From software to sustainability: why the future of milk is data-driven.

When I started my journey in agriculture, still as a veterinarian, I never imagined I would spend a good part of my career building technology. But, looking back, it all makes perfect sense.

From the beginning, what motivated me was not just the production itself, but understanding how to consistently improve production systems. This led me to develop management software, to build a company that came to serve thousands of farms in Brazil, and, most importantly, to a realization that would completely change my path: the value was not in the software, the value was in the data.

Over the years, we have seen that, when well-structured, data has the power to transform how the sector makes decisions. This is how benchmarks, comparisons between production systems, and a new ability to objectively view efficiency emerged. But there was a limit. Management, by itself, was no longer able to meet the new demands of the market.

The turning point: when efficiency is no longer enough.

In recent years, agriculture has begun to deal with a new type of pressure. It's not enough to produce more; it's necessary to produce better. It's not enough to be efficient; it's necessary to be transparent. It's not enough to generate results; it's necessary to demonstrate impact.

And here's an important point. There's still a tendency to associate sustainability in agriculture solely with carbon footprint. But that's only part of the story.

In practice, sustainability requires a much broader framework, based on reliable data, comparable indicators, analytical capacity, and, above all, evidence-driven decision-making. Without this, any discussion about ESG becomes superficial.

The biggest challenge is not technology.

If there's one thing we've learned throughout the construction of ESGpec, it's that the greatest challenge lies not in technological development, but in building understanding.

The sector still grapples with misinformation, excessive jargon, difficulty in technical communication within companies, and, in many cases, initiatives that exist solely to fulfill protocol. This scenario generates resistance and often alienates precisely those who should be at the heart of the transformation: the producer.

Therefore, an essential part of our work has become education. Translating complex data into something applicable in the field, connecting technique with reality, and showing, in practice, that sustainability is not a cost, it's a strategy.

From data to decision

In practice, what we built was a different logic. We didn't start with carbon, we started with maturity.

Today, it's possible to look at a farm and understand what stage it's at in terms of management, animal welfare, production efficiency, and environmental practices. From this foundation, it makes sense to move on to more complex metrics, such as carbon footprint.

This approach completely changes the dynamic. It avoids demanding something from the producer for which they don't yet have the structure and, at the same time, creates real conditions for evolution. More than that, it allows for the generation of value.

When applied correctly, ESG not only reduces environmental impact, but also improves economic results, reduces risk, and strengthens market positioning.

ESG is not about reporting, it's about structure.

Another key point is understanding who uses this data and for what purpose.

For the industry, they are essential for scope 3 reporting, market access, and building consistent narratives. For the producer, the value lies in efficiency, clarity of decision-making, and defining practical paths forward.

These are different perspectives, connected by the same foundation: reliable data.

Therefore, ESG cannot be treated as a report. It needs to be treated as infrastructure.

The future has already begun.

Today, we are already able to measure and analyze a significant portion of milk production in Brazil. But this is still just the beginning.

The next step is not only to expand this coverage, but to transform data into supply chain intelligence. To move beyond diagnosis and advance towards comparison between regions, identification of patterns, and decision-making at scale.

And, most importantly, to build a realistic narrative about Brazilian agriculture, based on evidence and not on perception.

For those who are just starting out.

If I could give one piece of advice to anyone entering the agricultural sector, especially in deep tech, it would be straightforward: don't go it alone.

Seek out good partners, work with data, and be willing to adjust course. Building something relevant in this sector requires technical knowledge, a long-term vision, and a constant ability to adapt.

In the end, it all starts with measuring.

The transformation of livestock farming doesn't begin with technology, it begins with understanding. And understanding begins with data.

Measuring is not the end goal, but it is undoubtedly the first step towards transformation.