Updated March 5, 2026
0:00 Welcome to Colaberry AI podcast brought to you by Colaberry AI Research Labs and the Carl Foundation. So today, we have a really interesting deep dive all set up, for you guys, and it really digs deep into the, the technical side of digital onboarding and risk assessment for smallholder farmers using AI, you know, all that good stuff. Our mission today is to understand the how and the what behind this fascinating collaboration between Colaberry Inc and Al Inn. Yeah. And I think what's really important for listeners to understand is how this partnership is leveraging data science and AI. 0:34 You know, those are the key elements that are really making a difference in this space and, honestly, changing the game for agricultural finance. So we'll get into the nuts and bolts, you know, see the tangible results, the real impact. It's gonna be a great deep dive. Right. So to set the stage right, smallholder farmers, we know they face a ton of challenges when it comes to traditional financial services. 0:53 I mean, they're often perceived as high risk by lenders. And, you know, they often lack that traditional collateral. So how do they break through? Yeah. And this is where, Alaland comes in. 1:04 They're this, you know, it's an ag fintech venture. Their website is https.www.letsgold.in, and they're tackling this head on. They're working in places like India, Kenya, Ivory Coast. Wow. It's really about making a difference where it matters. 1:20 And their partnership with Cullaberry Inc, well, that's what's really exciting. It brings that, you know, data science and AI muscle into the equation. Right. So I'm hearing a lot about empowerment here, empowering farmers, giving them the tools. But what does that actually look like? 1:33 Well, think about it. I mean, smallholder farmers, they're the backbone of our food supply. Right? Yeah. Allian, they created this unified digital platform to give them that much needed access to resources and financial services. 1:45 It's all about boosting their productivity. And in turn, you see you get greater economic stability for their communities. It's a ripple effect. Okay. I'm with you. 1:52 So we're talking big picture impact. But, have they made any headway? Like, how many farmers have they actually reached with this? Yeah. So they've already successfully onboarded, you know, a pretty good number, 4,391 farmers, and this is within the Farm to Market Alliance network. 2:08 It's a good start. Okay. Not bad. Not bad at all. But, you know, getting farmers on the platform is one thing. 2:12 I mean, are they actually using it? I mean, do they know how? And that's why training is so important. Right? Yeah. 2:17 So they've trained a 116 agents on the a line m platform. Think of these agents as, like, you know, guides. They help bridge that gap, making sure farmers can really utilize the platform and get the most out of it. Okay. That makes sense. 2:29 So let's get to the heart of it, the engine. Right? Talk to me about this AI based dynamic risk scoring engine. I mean, what makes it tick? And why is AI so so central to this? 2:41 Well, the key here is that they've moved away from the old school, one size fits all risk models. Right. This engine uses, you know, machine learning algorithms to look at each farmer's risk individually. It's super detailed. Mhmm. 2:53 And because it can crunch all this data, you know, various different data, it lets financiers make much smarter decisions about credit. It opens up possibilities for noncollateralized financing, and that's huge, especially for those who don't have those typical assets. Okay. So it's all about a more personalized approach. Right? 3:10 Using data to really understand each farmer situation. So how does the AI actually work? What's the approach here, technically speaking? Okay. So the main method is supervised machine learning. 3:22 Right. They train these algorithms on data that's already been labeled, you know, to predict risk for each farmer. They use different algorithms, you know, things like classification algorithms to categorize farmers into different risk levels. It's all about creating a nuanced understanding of risk. Right. 3:37 They also use something called predictive analytics, especially for agricultural insurance. They can look at, like, historical yield data and match it up with weather data to create models that can actually forecast potential losses. And these are more accurate than traditional methods, much more advanced. This is really interesting stuff. You mentioned dynamic in the name of the engine. 3:59 So does that mean it's constantly learning and changing? How does that work? Absolutely. The system's constantly learning. It's like, you know, think of online learning. 4:07 Right? Whenever new data comes in, like, you know, updated yields, real time weather data, or, you know, transaction logs, They use it to retrain the models. So they're always up to date, accurate, and reflecting the real situation for each farmer. Okay. I see. 4:21 So the engine is constantly adapting to changes. So let's talk data. What kind of data is the system actually using? Oh, they pull in data from everywhere. Think about it. 4:31 Satellite imagery to check on vegetation health. They've got sensors in the ground measuring soil health. They're using weather stations, you know, for hyperlocal climate information, obviously, historical yield data, detailed farmer profiles. We're talking land records, crop calendars, transaction histories, even external financial data. It's a huge amount of information. 