Decision AI for agriculture
We build AI and optimization tools for agricultural investment, supply chains and processing facilities.
Our work is measured by margin, throughput, operating cost and return on capital.
Evidence we work with
01 / Capabilities
Projects range from national food-security plans and crop investment to seasonal supply decisions and plant performance.
Plan long-term investment.
We assess crop potential, water, climate, trade and infrastructure before capital is committed.
Where should capital go? Which regions and assets are likely to remain competitive? How much capacity is needed?
Plan the next season.
We forecast demand, yield, quality and price, then use the results to set sourcing, inventory and sales plans.
What should we buy, make, sell or hedge? When and where should the plan change?
Improve output from existing assets.
We use feedstock, process, sensor and maintenance data to find bottlenecks and improve plant performance.
What limits throughput? Which constraint should be removed? How should the plant run as supply and demand change?
02 / Who we work with
Our work covers the full path from inputs and production to processing, distribution and finance.
Seed, fertilizer, crop protection, equipment and agritech.
What will each local market need next season?
Farms, estates, cooperatives and livestock operators.
What should we produce, where and when?
Aggregators, merchants, storage, cold chain, fleets and ports.
How should volume be sourced, stored and moved?
Mills, packhouses, food plants, feed plants and processing networks.
How can existing capacity produce more saleable output?
Brands, manufacturers, traders, exporters, distributors and retailers.
What should we make, price, sell and hold?
Investors, lenders, insurers, governments and development institutions.
Where should capital, insurance and public funding go as climate and trade conditions change?03 / Data and methods
We combine public sources with client data to answer a specific operating or investment question.
Forecasting estimates what may happen. Optimization tests the available choices against physical and commercial constraints.
04 / Our approach
We define the decision, identify the data it requires and build a tool that can be used in day-to-day work.
A processor needs to plan a short harvest when crops ripen at different times, deliveries peak and production lines risk standing idle.
What happened?
Daily volume, ripeness, queues, line speed, downtime and waste.
Why did it happen?
Separate field maturity, fleet, intake, changeover and equipment constraints.
What is likely next?
Forecast ripening, quality, delivery peaks, capacity and demand.
What should we do?
Choose harvest order, crews, routes, intake slots, line schedule and product mix.
05 / Representative experience
These examples are based on projects completed by CubeRoots team members in previous roles. Client names and identifying details have been removed.
Representative engagements. Client identities and identifying details have been omitted. Figures are rounded or presented as ranges.
06 / Client experience
CubeRoots engagements and work completed by team members in earlier roles include:
Names are published only where disclosure has been approved.
07 / Team
The team brings experience in agricultural strategy, data science, optimization, agronomy and engineering.
Our team includes former McKinsey consultants who have worked on national food-security models, crop and demand forecasts, supply-chain planning, field-to-factory operations and facility optimization for agricultural, food and public-sector clients.
Contact
We work on capital planning, supply-chain performance and agricultural processing. Send us a short note on the decision, the constraints and the data available.
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