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Investment Climate Podcast
On non-dilutive govt hacks, dodging the OpenAI threat, & $1.1M pre-seed pitch - Dan Bull, Scanabull
00:27:47
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- [00:00]
A
So first port of call was I went to talk to Tilburfer Fern Farms and I said, hey, I've got this really good idea. I think it's going to be really useful. What do you think? How could this fit in? How could this be useful for you guys? And they said, look, what you need to do is you need to talk to these guys at Sprout. So I went to talk to Sprout Agritech and they had an accelerator starting. We went actually through a massive pre due diligence phase and they kind of checked us out and all these things went on and eventually end of last year they agreed to invest, which unlocked the Deep Tech Callaghan Fund. My name is Dan Bull. I'm the CEO of Scannable. And Scannable has the mission to illuminate the world's beef supply chain. And what that means is we weigh animals quickly and easily. That means that there's data there so the beef supply chain knows what's going on. We are based out of New Zealand. I'm based in Raglan, North Island, New Zealand. We recently raised. Oh, and we're, we're a B2B company. We recently raised 1.1 million New Zealand and that money came from Sprout Agritech Enterprise Angels and the Callahan Innovation Deep Tech Innovation Fund.
- [01:10]
B
Amazing. Well, I'm really excited about this conversation. I've been speaking to a lot of New Zealand companies recently and I feel like New Zealand is going to save Agri Food Tech. With the recent announcement from Halter and anyone who's found looking at that closely, it's very interesting. So New Zealand is punching way beyond their weight here and I'm happy to have this conversation with you, Dan.
- [01:30]
A
Yeah, super excited to talk to you.
- [01:32]
B
Alex, take us through how you met your lead investor in this round.
- [01:38]
A
Yeah. Okay. So it kind of started with my first customer, my, you know, the major customer for the business. So I was, I've been working in artificial intelligence for the last seven or eight years and one of the customers I worked with was Silver Fern Farms. Worked on their nature program, basically carbon capture on farms in New Zealand and measuring carbon. I had this idea for, hey, we can weigh animals using 3D imagery. That's going to be really cool. It's going to make things a lot better. So first port of call was I went to talk to them and I said, hey, I've got this really good idea. I think it's going to be really useful. What do you think? How could this fit in? How could this be useful for you guys. And they heard me out and they're very gracious about it and they said, look, what you need to do is you need to talk to these guys at Sprout and they'll kind of help you out and tell you what you need to do to make this a thing. So they actually introduced me to Sprouts. This was back in mid 2024. So I went to talk to Sprout Agritech and they had an accelerator starting. So I applied for that and I went through the, you know, eventually got the application through and I was part of a cohort of 12 nascent businesses. And at that point in time I had nothing. I had a little bit of data, I had some code, but nothing was really working. And I just had this big idea in a way to make that go forward. So I did the accelerator mid 2024. At that point in time people were very good to me. But you know there's quite clear it was just me and I didn't really have that much going for me moving forward. I met my co founder, Daniel, one of my co founders and he, he joined the business and Daniel's a bit of a powerhouse so he did the iPhone, he did a lot of the coding part for the business and I do the artificial intelligence part and I have the industry knowledge for. So between us we were quite a lethal combination. So Daniel and I moved the business forward and we actually created something that worked. So mid last year, that's mid-2025, I went back to Sprout and actually we met Sprout at the National Field Day which is a big event in Hamilton in New Zealand and went back to them and they kind of started a conversation about hey, this could be something that you guys might be interested in. And we went actually through a massive pre due diligence phase and they kind of checked us out and all these things went on and eventually end of last year they agreed to invest which unlocked the Deep Tech Callaghan fund.
- [04:04]
B
That's amazing and great story as well. Deep Callahan, who are they?
- [04:10]
A
Yeah, so Callahan's, it's now run by mb. It's the government kind of R and D funding for Business agency. So they are not actually an investor. They've given us a 750k loan and that loan is paid. We actually pay interest on that loan at the 10 year government bond rate which in New Zealand is getting close to 5% now given the state of the world economy. But we have to pay that back at 3% of revenue. So although it's a loan. It's a pretty generous loan. And, you know, it's achievable for us to kind of hold that, hold that over us and. Yeah, and we don't, obviously we don't pay it back if the business fails.
- [04:51]
B
Okay, so that's quite.
- [04:52]
A
It works quite well for us.
- [04:52]
B
And just to clarify, that's a non dilutive play.
- [04:58]
A
That's not. Yeah, that's right. Yep.
