Detect the source
Receive files, exports, database changes, website updates, or API events through an agreed connector or schedule.
Private AI pipeline / source to answer
The model is not the pipeline. The source has to keep moving.
A private AI pipeline turns changing files, databases, websites, or business systems into searchable, permissioned, testable context. We design the ingestion, parsing, OCR, metadata, indexing, update, evaluation, and failure path around the source the client actually owns.
Ingest and parsePermission metadataVersioned indexRetrieval tests
Pattern demonstration / pipeline varies
What the pipeline does
A file change should not quietly break the answer.
The pipeline is the update loop between a source of truth and a workflow people rely on.
Make the context searchable
Parse, OCR, clean, chunk, tag, embed, and preserve source and permission metadata.
Update safely
Test retrieval, publish a new knowledge version, keep the last known-good index, and alert someone when the run fails.
A pipeline has failure states
Reliable ingestion is more than a green sync icon.
Keep versions visible
Know which file, page, row, or record supplied the answer and whether it has been superseded.
Preserve permission metadata
A new index must not make a restricted document visible to a user who could not access the source.
Test after the update
Run known retrieval questions, detect missing or stale answers, and keep a human route for a failed publish.
A private pipeline review
Trace one source all the way to the answer.
Name the source and update
Choose files, Notion, website, database, API, inbox, or another system and define how often it changes.
Define the record
Set source, page, date, department, matter, permission, version, and metadata fields needed for retrieval.
Run the retrieval test
Check parsing, OCR, indexing, citations, permissions, stale versions, missing documents, and not-found behavior.
Publish and monitor
Keep a last known-good version, alert on failures, document the operator, and review the pipeline after source changes.
A private AI pipeline is the difference between an assistant that knew the file once and one that can keep using the source responsibly.
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 this just a vector database?
No. A vector store is one component. The pipeline also needs source detection, parsing, metadata, permissions, versions, tests, publishing, and failure handling.
Can it update from Notion or a website?
It can use a supported synchronization, crawler, API, webhook, or scheduled process. The source terms and update behavior need review.
What happens when ingestion fails?
The last known-good version should remain available, the failure should be visible, and someone should own the correction before a new version is published.
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 source
What should your AI knowledge pipeline keep current?
Tell us the source, update pattern, users, permissions, and questions. We will map ingestion through retrieval and evaluation.