Your knowledge. Your control.
A neurosymbolic engine.
Context assembled for the task.
Connect the source and version.
Apply the relevant conditions.
Show why the work was flagged.
inside knowledge core
Knowledge Core is Primal's neurosymbolic knowledge engine.
It represents concepts, context and rules, the assembles knowledge structures applications can use.
Meaning, relationships and source references.
The subject, selected sources and purpose of the task.
Assemble a knowledge structure for the question or workflow.
Your app or agent uses Knowledge Core as it works.
Trust Core skills guide its development.
Sources and recorded processing steps, where captured.
Architecture illustration. A record of the processing steps shows what the knowledge engine did. It does not reveal an LLM's internal reasoning.
This is the symbolic foundation of Primal's neurosymbolic approach.
Neural models help work with language; explicit concepts and rules provide structures applications can use and inspect.
travel and expense policy ・ illustrative workflow
Several passages mention business class. Only some apply to this employee and this trip.
Application inputs and scope
Employee role
Staff member
Jurisdiction
Canada
Trip type
International business travel
Flight duration
Nine hours
Selected policy
Canadian employee policy, version 3
relevant knowledge core
Applicable: Canadian employee travel policy, version 3.
Inapplicable: US employee policy and superseded version 2.
Condition: Business class requires a flight over eight hours and prior manager approval.
finding and explanation
The flight meets the duration condition.
Manager approval has not been provided.
"For international business travel, staff may book business class on flights over eight hours with prior manager approval."
The selected policy covers this employee trip.
Approval must be confirmed before treating the conditions as satisfied.
Fictional company policies illustrate the workflow. This is not a live integration or a booking authorization. Supported capabilities and integration are confirmed during scoping.
scope and change
A due diligence team receives a document set.
The team chooses which documents to rely on.
Represent its meaning in Knowledge Core.
Prepare answers and check the summary against it.
An amended agreement arrives.
Decide what it changes, then review affected work.
These documents are definitive for this transaction at this stage. The team decides whether they can be used for other work.
Illustrative workflow. The application must support source selection, document changes and human review.
"We're shipping to France."
"Have we included the right instructions?"
The order
The order identifies the product and destination.
YOur approved guide
Your shipping guide sets this requirement for the product and destination.
The packing record
The record lists what has been packed.
Your team chooses which guide applies.
Knowledge Core connects its requirement to the order.
Your app or agent checks the packing record against it.
The packing record lists English only.
"Orders for this product shipped to France must include French instructions."
The order names France as the desination. The packing record lists English instructions only.
Include French instructions before packing.
Show which instructions this order needs.
Surface the missing instructions.
Show the requirement behind the finding.
Your team decides when a revised guide takes effect and which orders it applies to.
Illustrative workflow using a fictional company requirement, not a statement of French law. The packing record is evidence for review; the example does not authorize shipment.
Institutionally sovereign
Knowledge Core is designed to run within your infrastructure.
Your organization owns the knowledge it creates.
You define what is accepted for each use case and when that changes.
Keep concepts and rules outside the model.
Update them as sources and meanings change.
Keep your sources and rules independent of the model.
Connect it to new applications.
Inspect sources and recorded processing.
Identify checks made after generation.
Models can interpret language and reason. Explicit knowledge makes selected inputs and processing manageable independently. Operationsl permissions still required verified identity and enforcement in the application.
Where primal fits
Knowledge Core provides representation and synthesis for apps and agents.
Trust Core skills guide coding agents in building with it.
Reuse the capabilities across your workflows, with updates and technical support from Primal.
RAG retrieves context for generation.
GraphRAG uses graph relationships to enrich that work.
Knowledge Core assembles knowledge representations for a task and supports capabilities beyond retrieval, including content creation, verification and application workflows.
These approaches can work together.
Vector databases provide similarity search and filtering.
Graph systems can provide relationships, reasoning and explanations.
Primal’s proposition is a reusable knowledge engine with connected capabilities, rather than requiring your team to assemble every part.
Compare the actual behaviour, integration effort and operating cost in your workflow.
Primal’s research describes decomposing knowledge representations into elemental concepts and constructing new representations with processing rules as needed.
This includes synthesis beyond facts already explicitly stored.
Evaluate those benefits in the application you intend to build.
No.
Results depend on sources, interpretations, configured rules and the operations controlled.
Knowledge processing rules construct representations; they are not automatically permission controls.
Applications built using these capabilities help people inspect the basis and resolve uncertainty.
Additional Governance pack capabilities remain a development direction.

Expertise, applied.
Across your work.
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