- [04:59]
B
And then did you have to like sign off your house as collateral on the loan or. It's backed by the government.
- [05:07]
A
It's backed by the government. So if the business dies, the loan dies.
- [05:10]
B
Okay, great. Okay, that makes me feel better. You know, I was like thinking, gosh, Dan, let's go. Let's put that house up on the line. Yeah, yeah. Okay, great. Okay. So Lisa had Sprout, then Sprout unlocked this government play, which is a loan. And then there was also some Business Angels as well in the game. Or is that like Enterprise Angels?
- [05:30]
A
Yeah, that's correct. So we were talking to sprout mid-2025 and everything was taking a while. So we ended up, we started talking to other VCs. So we talked to a bunch in New Zealand. Enterprise Angels are an angel group. And in our case, we went to talk to these guys in Tauranga and Hamilton. Um, but the cool thing about that is in the Waikato is a large agricultural area. So there's a lot of farmers here. A lot of the people who have money are farmers or ex farmers or. So from our point of view, we could go to this fairly friendly audience who understands what we're trying to achieve and are super enthusiastic about it. So we went to pitch to Enterprise Angels and actually initially they said to us, oh, you guys are a bit early. You know, you probably don't, you know, you probably need to get a few customers and then come to pitch to us. But we kind of persuaded them. And so we went to pitch to this really friendly audience and they were super enthusiastic and basically agreed to kind of hop on board alongside Sprout. And I think getting Enterprise Angels on board was fairly pivotal to our success. So the chairman of Scannable is Simon o'. Connor. And we met Simon through Enterprise Angels. And Simon's been great at kind of helping us and unlocking doors. But also I think having that second set of capital interested certainly helped push Graph along. Like, if you've, you know, if there's, if there's one girl on the dance floor, it's all a little bit too easy. But if you know if there's, if there's someone else showing a bit of interest, it's human nature, it's just how things work. So Enterprise Angels have been fantastic and you're great to deal with too.
- [07:04]
B
Enterprise Angels is basically just a group of individuals that bonded together to a syndicate. Is that what it is?
- [07:13]
A
Yes. Now there's a bit more structure around it than that, unfortunately. I don't know the details
- [07:20]
B
but, but the individuals who are the angels themselves, some of them came from the industry that you're actually tackling as well. Right. So they understood the pain point that you're dealing with.
- [07:31]
A
Yeah, that's right. So we pitched to a bunch of people and a good chunk of those people we were pitching to were farmers or ex farmers. And you don't have to explain to them about weighing animals and why that's important and why this data is super useful because what we do is actually quite easy for most people to understand. It's quite a nice visual. So it was a super easy, you know, like people like a nice easy sell. So when we pitched, there were some other people pitching and they're pitching kind of file systems and these kind of complicated computer things. You can just see everyone's just eyes just go, start glazing over. Whereas what we said, we're doing something that everyone can understand. And as I say, half the audience were ex farmers or had something to do with farming and so they could see the use case of it.
- [08:13]
B
Yeah, and absolutely curious because, you know, you, you essentially dealt with the objection of too early saying, hey, the people in your cohort really understand our problem and they'll resonate with it. Is that how you dealt with the objection?
- [08:30]
A
Sorry, what objection to that?
- [08:31]
B
So the main objection is you guys are too early. So take us through how you dealt with that objection.
- [08:36]
A
Oh, okay, yeah. Well, they said to us initially, oh, you're too early. And we came back and said, oh look, you know, you have to understand this is deep tech. This is going to take a while. It's not, we're not creating a widget here. We're creating a fairly complicated machine learning AI system that runs on an edge device. So yeah, it's early, but it's going to take us a while to develop this. And when it's going, it's going to be super useful.
- [09:03]
B
Okay, so the way you dealt with it's too early is by saying deep tech takes longer than say cpg. Good. Right. Or some very basic soft software play that you can, you know, scan out through based form 44 like it's not too early if you come in the context of the company that we're building.
- [09:22]
A
Yeah, exactly. Yeah, yeah. And, and just adding to the, what I said before about, hey, there's an audience full of farmers, so they'll find it interesting and compelling. And these guys, they want some compelling stories to tell as part of the, the angel night. So I think that helped as well.
- [09:35]
B
Yeah, absolutely. Love that. So how did that pitch resonate? Because you said this thing about people just understood it. Right. And you could see it in their faces. Can you take us through the pitch? Maybe just pitch us for a second. Why should they care about. Why should I care about this problem? Okay, so you know, you weigh an animal. Why. Why is it even important? Why should weighing an animal be important?
