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From deep insight to exponential growth

Our novel agritech solution allows you to make informed decisions and build a robust, resilient, and profitable future for global agriculture.

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We build a digital model of the landscape you operate in, including infrastructure, assets, and people. We dynamically update the model with data from satellites, sensors and auxiliary sources to reflect the state of the system, then run thousands of simulations to identify the optimum solution to your problem. Even where data are lacking this innovative approach means we can still deliver valuable insight.

Our computational simulations provide critical and predictive insights for decision-making in land dependent industries. We help farmers improve logistics and optimise yield, and provide risk assessment and analysis for supply chain companies, credit providers and insurers.

Agriculture

We help you monitor and predict what affects your operations, risk exposure, and investment strategy, and provide you with insight into how to optimise the outcome.

Harvest planning
Resource utilisation
Supply chain integration
Risk prevention

Finance

Information asymmetry prevents large credit providers from serving the smallholder agriculture sector. We provide unprecedented insight with our unique combination of remote sensing and agent based modeling.

Due diligence
Investment object ranking
Key performance indicator monitoring
Predictive analytics

Insurance

We facilitate smallholder access to insurance by providing insights into yield, inputs and behaviours. We look to work with insurers and reinsurers.

Due diligence
Key performance indicator monitoring
Predictive analytics
Portfolio overview

Agent based modelling

Simple rules combine to deliver

insightful, emergent

outcomes.

Agent based modeling (ABM) underpins our computer simulations, and means we deliver uniquely predictive and prescriptive insights. In this methodology, individual agents follow prescribed behaviour rules, and interact in a shared spatial environment which evolves over time. Integrated remote sensing and GIS layers define the spatial environment that agents operate over, respond to, and modify.

Agent based modelling allows for users to be involved in its design, as it is driven by intuitive rules, and it handles varied data and behaviours. Outcomes emerge from these bottom-up rules, and one can test policy effects in advance – and thus find the best policies among many options.

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