Agri trade os

Inspiration

Agri Trade OS was born from a problem that is personal to our team. Both of our fathers are small-scale farmers, so agriculture is not an abstract problem to us — we have seen firsthand how difficult it can be for small producers to access better markets and navigate the fragmented processes involved in selling agricultural produce.

This made us ask a simple question: If farmers already have the produce, why is it still so difficult to turn that supply into a larger, reliable trade opportunity?

As we explored the problem further, we identified a broader gap beyond simply finding buyers. Small and fragmented lots often need to be aggregated, quality and requirements need to be verified, documentation and compliance need to be handled, logistics need to be coordinated, and the economics of a shipment need to make sense.

That led us to the idea of Agri Trade OS — an AI-powered trade orchestration platform designed to connect these fragmented steps and help transform farm supply into buyer-ready, executable trade opportunities.

What We Learned

While developing the idea, we learned that agricultural trade is not just a marketplace problem. Matching a farmer with a buyer is only one part of the journey. Aggregation, quality verification, compliance, logistics and cost estimation can determine whether a potential transaction can actually be executed.

This shaped our approach: instead of building another marketplace, we designed Agri Trade OS as an orchestration layer that helps coordinate these workflows.

How We Built the Concept

Our proposed system combines AI-assisted information extraction, buyer-supply matching, a retrieval-based compliance assistant, shipment and landed-cost intelligence, and a workflow interface for FPOs and other agricultural trade participants.

The goal is to convert fragmented information — such as available quantities, crop specifications, buyer requirements and trade documentation — into structured, actionable information.

Challenges

One of our biggest challenges was narrowing down a complex agricultural trade ecosystem into a problem that could realistically be addressed with technology.

We also recognized that agricultural trade involves regulatory and operational decisions that cannot simply be automated by an AI model. Therefore, our approach keeps humans and authorized service providers in the loop, while using AI primarily for information organization, matching, assistance and workflow coordination.

Our next step is to validate the identified pain points directly with farmers, FPOs, exporters and other stakeholders and use those insights to refine and prototype the platform.

Built With

• ai
• database
• fastapi
• generative
• learning
• llm
• machine
• natural-language-processing
• python
• rag
• react
• vector