Variables that look independent are often deeply connected.

The question

A client needed to know whether movements in global oil prices ripple through agricultural inputs — specifically fertilizer and transport — in ways that materially affect cotton production costs, and whether those effects compound rather than simply add up.

What we did

Partsol deployed AI agents to map the chain connecting oil prices to cotton production, combining data across petrochemicals, fertilizer feedstocks, and transport — tracing the relationships many layers deep and testing how those cost drivers interact under different conditions.

What we found

Fertilizer and transport costs interact non-additively in shaping cotton production costs. That interaction is a critical channel through which oil-price shocks reach the cotton supply chain — a relationship that looks invisible when each input is examined in isolation.

Why it matters

Variables that appear independent are often deeply connected. Partsol's platform surfaces those hidden connections with the depth and sourcing needed to act on them — whether the question is a commodity exposure, a counterparty's hidden risk, or a supplier's true ownership.