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Welcome to What the Tech, the show where we talk to the founders and operators building innovative companies across North America and dig into how they fund the work that matters.
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Today I'm joined by Hugh Molineau, co-founder and president and CEO of Refined Data, a Toronto software company that's been helping commercial real estate firms make sense of their data since 2007.
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If you've ever been told that RD tax credits are just for startups, today's conversation is for you.
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My guest has been building software since the mid-90s.
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He co-founded Refined Data in 2007.
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For nearly two decades, the company has been the operating layer behind some of the biggest names in Canadian commercial real estate.
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Think Colliers, Morgert, King said, Benzel Green Oak.
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And here's the interesting part.
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Instead of coasting, Refined Data is in the middle of reinventing its product, moving from compliance software into AI-assisted investor reporting, built on simple promise.
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Every number traces back to its source.
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Numbers you can defend.
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That's a promise that takes real engineering to keep, and it's the kind of work Shred was designed to fund.
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Hugh has been claiming with Boast, and he's here to talk about the RD tax credits behind the pivot, what the claim processes actually look like, and what non-dilutive capital has meant for a mature company that's still betting on innovation.
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Hugh Mono, president and CEO of Refined Data.
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Welcome to What the Talk.
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You've been building software since the mid-1990s and co-founded Refined Data in 2007.
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Can you introduce yourself and describe what your role as president and COO actually looks like day-to-day?
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Yeah, well, it's evolving.
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And, you know, obviously with AI, it's putting a lot of pressure on technology to grow quickly.
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You know, it feels a lot like the mid-90s when I first started, when the internet was new and exciting.
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And now obviously we have AI, and that's really exciting.
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So my role is evolving in that it used to be very tactical, high contact with clients, delivery of projects, and being really involved in that.
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But now we are looking to grow the business by a factor of 10.
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And everything that worked for me in terms of the hustle and all the founder mentality, you know, that stuff I'm discovering is working against me now.
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And so really what I'm what I'm doing is moving the operations down into the into a delivery group that we've created and also getting the programmers more involved as well, so that they're more client-facing too.
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And I'm really focusing now on strategy.
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Like what is it going to take to 10x our business?
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And what do I need to focus on to make that happen?
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Which is going to be a transformation both of myself, but also of the business and processes around the business too.
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So 10X, that is a lofty goal, but also something that I think is achievable with all of the not only prowess that you have from working in the space for as long as you have, but also with the new tools that have kind of emerged in our space and the ability for AI to give you superpowers to really make sure that this data can get collected and then actioned upon.
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So I'd love to build a little bit more about why you pull what pulled you towards real estate technology, I guess, in the first place.
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Yeah, so way, way back at the beginning of my career, which is now more than 30 years ago, my degree was in uh environmental sciences and engineering.
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So we were doing it, it was just the late 80s where there was this big focus in real estate on redeveloping sites that were contaminated or that had historic environmental issues.
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And so that was a very exciting and new field.
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It's where I started my career.
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Uh so I did a lot of engineering work in terms of things like the remediation and cleanup of Canadian Air Force bases in Germany, also the remaining cleanup and remediation around assessing liabilities for big mergers and acquisitions in the US.
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And I noticed sort of about 10 years into that career that there was an enormous amount of data that was getting generated, which I'm really dating myself, but was sitting in filing cabinets.
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And so the whole entry into technology was to take all of this information that real estate companies were storing and digitize it and make it available more broadly, particularly so that people could get a view across their portfolios of what was happening.
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So that was really, and that was so founded in real estate that the technology piece followed that.
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Yeah, and that makes perfect sense too.
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I mean, there's a treasure trove of data, and I'm gonna date myself too.
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Uh, I remember back when I started my career as a content marketer, digital transformation, and turning those file cabinets into actual actionable resources for your team was the name of the game.
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So uh very much the same journey when you look at it, right?
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Extremely similar, honestly.
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And I think it's just a through line throughout our entire industry.
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And when I say our industry, I just mean the innovation space.
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Making sure, again, whether it's real estate or whether it's any place where you're collecting all of this data, you can actually make it actionable.
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I think that's really what, again, we at Bhost are trying to do.
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We're not just collecting it for our own health, and we don't just need to be busybodies and know all about your finances.
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We really want to make sure that you can take the efforts that your RD teams, for instance, are doing and deliver better products to your customers.