- [09:55]
A
Yeah,
- [10:00]
B
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- [10:41]
A
Yeah, okay. All right, so basically it's a two sided. It's a two sided market here, right? So you've got a farmer on one side and they need to weigh an animal in order to better manage their stock to know when those animals are ready to be sent for processing and to the buying and selling. So you know, what, at what weight do I buy, what weight do I sell? So you need to know the weights to do that. Most farmers don't weigh because weighing takes time. The equipment's really expensive and a lot of them are older and you get a 6, 700 kilo animal. They don't want to be dealing with that in a cattle crush. Right. The other side of the market is the beef supply chain, like the producer. So the processors and all of those people involved are turning this into something that we actually buy in the shop. Now these people really need the data. So if you think about the beef supply chain, there's all this stuff that happens on farms and they have no visibility over it. Can you think of any other industry anywhere in the world where they have no clue what's going on in their supply chain? So Silver Fern Farms, who are, you know, key first customer for us, they understand that in fact, all the meat companies in New Zealand understand, for every single meat company we've talked to understands this, you go, you know, we've talked to a couple overseas in the States, Brazil, Australia, so these guys will understand this. Hey, look, this is ridiculous. We don't know what's going on in our supply chain at all. So if there's a way of getting that data and making it available to them, they can use that data too. Firstly, run their factories more efficiently so they know when animals are coming in, what sizes they are, and push stuff onto the correct lines, they can eliminate the, what's called out of spec animals. So in New Zealand, but actually all around the world, you have a certain percentage of animals that come through that are actually either too light or too heavy. And that costs the producers and the farmers money. And so if you can eliminate those, there's a massive saving there. You've got a product, you want to match that product to the best possible market. But in order to do that, you need to know what the product does before it hits the factory. You can't just be like, on the day, hey, we've got a whole lot of, you know, whatever, R2 steers with really good marbling, we've got to find somewhere for them to go. So if you have an idea of what's happening in advance, that there's economic advantages for the processor that are massive and for the farmer and, you know, between the two of them, that becomes quite a compelling use case to get this data. Okay, I can keep on going. A comparative system in New Zealand is dairy farming, right? So it's same overseas. So a dairy farmer gets the cows and he milks the cows on the day he milks, he or she milks the cows. You know exactly how much milk you've got, you know exactly how well your cows have done, and you can basically calculate how much money you've made on the day. And from this processor's point of view, in which case the milk processor's point of view, they know exactly what's going on as well.
- [13:19]
B
I love this. You just look at Frontera and Ketua Fund and you get to see this behemoth of power to understand that process. So you basically, you take the kind of transparency that already exists in other part of farming, like, for example, milk production, which is a really great example. Now you're actually putting that level of transparency on actually the weight of the animal, which is a lot of the value is the weight of the animal. Right. So I guess there would be a direct correlation between Kilos and value.
- [13:52]
A
Exactly, exactly. So just to add to that, we're doing weight at the moment, but we're also looking at doing carcass weight. So what you get paid on is the actual carcass weight. At this point in time, there's no way of calculating the carcass weight apart from a rule of thumb type measurement. So when we build that tech, that will be powerful because you're actually getting what you're being paid on.
- [14:13]
B
What is between carcass weight and tip weight?
- [14:18]
A
Yeah. Okay. So a large steer is typically about somewhere between 50 and 60% actual carcass weight. So for example, if the steer is 600 kilos, it might be 60% carcass weight, which is, you know, say three, what's that? 360 kilos carcass weight. But depending on the conformation of the animal, that can be smaller or bigger and the weight does not correlate exactly to carcass weight. So if you have a model that can predict carcass weight, that's going to tell you exactly what you're going to get paid on. So the farmers get paid per carcass weight? Kilo, they don't get paid per life weight. That's in New Zealand. It might be different overseas, but I'd imagine it's the same.
- [14:58]
B
Okay, we're going to go to my favorite part of the conversation. Okay, Dan, this is, this is going to be a lot of fun. Okay, so first of all, I'm really enjoying this conversation. I'm really, really enjoying this, this, this because it's so real to everyday farmers lives. And also we also see the prices of meat going up internationally, demand is skyrocketing. So more effective visibility tools. Like I can't imagine you being in a better time space than you are now. So this is, the timing is perfect. Now I'm going to ask just a bunch of you, go ahead.
- [15:29]
A
Yeah. We've luckily hit the high beef market. It's crazy. I don't know how that happened.
