Frontier models learn from millions of texts. We train AI on users’ actions in millions of apps and systems, in the concepts people understand.
One function, modelled close to the world, grows into a reasoning engine.
Today’s interfaces and APIs are built around device parameters. A washer receives three numbers, while the person knows what the sweater is, what state it is in and why heat is a risk. A system designer using the concept-driven framework models the intent, and the system records the event outcomes whenever an action is performed.
Systems do not register the events.
We provide intelligence from the actions of users and agents.
We use events and outcomes as knowledge.
Every word of an intent takes its place in the categories. Intent and event outcome form one record.
Wash, rinse, dry and clean share objects, means and properties. The space checks coherence and bounds meaning.
Every action, in every space, is recorded as an event in the same categories.
Concepts linked by real events and outcomes. It predicts, carries knowledge across spaces and composes what was never seen.
P-Suite carries an intent through the concepts to the device, and brings back the event outcome.
The device API becomes an expression of the concepts, not a replacement for them. Four components close the loop.
Turns signs in any language into conceptual structure. Language stays at the edge.
Matches the request to this device’s functional space and checks it before anything runs.
Calls the device or system API under an active functional space, deterministically.
Records the event outcome, what actually happened, in the same categories.
Intent and event outcome are kept together in one record. The difference between them is what the organic ontology learns from.
A conceptual layer between people, AI and every system they use.
Once actions are modelled as concepts and recorded with their outcomes, the same layer serves as ontology, middleware and interface, and becomes training data for AI that learns from what people do.
Seven fixed categories, with links grown from real events. Each company’s functional spaces form a private ontology; together they converge toward a universal one.
Conceptual structure sits between every input and every system. One coherent, event-based API across a company’s landscape, with canonical addresses for every event.
Voice, text, touch, gesture and telemetry compile into the same structure, so interfaces can change without changing what the system understands. P-Phone is built with blind users in mind.
Conceptual State Machines are deterministic. Forbidden states are shown unreachable before release, and every decision reproduces exactly for audit.
No party exposes its data model. Shared categories and event addresses make an insurer, a carmaker and a city mutually interpretable, without one vendor owning the schema.
A third path beyond symbolic AI and LLM scaling: reasoning on concepts grounded in what actions do, so knowledge transfers between spaces and composes the unseen.