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And I think that's a sympathy between refined data and at BOST because you guys have made the data very actionable.
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And while it's for one sector, the ethos is very similar.
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Um, I think it just really is about being smart with it, which again, I mentioned AI earlier.
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It gives us superpowers.
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I'm probably a broken record when I'm on the podcast talking about how AI isn't replacing anybody, it's just making them more efficient.
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I'd love to talk a little bit more about that and the business.
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So we talked about you, but let's bring it back to refined data a little bit.
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What is the elevator bench?
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Yeah, so essentially the focus of the company is we work a lot with our clients around aggregating and managing their finances.
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So the big accounting system in real estate or the big ones are MRI and YARDI, and particularly in Canada, yeah, YADI's really down.
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And so uh nobody ever questions the numbers coming out of YADI.
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What they're questioning is the why behind those numbers.
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So if you have a building and NOI has slipped, as net operating income has slipped in that building, uh, nobody's questioning if the numbers are accurate.
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People are questioning, well, why did that happen in that particular building?
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And so our software is really taking the numbers and then coloring those numbers with the why, the narrative, the views of the site accountants, the views of other operational teams inside real estate.
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And that's allowing the CEO to really see what's happening behind the numbers that are telling the true story of how the portfolio is performing.
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Oh, that makes perfect sense.
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And I actually think my next question kind of tees you up to expand on all that, if uh you won't mind me asking you a little bit of a repetitive one.
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But again, Refined has started an EHS in compliance software for real estate and is now positioned around financial planning and investor reporting.
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Walk us through that evolution.
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So you kind of just stood there, but I'd love to hear what the market tell you on that.
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No, well, I think you know, it's an interesting segue into our relationship with BOST because uh essentially what happened was in 1994, we kind of hit the edge or the end of the technology lifecycle for our core platform.
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So our platform is enterprise great, it's used by some of Canada's largest real estate companies, uh REITs and uh also investment companies as well.
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So it's enterprise great, and it had hit the end of its life cycle.
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So we were looking at a complete redesign of the platform or re-engineering of the platform as well as redesign.
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And so AI's arriving just at the same time.
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You know, we're really focused and always have been on the data management, data acquisition, data treatment.
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Um and so these two opportunities intersected at the same time.
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And we decided uh essentially to change the platform into something that was massively configurable.
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Um I could talk more about that after, like the decision behind that and what that's made available, but there was a dramatic change in our foundational view.
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So I will actually talk a little bit about that now.
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So before we had built the industry, what we felt was the industry best platform for dealing with risk in real estate.
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But it was very much about explaining to clients why they needed our platform.
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The transformation was saying, well, okay, let's put that to one side and let's build something so flexible that we can actually meet the clients where they are.
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And that transformed the business because it suddenly was us arriving at a client's site and saying, Oh, offices and saying, what are the problems that you're most dealing with?
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And so we got pulled out of risk and into finance, which is obviously a big focus in real estate.
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And the kinds of problems that we were solving changed dramatically.
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The platform had to change dramatically to deal with that, those new that new environment and the kinds of demand that financial uh systems need to meet.
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Also integrating AI in a way that was responsible, that made sense, because AI can't be 98% correct with finance, it has to be 100% correct.
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And so using AI in a very um thoughtful, I would say, thoughtful way, recognizing its limitations, let recognizing the opportunities that it built as well.
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That again, a lot of research and development, a lot of new ways of doing things.
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So that's a long answer maybe to your question, but it maybe provides a little bit of a flavor of the world that we were in, you know, two years ago.
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No, I think it actually tees up a bunch of points that I want to put a fine line on from everything that you just said there, too.
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Again, when you're moving from risk to finance, maybe on paper you do that as a different buyer, but it is still fundamentally it's the budget, it's the actual ability of the business to be able to be effective and have the funding that they need for everything in place.
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So I think, too, you listened and you met the buyer where they're at, and you made a pivot that was going to deliver something valuable for this customer, for your client base.
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That wasn't just reinventing the wheel.
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So again, you saw the limitations of the previous platform, you listened to what your ICP actually needed, and you started developing something that actually builds towards that.
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And you weren't just, again, taking something off a shelf.
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It was very much building something net new because there wasn't a prowess for or a product that could deliver what you guys are delivering in terms of that intelligence and really actioning.
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Again, I keep on reusing the word actioning, but I think that's the critical piece here.
