From Data to Decision – How AI reveals risk at the source

Translating complexity with a common language for risk

Commodity markets move on information. For decades that information has been fragmented, backward-looking and unevenly distributed. Traders and corporates have depended on national yield reports, manual collection or incomplete weather indices – slow, coarse data that masks the true drivers of volatility.

Treefera is changing that. Using AI to synthesize and make sense of rafts of satellite imagery, land records and live weather data, we provide a transparent, real-time view of risk at the source. Uniquely, we are applying the same rigor that underpins financial markets to the physical systems that feed them.

Real-time visibility at field - and portfolio - level

Treefera’s platform quantifies what was once invisible. Our models detect and measure change at the plot level – tracking how yield, weather and land use evolve week by week. This allows risk to be observed where it originates, not inferred months later from price movements.

Each dataset is continuously updated and reconciled against historical benchmarks. The result is a granular, dynamic picture of production that can be aggregated up to portfolios or drilled down to individual farms.

This precision matters because enterprise and financial users make decisions in cycles measured in hours, not seasons - and need both a micro and macro view. When data becomes live and verifiable, reaction time collapses and strategy moves closer to real-time execution.

From visibility to foresight

Real-time data delivers clarity, but forecasting delivers advantage. Treefera’s models combine historical, seasonal and live data to anticipate how yield, weather and land conditions are likely to evolve. This forecasting capability transforms visibility into foresight – enabling users to identify shifts in production, exposure or weather & climate volatility before they occur. By integrating predictive analysis into existing workflows, Treefera helps clients move from reacting to events to preparing for them. Forecasting unlocks a forward-looking view of risk, informing pricing, sourcing and investment decisions weeks or months ahead of market signals.

Financial-grade precision

Treefera’s risk architecture draws directly from the discipline of quantitative finance. In banking and trading, risk is measured in basis points, probabilities and distributions of expected loss. After decades spent in Finance & Automation, Treefera was founded to apply these same principles to the assessment of yield, exposure and resilience across commodities.

By applying portfolio-level analysis to natural assets, Treefera can express commodity risk with financial precision. This means a trader evaluating corn futures and a sustainability officer assessing deforestation risk can both operate from the same data foundation. Risk becomes measurable, comparable and priced with consistency.

Transparency that builds confidence

We recognise that for data to be actionable, it must first be defensible. That standard has rarely applied to commodity data. Anecdotal evidence and manual collection methods lack the rigour needed to guide investment or capital flow.

This is why every Treefera dataset carries full lineage – allowing users to trace how information was sourced, weighted and verified. This transparency turns complex data pipelines into defensible intelligence that meets enterprise audit standards and regulatory requirements. Through APIs and data feeds, clients receive continuous updates on commodity yield, regional performance and risk distribution that they can immediately use in their own analysis and forecasting. The same insights that inform trading desks can underpin procurement strategies or compliance audits.

This integration between physical and financial intelligence is what makes Treefera distinct. We are building the data infrastructure that allows risk to be measured, forecast, priced and managed with the same precision across both domains.