KKOS turns one conversation into an 11-dimension model. These five engines consume that model. Each is a separate mechanism with its own input, its own output, and a claim you can check.
Every engine below takes the same thing as input: a model that already carries evidence and a confidence value. None of them infer a personality from a photo, a job title or a form.
What does this person look like in one readable paragraph — and which evidence lines produced it?
How did this relationship reach today — and what did each interaction leave behind?
Which change actually caused which — rather than merely moving at the same time?
How do these two particular people react to each other — not how do they each score alone?
How far can this relationship travel, and where are the turning points and risks?
What happens when you tell it that a trait is wrong?
This is what makes the pipeline auditable: there is exactly one place where a person becomes data, and every downstream engine has to point back to it.
We would rather state something falsifiable than something flattering. Every row below is designed so that a failing result would be visible.
| Engine | The claim | How it would be falsified |
|---|---|---|
| Personality capsule | Removing every evidence line changes the capsule, because the summary is derived from them rather than written beside them. | A capsule that stays identical after its evidence is stripped. |
| Relationship memory | The same two people, re-interviewed later, produce a memory state that differs from the earlier one. | A memory representation that ignores timestamps and returns the same state. |
| Causal insight | Reversing the time order of the inputs changes the direction of the detected influence. | An undirected result that comes out the same either way. |
| Dyadic matching | A is compared with B and B with A gives the same resonance map, since both sides carry their own uncertainty. | An asymmetric result where swapping the two people changes the score. |
| Relationship forecast | The curve widens when the input model has low confidence rather than narrowing to a confident single number. | A forecast that reports one precise number regardless of input confidence. |
| Correction loop | Marking a dimension wrong moves its weight in the next model, and the correction log records who changed it and when. | A correction that produces no revision bump and no log entry. |
Engine names and their abbreviations are ours; the underlying methods draw on published work in temporal graph networks, causal discovery and survival modelling.