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It's something that people can actually do something with.
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And um, I think a lot of products tell people sycophantically sometimes what they want to hear, or sometimes just like, oh, I need data for the sake of reporting.
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It's not for the sake of reporting, it's for the sake of the business.
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So you're evolving because it's what your customers need, and you're not moving away from your buying audience, you're just making sure that you're serving them even better.
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So wanted to put a big thing.
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I think that's you that's very insightful.
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And I think that it raises a whole series of interesting challenges technically, but also interesting challenges from the business perspective as well.
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Because, and this is where the re research and development came in, because in developing a really see if you focus on one business set from a technology platform perspective, you can actually really optimize to meet that set.
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So, you know, for us we were dealing with large amounts of data, and so we optimized for speed, and we optimized everything in the platform, the early first version of the platform for speed.
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Now we're saying, okay, we're not going to focus on that anymore.
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We're going to focus on flexibility.
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And what we had to give up for the flexibility initially was speed.
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Because, you know, the app was incredibly flexible.
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It was almost like a spreadsheet that was also a database, too.
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But the problem was the first iteration of the software took 60 seconds to load the first page.
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You know, there was real challenges in providing flexibility, but then also managing the fact that it has to operate, you know, at a cadence that's gonna serve businesses.
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You know, it can't take 60 seconds to open up.
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So that was the technical challenges that we confronted, but then also rewiring ourselves to stop talking about what we did and what the platform could do and start to listen to it for clock to clients.
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What is the what are the issues they're facing?
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You know, where is the bottlenecks in the workflow?
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Who's starting to ask questions and questions and questions and and then this interesting issue of the clients saying to us, well, what do you have?
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What does your platform do?
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And we're saying, Well, what do you need?
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And they're saying, Well, we could tell you that, but we're interested in what do you do so we can map it onto what we need.
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And so a lot, you know, we again can talk more about that if it's if it's um if it's if necessary, if it's interesting.
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But you know, it's it's been change on so many foot fronts.
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Uh, and again, the ability to capture that, capture the challenges was one of the things that I was really, really happy about with uh Bose AI because we were focused so much on the business changes that we were dealing with.
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We also had to capture the technology changes, but in some respects those were secondary in terms of what was on the table for me.
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I'm moving this seamlessly into my next arena of questions here.
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That's exactly where I want to dig into next.
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So, I mean let's dig into the innovation story too, and about what's changed, because I think the context we've laid a good groundwork for, but I'd love to know what is the core technical innovation behind the current platform?
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What did you have to build that didn't exist before?
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Or off the shelf.
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So again, a basic cliche.
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One of the things that we were really interested in is technology's been around, as we both said, for 30 years.
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And then how come companies are using spreadsheets so prolifically?
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And what is it that we're just not able to provide people that forces them to use spreadsheets?
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And one of the things that we could see was that spreadsheets are massively flexible.
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You can fire them up, you can apply them to whatever business problem you're faced with.
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You're not in no way, shape, or form are you constrained from solving an immediate problem very quickly.
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And then there's very rich graphics you can generate.
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It's that it's just great.
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Spreadsheets are amazing.
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They are.
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With all the drawbacks that anybody listening to this would know about uh spreadsheets, they're not great at sharing data, they're not enterprise grade.
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Somebody can walk out of your office with the spreadsheet on the computer, you know, and who knows where that file will end up.
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So there's a there's a lot of considerations that you trade off for that flexibility.
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So we arrived with all of those considerations handled as an enterprise data system.
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What we lacked was flexibility.
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So we said, okay, let's show constraint to the four winds, let's just look at this flexibility issue.
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And so that's what we did was we built an incredibly flexible prob uh platform that built in all kinds of reporting capabilities that allowed us to the ability to duplicate exactly the format of clients' existing financial and other reports, did a great job with that.
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That took time, engineering and expertise.
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And then finally, once we built this phenomenal platform, the beta version, we had to deal with that it takes took 60 seconds to load the first page.
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So flexible as as crazy as you'd want it to be, performance was out of the window.
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And so really we we just spent a year just attacking the performance issue, looking for re-engineering it while preserving the flexibility, you know.
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So I do like to think of us, we don't talk about this because we're so focused on the business, uh the business uh benefits that we're delivering, the what matters to the business audience.
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So I never talk about this, but from the technology perspective, we're a spreadsheet that's a database and as easy to use and as flexible and as you know, we produce uh board-ready reports, you know, from the data that we're tracking.