- [15:34]
B
You know what the high powers above have decided this is your moment. So I actually want to go down through objections that come to mind to me and if you can just take me through how you would deal with objections or how you dealt with objections, because again, I'm someone who doesn't know a lot, but I know enough to be dangerous. Right. So I'm just going to give you the pattern objections that come to mind as you're discussing and introducing your solution. And let's see how we go. Is that okay? All Right. All right. So the first suggestion that comes to mind is, okay, damn, like, video. You know, you're gonna have Gemini, Claude, you're gonna have OpenAI. They're gonna kill you. Like, like whatever data you have. Like, as a small guy from New Zealand with, you know, a million, you know, New Zealand dollars, if they wanted to go into your market, they could just destroy you in seconds.
- [16:34]
A
Oh, guaranteed they could. I think they've got bigger things to do than way beef animals is the first thing. Secondly, the model is only really a portion of what we do. So there's a whole lot of around what we do before the image gets to the model. And then there's a whole lot of tech after we create a prediction. And then a lot of what we're doing here is these relationships with these different parties and creating a solution that works for these different entities. Simply building tech isn't simply building models is only a small part of the big picture. And what we're doing is building models that use 3D data and work on edge devices, in our case, consumer grade edge devices. So we're doing stuff on the iPhone and we're building custom hardware. So OpenAI, Gemini, all those kind of things, they've got their amazing solutions that work really well, but they're not building edge device models that run on that use 3D 3D images. So we're doing something a little bit different. And I think the big picture is there's more to this than just models.
- [17:42]
B
So are you saying you're building specific hardware that attaches to an iPhone?
- [17:49]
A
Yeah. So the iPhone's solution one, the second solution we're building is a hardware solution that sits out in the paddock or the field, and this weighs animals as they come to a trough or go through a gate. Now, farmers are probably just going to want to have something that weighs animals and reports back what's going on. So this is kind of an automated solution for doing so.
- [18:15]
B
Great. So let's break down how I heard what Dan handle this Sol this Objection. The first is a classic. Hey, for OpenAI to put out a new product, it has to be like a multibillion dollar move, right? Otherwise they're just not going to pay attention to it. Like, we're specifically starting with a niche problem. We're too niche for them to care, and we're the ones that actually care about this issue. So that's a classic move. I love that move. Right. That's the way to do it. And the second piece that sounds like you're saying as we're also building, not just like an LLM, we're also building a hardware piece that's gonna sit on the farm and automate scanning as animals say, go back to their home, whatever area. Right. So did I hear this correctly?
- [19:02]
A
Yeah, you did, yeah. Yeah. I could probably add a third point about this is we're specializing in animal data and there's a lot of domain knowledge that the people at OpenAI and Google just won't have that we have developed. And we know because we've worked in this industry.
- [19:18]
B
Okay. And there's industry experience. Okay, awesome. Okay. So we let's. That's good. I love how you handle that. Again, let's go now to the next stage, which is. I understand that you have covered, you know, how you may be different from them. Is there any IP here? Like, is there some sort of IP that actually protects your solution?
- [19:40]
A
Yes, we have a provisional patent.
- [19:44]
B
And that would be on the hardware part or on the future hardware part.
- [19:49]
A
So the patent covers the software solution, the models. And the models and the approach.
- [19:56]
B
Okay, great. Okay. Farmers are. We know that farmers don't like to pay for new tech. Like, how are you actually going to have someone pay for this? They're allergic to paying for this. Why would they pay for this?
- [20:10]
A
Yeah, yeah, yeah, yeah, yeah. Your farmers are notoriously tight. So you're right. I come from five generations of farmers. I know all about that. Now we're actually looking to sell to the beef supply chain because we see that the major one will be beef for the supply chain. So our customers will be the meat companies, the trading companies, those companies that benefit from the weight data. So we haven't completely worked out the business model, but one possible model is the processors give this tech to their pharma suppliers. From their point of view, it does two things. Firstly, it ties those suppliers closer to the processor. Secondly, it means that they get this data, so they get the stream of data coming through and that's where the value lies. So the farmers are getting benefit, but we did not expecting them to pay for it necessarily.
