Architecture / KynticAI Importance Kernel
The licence-ready scoring layer before model inference.
KynticAI Importance Kernel accepts structured facts or governed summaries, ranks evidence by outcome relevance, prepares a compact context plan, and hands provenance-aware evidence to the host model before tokens are spent for truth-oriented and evidence-weighted model behaviour.
Core layers
How KynticAI Importance Kernel is structured
01
Structured evidence input
Evidence enters as structured facts or governed summaries under the deployment's source and policy controls.
02
Importance scoring
Private scoring configuration ranks evidence by outcome relevance while protected scoring values stay inside the Kernel boundary.
03
Compact context plan
High-signal evidence is selected, medium-signal evidence is compressed, low-signal evidence is deferred, and caveats remain attached.
04
Host-model handoff
The deployment-owned model boundary receives the right evidence before tokens are spent.
Operating flow
The request path through the product
Select
Ranked evidence selection
Promote the facts most relevant to the requested outcome before inference begins.
Compress
Medium-signal compression
Compress useful but secondary evidence so the model route receives deliberate context.
Defer
Low-signal deferral
Keep weak, stale, or tangential material available without forcing it into every model call.
Caveat
Caveat preservation
Uncertainty, missing-source notes, and review needs remain part of the selected context.
Example signal path
A noisy evidence bundle becomes a compact context plan
Illustrative sample only. Deployment benchmarks can quantify token efficiency and model-quality effects for the chosen route.
01 / Source
Example fields
fact_type = support_signal
summary = usage_drop_after_release
provenance = support + telemetry
caveat = renewal_date_missing
02 / Evidence
What KynticAI creates
importance_band = elevated
reason_category = active_risk + recent_change
low_signal = defer
review_need = commercial_owner
03 / Action
What the business does
return compact context plan
retain provenance and caveats
send selected evidence to host model
Operating model
How the product stays useful at enterprise scale
IP
Compiled weight tables
Private scoring configuration can remain compiled inside the Kernel boundary rather than appearing in prompts or public output.
Privacy
Protected scoring values
Public examples and model handoffs use reason categories, importance bands, provenance, and caveats while private scoring values stay inside the Kernel.
Proof
Leak-resistance validation
Validation confirms prompts and public output carry context plans, reason categories, bands, provenance, and caveats rather than proprietary scoring tables.
Integration points
Where it connects to the wider stack
Rust
Embedded Rust library
Link the Kernel into a host runtime controlled by the platform or private model team.
Sidecar
Local sidecar API
Run local scoring beside a model gateway, RAG system, or private AI workflow.
MCP
MCP tool surface
Expose governed importance planning through approved agent or workflow environments.
OEM
OEM licence
Package KynticAI Importance Kernel for model providers, AI platforms, RAG vendors, enterprise AI labs, and private model teams.