4:53 Wow. So it's like this flood of data coming in from all these different sources. So how do they handle all of that? I mean, how do they organize and make sense of it all? Okay. 5:02 So for that, they use Azure Data Factory. It's a powerful tool, perfect for the extract transform load process or ETL. That's the technical term. So first, all the raw data that comes in, it gets clean. You know, they deal with any gaps or outliers. 5:16 Then they normalize it. You know, put it all on the same scale so it can be compared fairly. Right. Finally, they transform it, you know, structuring it into a format that those machine learning algorithms can easily digest. It's a computationally intensive process, but it's the foundation for building accurate and reliable models. 5:32 Right. So cleaning the data, making it all uniform, and then they do this feature engineering thing. Right? What's that about? Yeah. 5:39 So feature engineering is where we get really creative. We take all that raw data and turn it into signals that are actually meaningful for prediction. Like, let's say you have basic weather data, daily rainfall, and temperature. Well, we can turn that into something more useful like total rainfall during a critical growth stage or how many days in a row the temperature was above a certain point, you know, things that really impact the crops. Oh, I see. 6:02 And the same goes for financial risk. You can use transaction history to create features like how often payments are late or, you know, average transaction size compared to the expected harvest. These engineered features, they add a lot of power to the models. Wow. So it's about creating a much richer and more insightful picture of what's going on. 6:22 So all this complex data processing, the algorithms, all that. I mean, what are some of the cool, tangible innovations that come out of all this? I heard something about real time crop intelligence being sent out via SMS. Yeah. Exactly. 6:35 So based on all the data analysis, you know, things like weather patterns, soil moisture, and crop growth stages, the system can give personalized advice to farmers. You know, things like when to irrigate, the best time to fertilize, or even warnings about potential pest problems. Mhmm. And it's all delivered straight to their phones by text message so everyone can access it. Wow. 6:55 That's incredibly useful, especially in areas with limited Internet access. And the models are getting smarter all the time with adaptive learning. Can you unpack that a bit? What's going on behind the scenes? Well, it's all about online machine learning. 7:07 As new data comes in, you know, things like actual harvest yields or repayment outcomes, they use that to fine tune the models. They use some really clever algorithms like stochastic gradient descent, which allows the models to learn and improve with each new piece of data. Oh, okay. So they're constantly getting better at predicting. Okay. 7:26 So you mentioned different risk scores. Let's break those down starting with the distress score. What exactly does that tell us? So the distress score, it's all about figuring out which farmers might be in a tough spot financially. We look at things like savings balances, their debt to income ratio, you know, if we have that data. 7:42 And we even use indicators from transaction data or if they've reported any crop problems. Then we use a model trained on past cases of farmer distress to give a score that represents how likely or severe their financial distress might be. Okay. So it's like an early warning system. What about the overall risk score? 8:00 How's that different? Yeah. So the risk score, that's the big picture. Right? We factor in a lot more variables here. 8:05 Things like, you know, how they've repaid loans in the past, how secure their land ownership is, what crops are growing if they have irrigation, even, you know, risks at the community level. And then we use machine learning, either classification or regression models, depending on how we wanna represent risk, to predict the chance of something bad happening, like defaulting on a loan or needing to file an insurance claim. We often use algorithms like random forests or gradient boosting machines because they can handle really complex data. This is all so intricate. The alternate credit score, though, that's a really interesting idea for farmers who don't have a traditional credit history. 8:39 How does that work? Yeah. So with the alternate credit score, we think outside the box. Right? We look at data that's not normally used for credit scoring. 