Product / KynticAI Importance Kernel
KynticAI Importance Kernel
Licence-ready importance scoring for truth-oriented models and lower token use.
Score facts, preserve provenance, select evidence, and compress prompts before model inference.
Licence proof / Kernel
The Kernel sale is the moment a model team sees prompt noise become a context plan.
This page should make the licence path obvious: the host system sends too much evidence, KynticAI selects what matters, and the host model receives a compact, provenance-aware plan without exposing private scoring internals.
Evidence in
question = Should this account receive a renewal-risk explanation?
facts = support case open, usage drop, renewal date near, sponsor note stale
path_fragment_noise = old campaign notes, generic account history, duplicate CRM comments
Kernel plan
select = support case + usage drop + renewal date
compress = account history into one short governed summary
defer = old campaign notes and duplicate CRM comments
preserve = source references, missing sponsor note, caveat
Host output
Host model receives: renewal-risk context plan with evidence, provenance, and caveat.
Suggested answer shape: explain the risk, ask for latest sponsor note, recommend account-owner review.
Protected boundary: scoring configuration and private tables stay inside the Kernel.
Data in, data out
Importance Kernel gives model teams a clean before-inference step.
The buyer brings a large pile of facts or summaries. The Kernel ranks what deserves attention, keeps provenance and caveats attached, and returns a compact context plan for the host model.
Facts or summaries enter
The host system sends structured facts, governed summaries, matched path evidence, or candidate path fragments into the Kernel boundary.
KynticAI ranks the evidence
High-signal evidence is promoted, secondary evidence is compressed, and weak or stale material can be deferred.
A compact context plan is built
The output keeps the selected evidence, source references, caveats, and missing-data notes together.
The host model receives less noise
The model receives a provenance-aware context plan before it spends tokens on the answer.
Example input
task-output preparation
retrieved facts = 18 source snippets
question = explain renewal risk for account
source mix = CRM note, support case, usage summary, billing status
risk = too many weak snippets for one prompt
Example output
selected_context = support case + usage drop + renewal date
compressed_context = account history summary
deferred_context = old campaign notes
caveat = latest sponsor note missing
Buyer result
The model team sees a licensable layer that turns path-fragment sprawl into a focused context plan before inference.
Problem
Large models consume too much undifferentiated context.
State-matching systems, agents, and internal workflows can hand a model more facts than it should read in one call. Without ranking, source trails, and caveat discipline, the model spends tokens on context that may be weak, stale, repetitive, or missing the decision boundary.
Solution
Score facts before inference.
KynticAI Importance Kernel scores facts, preserves provenance, selects evidence, and prepares a compact context plan before model inference. It supports truth-seeking, evidence-weighted model behaviour with benchmarkable context efficiency.
Buyer spark
The Kernel gives model teams a licensable context discipline layer.
Model providers and private AI teams can add evidence-weighted context selection before generation, while keeping scoring IP, source ownership, and provider adapters behind the right deployment boundary.
The host model receives selected evidence instead of an undifferentiated prompt dump.
Provenance and caveats travel with the selected context.
Deployment benchmarks can quantify the context-efficiency gains for each model route.
Integration
Built for platforms that want to license the layer, not expose the internals.
Embedded Rust library
Link the scoring layer into a host runtime where the platform controls deployment, evidence preparation, and model handoff.
Local sidecar API
Run a local scoring service beside a model gateway, state-routing system, internal model, or private AI workflow.
MCP tool surface
Expose governed importance planning through an MCP tool interface for approved agent and workflow environments.
OEM licence
Package KynticAI Importance Kernel for model providers, AI platforms, state-routing platform vendors, enterprise AI labs, and private model teams.
Token reduction path
Context can become smaller because selection happens first.
The Kernel is designed to reduce undifferentiated context by ranking evidence first, compressing useful secondary material, and deferring low-signal items before inference.
Ranked evidence selection
Promote the facts most relevant to the requested outcome before inference begins.
Medium-signal compression
Compress useful but secondary evidence so it can support the answer without flooding the prompt.
Low-signal deferral
Defer weak, stale, or tangential material instead of spending tokens on every available item.
Caveat preservation
Keep uncertainty, missing-source notes, and review needs attached to the selected evidence.
Privacy and IP
The host model receives context, not private scoring tables.
Kernel licensing is designed to keep scoring IP protected while giving the buyer a clear, auditable context plan for local models, internal gateways, OpenAI-compatible gateways, Anthropic-compatible gateways, and approved provider-adapter routes.
Compiled weight tables
Private scoring configuration can be compiled into the Kernel boundary rather than published in prompts or public output.
Protected scoring values
Examples and handoffs use reason categories, importance bands, provenance, and caveats while private scoring values stay inside the Kernel.
Private scoring tables stay inside the Kernel
The host model receives the context plan while the proprietary scoring tables remain in the protected scoring boundary.
Validation
Validate the scoring boundary for the chosen model route.
Kernel validation can start with deterministic scoring and extend into the deployment's approved model route, including local Ollama, internal model gateways, OpenAI-compatible gateways, and Anthropic-compatible gateways.
Deterministic scoring proof
Local proof can confirm repeatable ranking behaviour for the same approved evidence input.
Leak-resistance checks
Validation confirms prompts and public output carry context plans, reason categories, bands, provenance, and caveats rather than proprietary scoring tables.
Local and provider-adapter proof paths
Proof can be scoped for local Ollama, OpenAI-compatible gateways, Anthropic-compatible gateways, or internal model routes under the deployment's approved policy.
Licence the Kernel
Add importance scoring before your model spends tokens.
Discuss embedded, sidecar, MCP, or OEM licensing paths for a model-provider, AI-platform, state-routing, enterprise-lab, or private-model deployment.