Large models get better when they learn from real experience. Today that experience is taken quietly, without consent, without payment. We built the open fuel line instead — where every unit of experience has lineage, consent, and a revenue share.
For investors · Lineage Anchored Ontology
Large models reason in probabilities — and they get sharper when that reasoning meets real experience. Every day, users are producing exactly that experience: judgments, corrections, preferences, workflows. Today it evaporates. Our design settles it, prices it, and lets it move — freely and with consent.
A model weighs probabilities, not certainties. What tips the odds in real work is experience — what a user accepted, rejected, corrected, and repeated.
Every working session generates it, whether or not anyone notices. Until now it has been absorbed without the user's consent — and without the user being paid.
Confirmed, consented experience can move between people under rules the user controls: ownership, revenue share, withdrawal. When experience moves freely with consent, it becomes the raw material that makes models better — look at what that process is called, and you will see what we have built.
LAO has been built around this experience fuel from its open-source beginning: it creates an entry point for voluntarily authorized, confirmed experience. The data stays with the user; there is no telemetry; only the portion a user authorizes can enter the loop.
Reusable experience is matched in a marketplace by a user's cognition, habits, and workflow — not by a simplistic pairing of “wrong” and “right” examples. Selecting, passing, buying out, or leaving an experience idle are preference signals.
This view is written for investors. It coexists with LAO's independent-third-party position: users see savings and opportunity; agents see a tool they can verify for their owners.
Experience-card records carry founder approval, a SHA-256 fingerprint, a revenue-share rule, a withdrawal right, privacy review, and provenance.
See the confirmed experience display →Experience is classified by impact: reference-only, decision aid, or runtime behavior. Cognitive experience has a preference firewall before it can affect behavior.
A feedback bus is designed to return hit-rate and stability feedback across the RIS → LAO → RIS loop, with lineage retained for review.
Experience is structured by event domain, property labels, and output destination, so it can be reviewed and routed instead of becoming an undifferentiated data pool.
We state our own design and its evidence. Where an external comparison needs independent confirmation, the status remains visible rather than being converted into a conclusion.
| Dimension | LAO design | External comparison status |
|---|---|---|
| Data sovereignty | Data stays on the user's side; no telemetry; authorized experience is the only entry to the loop — and it is designed to become a user asset: confirmed ownership, revenue-share rules, withdrawal rights, and tradability. | Harvey commits that customer data is not used to train underlying models and requires zero data retention by model providers (Harvey security page). That stance is protection; LAO's design adds assetization — authorized experience is something the user owns, earns from, can withdraw, and can trade. |
| Ownership and revenue share | Confirmed experience cards are designed with approval, fingerprint, revenue-share rules, withdrawal rights, and privacy review. | Public information has not shown a user-experience ownership or revenue-share mechanism at Harvey: its customer data serves that customer's own requests, and bespoke model training happens only on explicit request. In LAO, confirmed experience is designed to be shared across users with revenue sharing and withdrawal. |
| Collection method | Source collection begins at the authorized, confirmed-experience entry point; marketplace choices provide preference signals. | Interview account: feedback is used to retrain models. |
| Openness | Apache-2.0: the recipe is open while users retain control of their experience. | Harvey is closed-source. |
External comparisons follow the external verification receipt registered on 2026-09-01 (No. 105): each comparison either cites public sources or is stated as not found in public information. Interview accounts remain labeled as such; they are not presented as settled facts.
LAO is the first product, shipping now. The same settled experience feeds the next two — each one fine-tuned on experience that has been confirmed and authorized, not scraped.
The independent third party between the LLM and the Runtime: it settles experience and keeps it on the user's side.
In preparation: help for a real shop, fine-tuned on settled experience — grown out of actual store operations.
In preparation: coaching to be fine-tuned on real training floors — the next step after experience settles.
The marketplace is currently in its display-and-education stage. Its first real match or transaction will be the point at which the preference loop ignites — not a milestone we claim has already happened.
And this is not a slide deck: our founder has run real fitness businesses for ten years, including one gym that has stayed open for seven years in Dongguan. This story did not start in a lab. It started with one wish: let AI be real employees in our own gym — run the store, and turn members’ healthy habits into assets that belong to them. When we tried, today’s AI could talk, but it could not be trusted with a real business. So we stepped back and built from the ground up: store operations, then behavior capture, then the lowest problem of all — agents themselves are not reliable. That is why we built LAO. Without reliable agents, AI will never be able to run a store.. ZWISERFIT is built as a human-and-AI operating team — the same symbiosis that keeps a real gym running every day.