8:48 Things like their social networks within farming co ops, how they use their mobile phones, even qualitative data that field agents gather. Then we can use machine learning techniques like collaborative filtering or graph based methods to understand their creditworthiness based on their community. It's all about finding those trustworthy farmers that might slip through the cracks of traditional credit scoring. I see. Okay. 9:10 So let's move on to the reporting side of things. What about farmer subscription churn prediction? What's the goal there? Right. So churn prediction, it's basically trying to figure out which farmers might leave the platform. 9:21 It's a yes or no prediction, and we use algorithms that are good at that, like logistic regression, which tells us the probability of churn. Or we might use more complex methods like random forests, which are really good at finding patterns in data, even nonlinear ones. We look at things like how often they use the platform if they're engaging with the SMS services and how satisfied they say they are. So that's about keeping farmers engaged. What about predicting whether farmers will fulfill their contracts? 9:49 What data goes into that? Well, for contract fulfillment, we dive deep into the data. Right? We look at the specifics of their contract, their past harvest yields, the weather forecast leading up to harvest time. Again, this is usually a classification problem trying to predict a yes or no outcome. 10:05 And for this, we use algorithms like XGBoost and neural networks. These are some of the most advanced techniques out there. It's really incredible how they're applying all this technology. So let's talk about the Detect stack itself. What's the architecture like? 10:17 So the entire system is cloud based, built on Microsoft Azure. They use Azure Blob Storage for the data lake, which is perfect for storing massive amounts of data, and it's super cost effective. Then they use Azure Functions and AdriLogic apps to manage all those complex data processing workflows. These tools make sure everything runs smoothly even with all that data flying around. And how are they managing all the software applications themselves? 10:42 So for that, they use Docker for containerization, packaging each application neatly so it can run anywhere, and then they manage these containers using Azure Kubernetes Service or AKS. It's a powerful tool for managing these complex applications, you know, making sure they run reliably even when things get busy. Workflow automation seems key here. What's Azure Data Factory doing in all of this? Azure Data Factory is like the conductor of the orchestra. 11:08 It orchestrates the whole data pipeline from start to finish. It handles connecting to the data sources, cleaning and transforming the data, and then loading it into the right places. It's all automated and super reliable. Okay. So what about the actual tools and languages they're using for development? 11:22 Well, the data science team, they're big on Python. They use libraries like TensorFlow and PyTorch for building those deep learning models. And for more traditional machine learning, they use scikit learn. Power BI is their go to for data visualization, creating those dashboards that stakeholders love. The back end of the ALAN platform, that's built with c sharp and asp.net core. 11:43 And for the databases, they use Microsoft Fabric and SQL Server. And finally, how do these AI models get integrated into the Allen platform? So once those machine learning models are trained, they deploy them as restful APIs using Azure App Service. This lets the Allen platform send data to these APIs and get back real time predictions. Azure Synapse Analytics plays a key role here too. 12:06 It's like a central hub for analytics, pulling together data from different sources for both training the models and making those real time predictions. That way, the insights are always readily available within the Allen application. Wow. So it's this incredibly sophisticated system all working together seamlessly. For our listeners, we've really gone deep into the technical side today. 12:25 We've seen those complex data pipelines built with Azure Data Factory, the powerful AI algorithms like random forests and neural networks, and the rock solid infrastructure provided by Microsoft Azure. It's all coming together to transform agricultural finance. And it's not just about the technology. Right? It's about the impact. 12:44 Smallholder farmers are finally getting access to financing. They're getting better yields. They're running their operations more efficiently, and it's all thanks to these technical advancements. This has been a really fascinating exploration. It makes you think, doesn't it? 12:57 Could these same methods be applied to other industries facing similar challenges? Could we see a world where these techniques are used to industries, not just in agriculture? Absolutely. This idea of using data to make better decisions, assess risk more accurately, and deliver insights through easy to use platforms, it has the potential to transform many industries, especially those dealing with complex problems and trying to reach those who have been traditionally underserved. It's definitely food for thought. 13:24 It is. Thank you for listening in. Subscribe and follow Colaberry on social media. Links are in the description. And check out our website, www.colaberry.a I backslash podcast for more insights like this.
