Answer from documents
The system searches the approved corpus first and only drafts from what it finds.
Reliability / grounded AI
The model can be wrong. The system does not have to be. Make the source the answer's passport.
Prevent fabricated answers by grounding the system in your documents, forcing citations, allowing it to say it does not know, and testing against real examples before anyone uses it. Every deployed workflow ships with those controls.
Grounded retrievalSource citationsNo-answer ruleEvaluation set
Pattern demonstration / same workflow, synthetic material
Fixed-scope setupClient-owned accountsDraft-and-review firstFull handover
Where hallucinations come from
Open-ended generation is the risk.
When a model answers from memory instead of evidence, confident errors sound reasonable. The fix is to change the question from memory to retrieval.
Show the source
Every answer links to the source file and the passage that supports it.
Test the edge cases
A set of real questions catches wrong answers, missing context, and false confidence before go-live.
The reliability boundary
The system reduces risk. It cannot remove judgment.
Saying I do not know is correct
The workflow is configured to decline when the source is missing, ambiguous, or stale.
Human review stays final
High-stakes outputs keep a named reviewer and approval step.
Sources need refresh rules
Stale or conflicting documents are identified in the review pack rather than silently merged.
A first grounded workflow
Build the guardrails before the interface.
Scope the corpus
Define which documents are authoritative and which are excluded.
Set the answer rules
Require retrieval, citations, and a no-answer route for gaps.
Build an evaluation set
Collect the real questions, expected source, and review notes.
Test before handover
Run the evaluation, fix failures, and document the remaining boundaries.
The best way to prevent AI from making up answers is to stop asking it to answer from memory.
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.
Can grounded AI still be wrong?
Yes. Grounding reduces risk but does not eliminate it. That is why citations and human review remain part of the workflow.
How many test questions do we need?
Enough to cover the question types and edge cases your team actually hits. We start with the real ones from discovery.
Can it answer questions not in the documents?
It is configured to decline or say it needs a source, rather than inventing an answer.
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 failure
Which answers need a source attached?
Tell us the documents, the questions, and the mistakes you cannot afford. We will scope the grounded workflow around them.