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Yeah, but you can hear my enthusiasm.
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And my enthusiasm isn't about how great the platform is.
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We frankly don't care about that.
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My enthusiasm is because of what we're able to deliver to our clients and what we're able to deliver to the market.
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I'm more excited about the business benefits that we're delivering, this ability to source and tell the story behind your numbers, the ability to point this at a whole range of different problem sets.
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We've solved for financial issues, we've solved for due diligence that used to be managed on spreadsheets, we've solved for sustainability issues, we've solved for statement of values, insurance issues, all on the same platform, you know, just like you would use a spreadsheet for dealing with all of this wide variety of business challenges.
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Yeah, I'm gonna stop there because I honestly got me on a topic that I could just yeah.
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No, it's incredible too, and your enthusiasm, again, it shows again that you're listening to the customer challenge.
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It's not just creating an innovative platform for the sake of intuitive innovation.
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You want to be sure that you're solving problems that you've heard acutely in the field, and that you're again, it's the listening.
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And that's something that I think really I want to say something else about that.
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Uh, because I think this also bears on our experience with both.
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So I think it's very related to the conversation we're having here, which is this you know, we our client retention has been really strong.
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Like we we have our original founding clients from 20 years ago, and uh you have almost no client turnover, except a couple of situations where the need for the platform changed.
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So but we've had this enduring thing, and so so our client relationships have been very central, but I was always a little bit feeling with our clients that while the platform was good, it wasn't ever quite what they really wanted.
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There was always improvements that we said were coming down the pipeline, it was slow to implement, we were choked off by you know development resources and the like.
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All of that has gone.
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And what I'm really excited about is actually not how we feel about our clients, but the delight that we're able to generate for our clients.
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They are really happy because finally they're able to get exactly what they want, tailored down to the T, exactly what they want.
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And there's been another interesting benefit, which is that as we've solved one business problem with the platform, we can then solve an adjacent business platform that's problem that's just the extension of the platform.
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So all that data that was useful for the first one, about 50% of that data is now useful for the second issue.
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And then we're tailoring that second issue, which means that the training almost disappears because people are using something that looks a lot like the spreadsheet version of what they were doing anyway.
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So, anyway, but it's the client delight that's got me so lit up.
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And and that was central to you know the journey that we took with Bost as well.
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Okay, so we're gonna move into a little bit more of the Bose-centric conversation because basically, like you said, delight.
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That's really what we want to generate.
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And again, it sounds a little uh trivial when we're just saying, no, I want to delight my clients.
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It's I'm 62 years old.
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I'm at the end of my career.
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I'm approaching the end of my career.
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I hope I'm gonna still have many, many years left to be passionate about.
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But I just but to your point, what matters to me now is my relationships, my business relationships, my personal relationships.
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I think that's very natural as as you get older.
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Those things now start to come very sharply into focus in a way that in my 30s maybe it wasn't quite so important, right?
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There's maybe a little bit more about me.
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No, but I honestly too, and it's not like clock isn't ticking or anything like that, but there's so much crap.
00:19:58.400 --> 00:20:09.039
I'm gonna use bullshit, sorry, that uh you get served up when you're in this space and where you're offered solutions and where again it's okay, I can test this out, but um, clock is ticking.
00:20:09.119 --> 00:20:16.400
Uh, we actually have to get this to market, or we actually have to make sure that our customers are happy and that they're paying for what they want and that they want to keep what they want.
00:20:16.559 --> 00:20:25.599
So, even your point about like the churn um and how you've been able to really make sure that isn't an issue at your business, it shows that you're listening.
00:20:25.680 --> 00:20:31.200
And again, you're just trying to make sure that there's so many opportunities for us to get drowned out and BS.
00:20:31.359 --> 00:20:38.079
And um anything you can do to just like put your best foot forward, show that you're actually listening, so that you're actually human.
00:20:38.240 --> 00:20:39.519
There's humanity behind it.
00:20:39.759 --> 00:20:45.599
It validates the mission of what your customers are trying to do because it shows the data that they're collecting is valuable.
00:20:45.759 --> 00:20:47.519
There's a lot that they can action on.
00:20:47.599 --> 00:20:52.559
They just need a partner who's able to make sure that it's served up in a way that is actually actionable.
