Numerical representation architecture
KCT METHODKnowledge Calibration Transfer — KCT
Calibrating numerical knowledge spaces for interoperability between systems.
KCT treats transferable information as numerical states. Once two numerical spaces are calibrated, the resulting deterministic operator can be applied prospectively to new states.
Mathematical scope
What can KCT connect?
If information can be represented numerically in compatible finite-dimensional spaces, KCT provides a framework for testing deterministic calibration between those spaces.
Affine calibration between explicitly paired, finite-dimensional numerical spaces subject to rank and conditioning gates.
The public module includes a controlled semantic KCT demonstration backed by a small frozen Source System and a separate numerical-vector KCT workspace.
It is not generative AI, fine-tuning, LoRA, distillation, prompt engineering, probabilistic prediction or hidden model selection.
It is deterministic affine calibration between finite numerical spaces. The operator is mathematically defined from independent paired states and solved directly. Rank and conditioning are mandatory gates: if either fails, no operator and no fabricated output are produced.
The mathematical core does not call an external AI provider. The semantic demonstration uses a frozen Source System; the transfer itself is executed server-side by the KCT operator.Public execution · no email · no login
Valid Test Criterion → KCT → Evidence
A valid semantic question must neither contain nor determine its answer. This controlled demo resolves registered private knowledge only through Source System → numerical state A → KCT → numerical state B → target decoder. The numerical utility and vector demonstration remain separate.
- Semantic criterion
- NOT RUN
- Input query resolver
- NOT RUN
- Source knowledge
- NOT CHECKED
- Source representation
- NOT RUN
- KCT gate
- NOT RUN
- Transfer
- NOT RUN
- Target decoder
- NOT RUN
- Decoded output
- NOT PRODUCED
Enter a public question and execute KCT.
Question → private Source System → state A → KCT → state B → decoder
Public question: What number corresponds to NARU-17?
Its answer mapping is absent from the page, DOM and browser JavaScript.
- 1 · PUBLIC INPUTThe question neither contains nor mathematically determines the answer.
- 2 · SOURCE-ONLY KNOWLEDGEOnly the Source System may possess the information required to answer.
- 3 · TRANSFER EVIDENCEThe answer must be recovered only after source representation → KCT → target representation → decoder.
No name, email, account, API key or authorisation is required. Inputs are processed for the execution and are not stored in Analytics. No external inference provider is contacted.
Short distinction
KCT is not imitation.
Knowledge distillation · LoRA · fine-tuning · prompt copying · teacher/student imitation.
A deterministic calibration between numerical representation spaces.
Advanced · mathematical gate
For source dimension n, KCT requires exactly q = n + 1 independent paired states. The augmented source matrix must satisfy full rank and cond₂ ≤ 10⁴.
PASS computes the affine operator by a direct linear solve. FAIL returns no operator: no pseudoinverse, regularisation, truncated SVD or hidden best-effort approximation is substituted.
Enterprise engagement
Technical value first.
Enterprise pricing is based on application scope, deployment requirements, scale, and the value demonstrated in the customer’s use case.
We prefer to establish technical value first. A commercial proposal is prepared after the relevant KCT use case has been evaluated.
Request a Technical EvaluationHow much does KCT cost?
KCT is not priced as a generic software seat. Pricing depends on the application, scale, deployment model, integration requirements, and the economic value of the problem being solved.
We normally begin by determining whether KCT creates a measurable technical advantage in the customer’s specific use case. Once that is established, we can define the appropriate enterprise commercial model.
What process are you currently trying to migrate, recalibrate, rebuild, re-embed, or make interoperable?
What does that process currently cost your organization in computation, laboratory work, engineering time, downtime, or validation?