Convert meaning
Documents are split and converted into mathematical representations that preserve context.
Plain English / retrieval technology
A vector database helps the system find meaning, not just keywords.
Documents are converted into embeddings, stored in a vector index, and searched by similarity. You need one when the answers are buried in unstructured documents and keyword search keeps missing them. We handle the index so the team never manages it directly.
EmbeddingsSimilarity searchDocument indexManaged for you
Pattern demonstration / same workflow, synthetic material
Fixed-scope setupClient-owned accountsDraft-and-review firstFull handover
What changes with a vector index
Search stops depending on the exact word.
A contract can say termination notice instead of cancellation clause. Vector search finds meaning, then points the answer to the source.
Store for retrieval
The representations live in a searchable index connected to the original source.
Return the evidence
Answers reference the matching source so the team can verify what the system found.
The vector database boundary
The index is infrastructure. The document is the authority.
Original files stay source
The index points to documents; it does not replace the original file or approval controls.
Chunking affects answers
How documents are split, labelled, and refreshed changes retrieval quality, so it is tested.
The index stays client-owned
The vector index lives on your accounts and can be rebuilt or moved.
A first retrieval workflow
Start with one document set and one question type.
Choose the corpus
Pick the contracts, policies, manuals, or records the team searches most.
Design the index
Decide split size, metadata, permissions, refresh rules, and which fields matter.
Test with real questions
Run the queries the team actually receives and review the citations and gaps.
Hand over the run
Document the index, refresh route, access, and the person who owns source changes.
You do not need to know how the index works. You need to know what it can find.
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.
Do we need a vector database for every AI tool?
No. Some use cases work with structured search or a small index. We scope the smallest setup that answers the question well.
Is this the same as full-text search?
No. Full-text search matches words; vector search matches meaning. Many setups use both.
Do we administer it ourselves?
No. The first workflow is deployed, tested, and handed over with a runbook for normal operation.
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 search problem
Which questions keep getting missed in your documents?
Tell us the document set and the searches that fail today. We will tell you whether a vector index is worth building.