00:20:52.799 --> 00:20:54.400
It's validation on all sides.
00:20:54.559 --> 00:21:00.079
So I I could go on a whole tangent about that one, but I'll think I think you phrased that perfectly.
00:21:00.240 --> 00:21:06.559
So I'm like I said, gonna jump into a little bit about Boast and your working relationship with our team.
00:21:06.720 --> 00:21:17.440
And so for everyone listening, if this is going to be on the podcast or if we're doing it in another format, you probably know what Shred is and you know what the scientific research and experimental development tax credit is.
00:21:17.599 --> 00:21:24.000
It's not the only tax credit in Canada, and it's not the only program that Boast helps customers claim, but it is our bread and butter.
00:21:24.079 --> 00:21:26.480
Um, just because, again, we're humans behind it.
00:21:26.559 --> 00:21:35.519
We're boast.ai, but I think our superpowers are in what the human intelligence is able to do because again, we listen to our customers.
00:21:35.680 --> 00:21:50.400
We're collecting the data, we're pulling all the resources that we need to craft that claim without making it a heavy lift for the folks that we work with, so that they're delighted at the end of the day, but also that they're getting everything that they're entitled to, because I think that's the biggest piece too.
00:21:50.640 --> 00:21:55.519
Shred, which is again the acronym for that long scientific research that I said earlier.
00:21:55.759 --> 00:21:56.720
It's an entitlement.
00:21:56.880 --> 00:22:02.000
You made the investment, you're tackling tough RD, you're doing something that's Is tackling technological uncertainty.
00:22:02.240 --> 00:22:13.279
We want to make sure you're getting the money you deserve because the government in Canada really wants to make sure that you're building new cool stuff and tackling these challenges on their soil and with the proper backing.
00:22:13.440 --> 00:22:17.680
So I'd love to know, Hugh, before Burst, what was your experience with Shred?
00:22:17.839 --> 00:22:20.960
What was your understanding of Shred or how did you maybe claim it in the past?
00:22:21.279 --> 00:22:32.799
Yeah, so we've always been involved to some degree in innovative work and right the way through from Shred, but also IREP grants as well, which just made a huge difference for the business in the early stages.
00:22:32.960 --> 00:22:39.119
I mean, look, we've really benefited from the support of these programs that the government provides.
00:22:39.279 --> 00:22:44.240
And, you know, they resulted in dramatic growth in the business in the early days, employment.
00:22:44.319 --> 00:22:56.640
You know, I feel that any investment that the government made, uh I mean, I'm proud to say that we took that and turned that into real dollars, not just for clients and providing services, but for employees and that and helped grow the business.
00:22:56.720 --> 00:23:02.079
So, you know, I feel very blessed that we have these programs and very appreciative as well.
00:23:02.400 --> 00:23:05.839
So the experience in the past we'd always been engaged and involved.
00:23:05.920 --> 00:23:10.720
In the early stage of the business, it was much more prevalent when we were doing more research and development.
00:23:10.880 --> 00:23:16.880
And then when that quietened down and we were focused on sales, then our IREP grants shrunk significantly.
00:23:16.960 --> 00:23:21.599
And then also for many years we didn't actually apply because we didn't feel we were doing innovative work.
00:23:22.000 --> 00:23:26.799
With this change in the business two years ago, where we did this dramatic.
00:23:27.279 --> 00:23:31.519
Now, actually really proud of kind of that we had the courage in retrospect to do it.
00:23:31.759 --> 00:23:42.480
But with this dramatic turnaround or leaping out into the air with this new approach to the platform, you know, it we were right into research and development.
00:23:42.640 --> 00:23:46.319
And, you know, we had the existing platform and the existing business.
00:23:46.400 --> 00:23:50.400
So I think that cushioned the risk a little bit from actually the business decision.
00:23:50.559 --> 00:23:52.319
But the technical risk was enormous.
00:23:52.480 --> 00:23:58.720
Like honestly, when we first looked at the beta of this, and it was taking this long to open up.
00:23:58.799 --> 00:24:01.839
And the guys were saying, I don't know if we can make it any faster.
00:24:01.920 --> 00:24:09.119
We can't optimize it because we want to keep in a way we want to, because we don't we've got to keep it broad, we can't focus down, you know.
00:24:09.359 --> 00:24:12.079
So it it was, you know, it was huge.
00:24:12.400 --> 00:24:14.079
It was massive uncertainty.
