Cortex is the predictive layer you embed inside your AI agent. It turns your client's first-party data into a twin your agent can query over a stateless API, then runs one loop: predict what's about to happen, simulate the change before it ships, and monitor the outcome to prove the action worked. Every prediction comes back with a confidence interval; every outcome feeds the next model. This page walks the whole thing, end to end.
Cortex turns your client's data into a twin your agent can query over the API: simulate a what-if (blue), act on the forecast (jade), monitor outcomes and drift (amber), then learn and predict again. Telemetry from each action becomes a labeled outcome, and when drift shows up, the model retrains on it automatically. Entities, actions and roles: the same model behind every job.
Every Cortex forecast is built from three kinds of twin: Entity, Action, and Role. It's the same model behind every job your agent does: the things it acts on, the changes it runs, and the goal it's accountable for.
Your client's customers, SKUs, accounts, or assets become queryable twins built from their real data. Your agent can segment, classify, and predict against them, the risk-free sandbox for every what-if it wants to run.
A task, offer, message, or process change the agent ships becomes a testable action. Replay it across the entity population in simulation, compare scenarios, then push the winning one live when the numbers hold.
The agent is a Role Twin: it owns a goal, watches every entity and action, alerts on drift, recommends the next move, and feeds real outcomes back so the next forecast is sharper than the last.
A Cortex model moves through three stages over its life. You don't need labeled data to start exploring, but you do need roughly 200–500 labeled outcomes to leave cold start and ship calibrated predictions. After that, the model never really stops learning.
Point Cortex at first-party data and it trains a calibrated model. Cold start needs ~200–500 labeled outcomes; below that, predictions ship wider confidence intervals and Cortex tells you so.
Your agent calls the simulate endpoint to replay an action across the entity population and read a distribution of outcomes, with confidence intervals, not a guess. The change fails safely here, where it costs nothing.
In production the model serves predictions, watches the outcomes that come back as telemetry, and retrains automatically when drift crosses threshold. Every labeled outcome makes the next forecast better.
In football, expected goals (xG) scores chance quality, not just shots taken, a tap-in and a half-court heave both count as one shot, but only one was ever going in. Cortex does the same for your agent's work. Completion is the shot count: the agent finished the task. Expected Outcome is the chance quality: the probability the action actually worked, normalized by task type so a hard task and an easy one are scored on a fair scale. No failure signal inside the labeling window counts as a success.
Completion tells you the agent took the shot.
Expected Outcome tells you whether it was ever going in.
Get an API key, point your agent at the endpoint, and ship demand, revenue and outcome forecasts, with what-if simulation and drift monitoring built in, under your brand.
Want to talk it through first? Email us at [email protected].