Turn logistics into intelligence
Built to give world-class logistics teams decision advantage
Build a logistics plan that survives reality
From reactive to predictive
Crear turns reactive logistics into predictive outcomes, so teams plan ahead instead of chasing real-world variability.
Scanning demand, lane, and disruption signals before execution drifts.
SEARCHING
Projecting lane-level risk and staging mitigation before SLA impact.
PREDICTED
Resilient and adaptive
Rerun scenarios when demand, constraints, or disruptions change. Get clear mitigation options—not guesswork.
Rerunning your plan the moment constraints or demand patterns shift.
MODELING
Reasoning cost-to-resiliency trade-offs across active disruption scenarios.
COMPUTED
High-fidelity twins
Model your real network—facilities, labor, carriers, lead times, and rules—so simulations match reality, not spreadsheets.
Building a calibrated twin of facilities, labor, carriers, and lane rules.
CALIBRATING
Validating simulated outcomes against live operations and baseline KPIs.
COMPUTED
Why top logistics teams choose Crear
Move fast
Forget months of feasibility studies, spreadsheet sprawl, and project-management overhead. Crear unifies planning into a single streamlined workflow—delegate execution to agents and focus your team on high-value decisions.
Collaborate
Align operations, finance, and procurement around the same assumptions, models, and data. Plan together, evaluate trade-offs in real time, and move from alignment to action without friction.
United States
Mexico
Canada
CBP
SAP
Oracle
Samsara
Context
Import your operational data—contracts, rates, historical performance, and live feeds. Crear equips logistics teams with an intelligence layer to make critical decisions in full business context.
Equipped to do real logistics engineering
Network modeling
Configure facilities, lanes, carriers, capacity, constraints, and service rules in a high-fidelity digital twin of your real network—built for operational accuracy, not slideware.
Interpreting modeling objective and retrieving network configuration.
SEARCHING
Constructing high-fidelity network graph and generating optimization model.
EXECUTING
Scenario simulation
Stress-test demand shifts, cost changes, disruptions, and policy updates. Compare trade-offs across cost, service, and risk before committing to action.
Loading input files for network-model.json with demand_surge and port_closure_ca.
LOADING
Comparing trade-offs across cost, service, and risk impact.
COMPUTED
Auditable/
Track assumptions, version runs, and maintain a clear audit trail. Move from simulation to execution with governance built in.
Tracking assumptions and version runs from demand updates through simulation.
RECORDED
Approve Scenario 5A and publish configuration with executive report.
EXECUTED