Use the existing stack
Vector search runs in the database the team already manages.
Retrieval infrastructure / pgvector
pgvector keeps vector search inside Postgres. For teams that already run on Postgres, that is the point.
pgvector adds vector search to a Postgres database, so document embeddings live beside the data your team already manages. We deploy it when the operational fit is right and keep retrieval, citations, and permissions client-owned.
Postgres nativeDocument embeddingsOperational fitClient-owned index
Pattern demonstration / same workflow, synthetic material
Fixed-scope setupClient-owned accountsDraft-and-review firstFull handover
When pgvector is the right fit
Fewer systems means fewer moving parts.
If the business already runs Postgres and needs a modest vector workload, pgvector can reduce the operational burden compared with a separate vector database.
Size the workload
Document volume, queries, and latency determine whether pgvector is sufficient.
Keep the boundary
Sources, indexes, permissions, and backups stay on client-owned infrastructure.
The pgvector boundary
A database extension is not a complete assistant.
Workload limits exist
Very large or high-concurrency vector loads may need a dedicated index.
Indexes need tuning
Embeddings, chunking, metadata, and query filters are tested on real documents.
Citations stay attached
Answers point back to the source files, not just to vectors.
A first pgvector project
Start with the document set and query types.
Scope the corpus
Name the documents, metadata, users, and questions.
Design the schema
Define embeddings, source references, permissions, and refresh rules.
Test retrieval
Run real questions and compare results with keyword search.
Hand over the run
Document the index, refresh route, and operating owner.
pgvector is a good sign that the stack is already close to the data.
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 Postgres already?
It helps. We recommend pgvector when the team already runs Postgres and the workload fits.
Can it replace a dedicated vector database?
For many small and mid-size workflows, yes. We test the workload before committing.
Who maintains the index?
The deployment ships with a runbook and a named owner. Annual support can cover normal care.
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 stack
Do you already run Postgres?
Tell us the database, document set, and search problem. We will confirm whether pgvector is the right retrieval route.