Run closer to the work
A local-capable model can reduce the infrastructure distance between the user, documents, and the assistant.
Self-hosted models / Gemma
Gemma makes local AI more approachable, when the task is chosen carefully.
Google's Gemma family is built as lightweight open models with variants intended for local and cloud use. We help businesses test whether a Gemma version is suitable for their documents, languages, latency, hardware, and privacy boundary before making it part of a workflow.
Open model familyLocal-capableTask evaluationVersion-specific terms
Pattern demonstration / model version varies
Why Gemma is useful to evaluate
A smaller model can make the first experiment practical.
Google positions Gemma as an open model family with local and cloud deployment options. The business question is what it can do reliably for the chosen workflow.
Test the operating tradeoff
A smaller model may be cheaper or faster for a bounded workflow, while quality, hardware, power, and maintenance still need measurement.
Use familiar runtimes
Gemma can be evaluated through supported frameworks and deployment environments; the exact version and terms should be checked before production.
Open does not mean automatic
The model still needs a business system around it.
Test the actual documents
A model's public benchmark or marketing description does not tell you how it handles your questions, files, language, and citations.
Review the responsible-use terms
The current model card, license, safety guidance, and deployment controls belong in the selection record.
Plan updates and failures
Local inference still needs model updates, backups, monitoring, access, evaluation, and a person who can repair the workflow.
A self-hosted Gemma test
Make accessibility a measured advantage.
Choose the bounded job
Pick document search, summarization, drafting, classification, or another task with a clear input and output.
Test the current variant
Evaluate quality, context, speed, memory, hardware, language, citations, and failure behavior on real examples.
Set the offline path
Define which model, sources, interface, updates, telemetry, and connectors stay internal or require a network.
Deploy with an owner
Configure the runtime, retrieval, access, monitoring, backups, and model update process in the client's environment.
Gemma can make local AI easier to try. The business value still comes from the workflow that survives the test.
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 Gemma free for any commercial use?
Google publishes model terms and responsible-use material, but the applicable version and terms should be checked at deployment time.
Can Gemma run without internet?
A local setup can be offline-capable after installation. Updates, package downloads, external sources, telemetry, and connectors require deliberate configuration.
Is Gemma better than DeepSeek?
There is no universal winner. The useful comparison is the client's questions, hardware, language, latency, quality threshold, and operating cost.
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 local task
What should Gemma handle for your team?
Tell us the documents, questions, hardware, offline requirement, and acceptable output. We will define a fair model test.