RAG and embeddings
RAG grounds answers in documents; embeddings let the system search by meaning.
Reference / plain-English terms
Private AI without the jargon tax. Definitions that help you decide.
A short glossary of the terms that appear in procurement, vendor decks, and implementation notes. The goal is not to make you a developer. It is to make the purchase decision clear.
RAGEmbeddingsSelf-hostingFine-tuning
Pattern demonstration / same workflow, synthetic material
Fixed-scope setupClient-owned accountsDraft-and-review firstFull handover
Terms grouped by decision
Jargon matters when it changes the contract.
The terms below map to practical choices: where the model runs, how it finds documents, and who owns the result.
Self-hosted and managed
Self-hosted runs on your infrastructure; managed AI runs on a vendor's service.
Fine-tuning and prompt
Fine-tuning changes model behavior on examples; prompting guides behavior at run time.
The glossary boundary
Definitions explain. They do not decide.
Terms change
Model names, licences, and features change; current details are confirmed at implementation.
One page, one core
This glossary covers decision terms, not a full technical manual.
The business owns the choice
Definitions help, but the workflow, data, and review rules decide the right setup.
How to use the glossary
Look up the term in the context of a decision.
Find the term
Use the FAQ below for the definitions that appear in most conversations.
Connect it to the choice
Ask what changes: data location, cost, maintenance, or answer quality.
Bring it to discovery
Use the term in the lead form so the scope is precise.
Get the plain-English plan
We explain the architecture and what it means for the workflow.
The buyer who understands five terms can have a much better AI conversation.
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.
What is RAG?
Retrieval-augmented generation: the system finds relevant documents first, then drafts an answer from that evidence with citations.
What is an embedding?
A mathematical representation of text that lets the system search for meaning and similarity.
What is a vector database?
A store built for similarity search over embeddings, connected to the source documents.
What is self-hosting?
Running the system on your own infrastructure rather than inside a public AI service.
What is fine-tuning?
Additional training on selected examples to change a model's style, format, or behavior.
What is an LLM?
A large language model: the component that generates text. It is a swappable part of a private deployment.
What does client-owned mean?
Accounts, infrastructure, documents, indexes, and credentials are under the client's control.
What is a data path?
A documented description of where data goes, who processes it, where it is stored, and how it is deleted.
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 term
Which term is blocking the decision?
Tell us the term and the workflow. We will explain it in the context of your setup and recommend a first step.