
The model is tested against the team's real questions and sources before go-live.
The knowledge base stays portable
The retrieval index and workflow can move to another model without losing the corpus.
A first Llama decision
Start with the workflow, then the variant.
Name the output
Pick search, drafting, extraction, or classification with a clear success test.
Review the model
Confirm the current variant, licence, hardware, and quality against the task.
Build the grounded setup
Connect the client-owned corpus, retrieval, citations, and permissions.
Test and hand over
Run real questions, fix gaps, and deliver the runbook and support route.
Llama puts the model in your hands. The workflow decides whether that helps.
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.
Is Llama free to use?
Open weights do not mean free in every sense. The licence, hardware, support, and operational cost are reviewed before deployment.
Does it need a GPU?
Some variants run on CPU or modest hardware; larger variants need more memory or a GPU. We scope to the workflow.
Can we switch models later?
Yes. The documents, index, and workflow stay yours, so the model can be swapped without rebuilding the knowledge base.
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 task
Which workflow should a Llama deployment run?
Tell us the output, document set, users, and hardware constraints. We will confirm whether Llama is the right model before quoting.