- [21:00]
B
Yeah. So this kind of. This reminds me of another guest, one of my favorite guests, Adam Greenberg for I INU. They raised $20 million and they got actually funding from the California. Sorry, the Canadian Farmers bank, because this solution was. It doesn't. What they do is essentially they focus on greenhouses, observation and improvements of greenhousing using automation and robotics. And the idea was that the insurers that give money to greenhouses wanted to make sure they can get money back from Their investments. And this was essentially giving them supervision over their loans. Right. Because they could know they're going to default early. And to me, I actually think this is a really cool model that you do, which is you basically give real time health of a client. Right. Are these guys going to default or not? That they're going to be able to deliver on their. See, the industry can actually force adoption because they're going to say, look, we need to know where you are if you're going to deliver on our, on your promise to us. And this solution is that. Right. So love this model that you're doing. Okay, let's go next, last two questions, which is, what if a mistake is done? Who pays for it? Right. So Dan sold the solution. He says, look, you know, here's our model. It predicted this amount of weight you get access to the animal in damn dam was off at 50%. Who pays?
- [22:24]
A
Yeah. Okay. So personally, we're not going to be off by 50%. It is a predictive model. So there is going to be. Hey, it's not going to be perfect every time. Now I think you've got to look at what is it compared to what's happening at the moment. So at the moment, most of these decisions are made by someone looking at an animal and making a judgment using their experience in their brain. This is, this is often off mark. So even those experienced agents who've done this for 20 years will admit they'll get it wrong by 40, 50 kilos for an animal. So it's much better than that. We can be much more objective than the existing solution. Secondly, when you have a bunch of animals, if one or two are under, likely one or two are over, it evens itself out. So most farmers don't care about if an individual is off, it's a trend for that individual. And if it's, if you're moving a bunch of animals, it's the mob as a whole.
- [23:22]
B
And the last one would be just, you know, I see alter. Right. Can you tell me what you see in halter? What are you learning from halter that you're applying to your solution? Yeah, because I think a lot of people just open up their eyes and say, what the hell just happened here? Why spirit seal putting money into agriculture? Right. So take us through how you see halter and what lessons you may be learning from there as it applies to your company.
- [23:49]
A
Yeah. Okay. So halter have created a really compelling story of basically revolutionizing livestock farming in New Zealand and around the world. So. And it's not Built on nothing. So I know for a fact that there's people I know who have put halter in a beef system and their productivity's gone up by 50%, which is unreal. Nothing ever goes up by 50%. And these guys have done it. And not only that, they've reduced the amount of labor input. So it's a really compelling story. How they've done that is through the power of incremental improvements. So when they started off, not even that long ago, they weren't that great. Let's be honest. They created something that barely works and didn't do a whole lot of stuff that was useful, but they created something was just good enough, and then incrementally improved it to the point that now it's really good and it's a great product. And by doing so, they're harvesting all this data and they've turned themselves into a data company that allows them to create further value for their clients and create further income streams for themselves. So incremental improvements have just moved them ahead so fast. And then I should probably. I should probably add to that our technology. Our technology works really well with virtual fencing, and we've talked to a couple of virtual fencing companies about how we can do that in the future. So I see what we're doing. It's like a case of one plus one is four with virtual fencing. And I could go through the kind of use cases here, but it's just a really compelling argument for that.
- [25:21]
B
Yeah, it sounds like them winning is just great traction for you. Like, this is a very good traction opportunity for you guys.
- [25:33]
A
Yeah, well, yeah, and they've been really good to us too. You know, they've kind of. They've engaged with us and kind of talked us through stuff about, you know, how we can work together in the future.
- [25:43]
B
Amazing. Dan, look, I think you did such a great job answering these objections. It makes a perfect sense what you're doing and really excited about seeing your growth and being an ally to you. For our listeners who are. Who just learn from you, is there a certain ask that you guys have for what you need to accomplish in the next six to 12 months, what you're looking to do, and how can our audience help you?
- [26:08]
A
Yeah, so we're looking at commercializing the product. So we've got some user testers now, and we're going full commercial around June. So to move this ahead and to build our custom hardware solution out, so we've got a custom hardware thing we're building. We'll need capital and that will probably be early next year. So we're looking to talk to investors now so we can start to raise capital next year and get people involved in our story. We're also looking to talk to customers overseas. So our iPhone solution in particular is obviously really portable and we'd love to find partners who can help us commercialise this in other markets and we can whitelist our technology. We can give our app to people to use in various situations. So we're looking for those kind of connections overseas and in New Zealand.
- [26:50]
B
Amazing. Excited to be your friend and see you succeed. Congratulations.
- [26:53]
A
Well, thank you, Alex. Yeah, it's. Yeah, really great to have the opportunity to come on and talk about this.
- [26:58]
B
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