What we built: AI to strengthen crop collection strategy

In the agricultural sector, commercial planning often focuses on the same small subset of clients, leaving a large share of opportunities untapped. This proof of concept started with a simple question: What if a collection manager could analyze more than just the 20% of clients they consider today?

The team envisioned an AI-powered tool that enhances, rather than replaces, the collaborator’s expertise, enabling broader, faster, and more accurate commercial plans by combining historical data, climate signals, and business context.

The challenge: Bringing the invisible 80% into the commercial plan

Today, building a Commercial Plan (CP) is a manual, limited process, highly dependent on individual judgment. As a result:

• Only around 20% of clients are consistently considered, usually the same ones

• Opportunities to anticipate purchases, sales, and input needs are missed

• Planning is slow, fragmented, and difficult to update

The opportunity was clear: strengthen the collection manager’s role by enabling them to plan for the full client universe, not just a fraction of it. So, the guiding question of the POC was: How can AI expand the analyzed client base, accelerate planning, and improve decision quality?

How we built it: An intelligent agent that generates commercial plans

The solution combines AI, climate data, and business context to generate a complete Commercial Plan from just a few inputs provided by the collaborator.

AI-powered commercial planning module

Built on a defined business unit structure, the agent generates CPs for each collection category (soybean, corn, inputs), integrating relevant operational and commercial data.

Climate data that matters

• Historical rainfall records

• NASA API integration for climate forecasting data

These inputs enable more realistic production scenarios and better alignment with each campaign cycle.

RAG-based architecture with vector database

The agent leverages Retrieval-Augmented Generation to:

• Retrieve historical client information

• Contextualize it by collection site

• Deliver precise, personalized recommendations

A complete plan from a single prompt

The collaborator enters a small set of parameters. The agent returns:

• A concise historical analysis

• A suggested commercial plan aligned with objectives

• Actionable recommendations

The manager validates and fine-tunes the output. Planning, tracking, and updates become significantly faster and more consistent.

Demonstrated impact: Expanding the commercial horizon

• Expansion of commercial coverage from 20% to a potential 60% of clients

• Smarter planning powered by historical climate data and NASA forecasts

• Reduced operational load, with AI generating and summarizing plans

• Faster, better-informed decisions based on real contextual data

A more strategic approach to crop collection, with AI augmenting human expertise and unlocking a new way of thinking about commercial planning.