How GrainBridge became a product development machine

When I joined GrainBridge as CEO in April 2020, I was excited about our vision. As a joint-venture between ADM and Cargill, we had the financial backing, expertise, and data to help farmers become more profitable by building data-driven grain marketing software.

Of course, visions need to be translated into reality. My charge was to transform GrainBridge from an agricultural company (that built software) into a software company (in agriculture).

During my first week, the team told me that we wouldn’t be able to release a product until 2022. With a broad, complex product roadmap and no data yet from our owners, the road did seem long and daunting.

I challenged the team to release the simplest product as quickly as possible because I have a deeply-held belief that great products are built only through iteration. Finding product market fit with the waterfall method is just too risky. No matter how much user research you do, nothing beats having real users give you feedback on real software.

Fast forward to a year later and I couldn’t be prouder of the team here at GrainBridge. We released our first version of the new GrainBridge in September of last year and have been consistently releasing every 2 weeks since then.

Head of Product, Kevin des Lauriers, demoing auto-linking to ADM accounts.

How did we do it? We had a relentless focus on process.

Luckily, from a software perspective, we weren’t starting from zero. Before I arrived, the team had built a robust, modern infrastructure on AWS, capable of ingesting millions of records of data and able to scale to millions of users on the front-end.

With that foundation, the first order of business was to distill our product plans into a reasonable PRD. We asked ourselves what it would take to release by the end of summer, forcing us to evaluate whether every feature was necessary for a minimum viable product. Even though that meant there was no fancy data science in v1 of GrainBridge, it helped us to scope the product down to under 6 months of development time.

GrainBridge launched our initial product in September. A farmer could connect to her accounts on ADM and Cargill and see all of her contracts, scale tickets (deliveries), and payments. As far as we know, this was the first time transactional documents were aggregated automatically from two grain buyers on the same platform. GrainBridge was far from perfect, but that wasn’t the point of the release. The important thing was to release, gather feedback from our customers, and iterate.

The next step after the initial release was to move to regular release cycles. After a lot of internal debate, we settled on an aggressive two-week sprint cycle. Unsurprisingly, our first few releases were horrendously painful.

Product was behind on stories, engineers were spinning wheels, our metrics didn’t give us any information, and everyone was extremely frustrated… and yet, we persevered. By the time we hit the beginning of 2021, we had our first solid release and every sprint in Q1 was released on time, many of them at a faster velocity than we had predicted.

This process wasn’t easy and we certainly didn’t execute perfectly. But again, that’s OK. Product development process is just like the product: it only gets better through iteration.

Our best tool was the weekly retro. At first, retros were quiet. The real conversations would happen afterwards. Eventually, people realized that speaking up in the retro — voicing the problems that were slowing us down — was the only way to bring them to light and solve them.

Becoming a product development machine in under a year is a feat that few companies can match. With all the work we did in Q1, I can confidently say that GrainBridge is a best-in-class product for farmers who want to view their contracts, scale tickets, and settlements and keep up on local cash prices from ADM and Cargill.

Just recently, we switched from a marketing-style landing page to a product-focused landing page.

GrainBridge has a much bigger vision of helping farmers to know when to “market” (that’s agriculture-speak for “sell”) their grain to help them to become more profitable. Now it’s time to point our product development team at that problem.

Our main advantage is that we have years of data from the two largest grain buyers in the US. Those data can help us give insight to the farmer — all done safely and securely, with respect for privacy. We have a notion that we’ll be able to analyze our data to figure out the best marketing strategies to maximize profit. Internally, we joke that we’ll be able to boil it down to the “Seven Habits of Highly Effective Grain Marketers.” Once we know those attributes, we can give each farmer a grain marketing score and how they might consider improving.

Imagine a “grain marketing score” for farmers.

We’re building out the foundations of a data refinery right now to build and test out user features. Our data science team now has two PhD data scientists and a data engineer. If you’re interested in joining, we have an open business analyst (BA) position focused on data.

Finally, the main point of building a product development machine is to solve problems for our farmer customers. In Q1, we conducted ethnographic research (i.e., observing the farmers in their environment) on a group of farmers. Grain marketing software has never been widely adopted in the industry. Our biggest insight from the research is that adoption is driven by trust; and it’s much harder to trust a piece of software than a human being. We’ve got some ideas about how to build up that trust with the farmer and lead him to higher profits.

GrainBridge has achieved so much in this past year thanks to every member of our small strong team, the incredible support we’ve gotten from our board and owners, and the feedback we’ve gotten from our customers. I can’t wait to report out on some of the releases we have coming up.

CEO of GrainBridge, an AgTech JV between ADM & Cargill. Co-founder of Descartes Labs, former CEO of Zite. Love product, philosophy, data refineries, and models.

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