AI planning for incident response and quick commerce.

”We help our customers manage unplanned work with machine learning, simulations and scenario planning”

AI Planning for incident response

Improve incident response time by preparing your first responders for the next incident. Stream line your incident response. Make first responders situational aware and connect your traffic control center to you first responders seamlessly.

Predictive patrolling

Inform your inspectors on the best driving directions based on incident risk predictions

Instant assignment

Assign inspectors to incidents automatically or with the click of a button

Workflow automation

Automate the process of assigning incidents and inspector status logging

Machine learning, simulation, planning

Numerico uses its AI planning platform to find the optimal assignments for road inspectors. At the core of the platform is a rapid simulation algorithm that computes thousands of scenarios in seconds, selects the best scenario and turns this into patrolling advice. The system uses prediction and machine learning to simulate incidents and traffic as realistically as possible.

Improved incident response time
Safe valuable time of traffic controllers
Reduce the cost of communication

Picture source: Rijkswaterstaat

AI Planning for
Quick Commerce

Increase your competitive advantage with faster and predictable delivery times. Simulate different operational scenarios for delivery performance and rider utilization.

Scenario simulator

Find the best balance between costs and delivery performance.

Rider assignment

Automated rider assignment for fastest delivery.

Batching algorithm

Smart batching algorithm to support demand peaks.

Machine learning, simulation, planning

Numerico uses its AI planning platform to find the optimal assignments and order batching. At the core of the platform is a rapid simulation algorithm that preselects realistic scenarios, computes thousands of scenarios in seconds, selects the best scenario and turns this into actionable assignments and batches. The system uses prediction and machine learning to simulate as realistically as possible.