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The World Model is Oxygen’s formula-first model of your business. It defines every metric that matters as an equation over its drivers, so that agents and applications can explain why an outcome moved and what to do about it. Everything in the World Model fits into two layers:
  • The physics layer models what the business is: its measures and the equations that define them, the qualitative map of what drives what, and the real objects that produce them.
  • The control layer models what the business does: the versioned decision rules that read the physics and move its levers, together with the feedback loop of forecasts, evaluation, and calibration that scores every change and updates the physics in turn.
World Model architecture. A physics layer (semantic model, metric tree, entity graph) feeds a control layer of versioned controls with anomaly, opportunity, and trigger criteria. A feedback loop of forecasts, evaluation, and calibration updates the physics and learns driver magnitudes.

Controls read the physics and act on the business, while the feedback loop of forecasts, evaluation, and calibration scores every intervention and updates the physics.

A Digital Twin of the physics

Most digital twins model a business from the bottom up, cataloging its machines, parts, and stores so that operators can monitor each one. Oxygen builds the twin from the top down instead, starting at the outcome you are accountable for and modeling downward toward the drivers you can actually move. The result is a set of equations faithful enough to forecast with, backtest against, and use to change what the business produces. Where a twin of the parts tells you the state of everything, a twin of the physics tells you what to do.

The physics layer

  • Semantic Model: the vocabulary. Measures and dimensions are defined once and compiled into correct SQL by airlayer, with no LLM in the compilation path, so the same request always produces the same SQL.
  • Metric Tree: the relationships. Every top-line metric decomposes into the drivers that explain it, which powers root-cause analysis and scenario prediction.
  • Entity Graph: the objects. Measures are tied to the stores, employees, customers, and orders that produce them, so any metric can be sliced, rolled up, and acted on.

The control layer

A control codifies a standing practice, such as “reorder before a SKU runs out,” as criteria that state when to act and an action that states what to do. Because both are written in World Model terms, every control can be backtested against your own history. The same primitive powers three engines:
  • The Control Engine runs the business, executing the decisions the company operates by, such as replenishing, staffing, and pricing.
  • The Opportunity Engine expands it, finding where the business underperforms its own benchmark and sizing the gap in dollars.
  • The Anomaly Engine fixes it, detecting when an outcome breaks from its forecast and tracing the break to its root cause.
Every action then feeds a loop: forecasts set the expected value, evaluation measures what each change actually did, and calibration writes the result back, so the physics the next decision runs on is sharper than the last.

Next steps

Semantic Model

Define measures, dimensions, and entities

Controls

Codify the practices the business runs on

Evaluation

Version comparison and criteria-triggered measurement

Calibration

How the feedback loop keeps the physics accurate