Use data to optimise micromobility operations, reduce costs and increase revenue.

We make micromobility companies more profitable through demand predictions and route optimisation.

Our technology has been featured in

Join our mailing list

Stay up to date with the newest product features, case studies and industry news.
Learn about beta testing and exclusive collaboration opportunities.

Boost rides by up to 30 %

You want more rides but rebalancing is expensive. We help you optimise your fleet so you can boost overall rides while focussing on the rebalancing interventions that are profitable. 

With Lanterne you get
  • 30 % more rides on fleet level and
  • more than double the rides per vehicle per day after a rebalancing intervention.

Know demand patterns ahead of time

We provide you with detailed demand predictions to facilitate smart mobility. You’ll know exactly when and where you should be distributing micromobility vehicles to increase the number of rides per day. With Lanterne you get demand predictions:

  • through an API, dashboard or emails – tailored to suit your operations
  • on an hour-by-hour basis
  • down to the street-level

Gain a thorough understanding of your business

To steer your operations effectively, you need to understand what’s going on. Our KPI dashboard gives you new insights into operational efficiency, fleet performance and the success of your rebalancing strategy. This helps you understand when and why things are going wrong and allows you to take immediate action.


Get the most valuable tasks done

Our smart tasks solution prioritises the highest impact battery swapping and rebalancing tasks. Get your fleet ready to capture as much demand as possible.

Reduce cost per task

Our innovative route optimisation system will help you to get all your highest value tasks done in the most efficient way possible.

We distribute tasks efficiently between your operations vehicles and dynamically optimise the route to increase task density and decrease your operations costs.


How it works

We ingest your utilisation data and combine it with external data to train our demand prediction model. We then use the state of your fleet, and the demand predictions to create and prioritise operations tasks.

A route optimisation system then helps you to get the most valuable tasks done in the most efficient way possible.

Environment data
E. g. weather, public transport, events
Internal data
Utilisation and supply data
Forecast demand
ML model to predict demand
Manage tasks
Automatic task creation and prioritisation
Optimise route
Calculatig the most efficient route

Our solution is easy to integrate

The operator provides access to their rides and supply data feed (e. g. a MDS feed).

Lanterne generates demand predictions and actionable fleet optimisation recommendations.

The operator receives predictions and recommendations through our dashboard or an API.

The operator rebalances the fleet to close the demand-supply gap.

Lanterne's Covid-19 response

Lanterne developed Crowdless, a free platform with global coverage that helps people with social distancing. It provides real-time data on how crowded supermarkets are so that people can avoid crowds, choose the least busy place and best time to visit. The platform uses a combination of existing data sources and crowdsourced data in combination with machine learning.

“I have been promoting Crowdless in my community, because I am sure there are lots of people like me who prefer to avoid queues and crowded shops.”
Community Ambassador from Oxford, UK​
“The Crowdless app enables users to provide live updates of the busyness of supermarkets. Being able to choose favourite stores is also a great feature which will empower users to find information related to the busyness of the stores which they mostly buy from.”
Community Ambassador from Cape Town, South Africa
“I strongly recommend using the Crowdless app… which is really innovative since it allows people to interact by updating with real-time information on how crowded stores are.”
Community Ambassador from São Bernardo do Campo City, Brazil​