Revenue Forecasting Model for a US AgriTech Platform

Summary 

We partnered with a US-based AgriTech platform to develop a dynamic revenue forecasting model integrating crop yield data, commodity price curves, and supply chain intelligence. The solution combined agri produce analytics with commodity market modeling to enhance revenue predictability and margin visibility. Our approach enabled institutional-grade forecasting, improved working capital planning, and provided actionable insights for investors and management amid volatile agricultural and commodity market cycles.

Context 

A Midwest-based AgriTech platform specializing in grain procurement and digital farm management tools, with exposure to corn, soybean, and fertilizer-linked commodities, required a robust forecasting framework. Operating across fragmented agri supply chains and exposed to CME-linked price volatility, the company lacked integrated revenue visibility. With increasing institutional investor interest and private capital inflows into AgriTech, management required a model linking yield variability, commodity pricing, and contract structures to forecast revenues accurately.

Identifying Challenges

• High volatility in agri commodity prices driven by weather patterns, global trade flows, and futures curve movements impacted revenue predictability.
• Lack of integration between farm-level yield data and realized pricing created disconnects in forecasting realized revenues and margin outcomes.
• Exposure to input cost fluctuations, including fertilizers and energy-linked commodities, complicated gross margin forecasting and sensitivity analysis.
• Fragmented data across procurement, logistics, and farmer contracts limited ability to build scalable, real-time forecasting and scenario modeling frameworks.

Our Solution

• Developed a multi-layered revenue forecasting model integrating crop yield projections, acreage data, and historical productivity trends, enabling granular forecasting at crop, region, and farmer cohort levels aligned with agri produce industry benchmarks.
• Incorporated commodity price modeling using futures curves, seasonality adjustments, and historical volatility bands, allowing dynamic revenue projections linked to real-time pricing movements across corn, soybean, and related commodities.
• Built an integrated cost module capturing fertilizer, fuel, and logistics inputs, linking commodity-linked cost drivers to gross margin forecasts and enabling sensitivity analysis under inflationary and supply disruption scenarios.
• Designed a contract-level revenue recognition framework accounting for forward contracts, spot transactions, and pricing hedges, ensuring accurate alignment between physical volumes, pricing realization, and reported revenues.
• Enabled scenario modeling across weather shocks, yield variability, and global trade disruptions, providing management with downside and stress-case visibility critical for capital allocation and risk management decisions.
• Delivered interactive dashboards and investor-ready outputs, including revenue bridge analysis, margin attribution, and scenario comparison tools, supporting strategic planning and enhancing transparency for institutional stakeholders and private market investors.

Highlights

• Integrated yield and pricing forecasting engine
• Commodity-linked revenue sensitivity modeling framework
• Farm-level data to revenue mapping precision
• Input cost volatility embedded forecasting structure
• Scenario-driven agri revenue intelligence platform delivery
• Institutional-grade commodity analytics for AgriTech platforms

Highlights Overview:

This engagement highlights deep domain expertise across agri produce, commodity markets, and financial modeling. By combining yield intelligence with real-time commodity pricing and cost structures, we delivered a forecasting framework that enhanced revenue visibility, improved margin predictability, and strengthened investor confidence in a volatile agricultural market environment.

Marking the Transition 

From fragmented agricultural data and commodity volatility to an integrated, data-driven revenue forecasting engine, enabling the AgriTech platform to transition toward predictive analytics and investor-grade financial visibility.

• Data silos to integration
• Volatility to forecast clarity
• Assumptions to predictive analytics
• Complexity to actionable insights

Client Testimonial

Their ability to integrate commodity price dynamics with farm-level yield data transformed how we approach revenue forecasting and investor communication.

CFO

Business Impact 

For AgriTech platforms and commodity-driven businesses, our forecasting solutions bridge operational data with market intelligence, enabling precise revenue visibility and risk management. By integrating yield analytics, commodity pricing, and cost structures, firms can improve capital allocation, optimize procurement strategies, and enhance investor reporting. This approach is particularly critical in volatile agri and commodity markets, where forecasting accuracy directly impacts profitability, liquidity planning, and valuation outcomes.