00:24:14.240 --> 00:24:26.079
And we were also diving into areas that we couldn't see products out there that were addressing both of the sides of needing technical integrity and flexibility.
00:24:26.559 --> 00:24:32.480
People were either really technical and really good but narrow, or really broad like a spreadsheet, but not depthful.
00:24:32.720 --> 00:24:39.519
So, you know, we we kind of knew with eyes open why we you had to make these trade-offs, but we wanted both.
00:24:41.519 --> 00:24:56.880
Taking some notes while we're talking, real quick, I'm so happy too that you identified IRAP at the top of this conversation, too, because I just want to put for some table sticks for anyone listening to this conversation the difference between Shred and tax credits versus IRAP and grants and things like that.
00:24:57.039 --> 00:25:01.680
You apply for IRAP when you have an idea, the government funds you, and you go off to the races.
00:25:02.079 --> 00:25:09.119
You execute that product, you if you dub become a product at the end of the day, you still did the research, so it's nothing lost.
00:25:09.359 --> 00:25:10.880
But that's the fundamental difference.
00:25:11.039 --> 00:25:14.160
Shred is something that you claim after you've already made the investment.
00:25:14.319 --> 00:25:27.359
You also can't double dip on iREP versus Shred, but truly innovative companies like refined data and like the projects that you're doing, they uncover things in the process of spending their IRAP grant that could be shreddable down the line.
00:25:27.519 --> 00:25:31.039
So again, it's a very, it's a virtuous cycle, if that makes sense.
00:25:31.200 --> 00:25:40.160
And so I want to just draw a point on that one for anybody who needs a little more education or would like to learn more about the different kinds of stages of when these programs become valuable.
00:25:40.240 --> 00:25:48.240
Because I think, to your point, perfect example of why you would want to claim IREP at that early stage, really build something else.
00:25:48.319 --> 00:26:03.039
And then when you need to make that performance uh layer actually accelerate, when you need to enhance the product and get the platform to a new phase, that's where you can really build on what you got going when you were working from the IREP pool and then claim the shred after the fact.
00:26:03.200 --> 00:26:13.759
So a little bit with the you could hear you could hear that we've been engaged in both, and yet if you ask me to explain and say it the way that you just did, no, because that's not our business.
00:26:13.839 --> 00:26:14.799
That's not what we do.
00:26:14.960 --> 00:26:23.920
We focus on the innovation, the programs we do rely on Boast to explain to us, to say where we may qualify, where we don't qualify.
00:26:24.160 --> 00:26:33.039
Even as I said, with 30 years of experience, it's just not been the core focus of obviously of what we've been doing, but it's just been an important adjunct.
00:26:33.359 --> 00:26:34.640
So, how did you please come?
00:26:34.960 --> 00:26:36.400
How did you first come across Boast?
00:26:36.720 --> 00:26:43.680
What was maybe the winning characteristics of what we were doing that was in the same ethos of what you guys are trying to accomplish over at Refined Data?
00:26:44.160 --> 00:26:44.799
Yeah.
00:26:45.279 --> 00:26:47.119
I'm gonna, it was a cold outreach.
00:26:47.200 --> 00:26:54.799
I think we got an email from Boast as a cold e-tra re uh re reach out, cold reach out, and that's how I think we met.
00:26:54.960 --> 00:27:00.480
And the timing was good because we had already been engaged in the beginnings of this research.
00:27:00.720 --> 00:27:08.240
We hadn't been actually applying for shred because the old product was mature and we weren't really doing anything particularly innovative with the old product.
00:27:08.400 --> 00:27:10.559
And so the it was just timing.
00:27:10.640 --> 00:27:37.839
The email came up, and I was intrigued by the name Boast AI because uh the key thing that had been a blocker for us in the past with uh shred credits was the amount of work that it takes to aggregate the core information, you know, and just really mundane work that wasn't automated, that took time to compile, that was a drain on the developer's time, complex to administer and grindy.
00:27:38.640 --> 00:27:39.440
Absolutely.
00:27:39.599 --> 00:27:48.240
You just made my sales pitch for me, but honestly, like that is the biggest thing, especially when you have developers working on something that is finance related, or vice versa.
00:27:48.400 --> 00:27:53.599
You have a finance team trying to figure out what your developers are doing, and they're not speaking the same language.
00:27:53.839 --> 00:28:03.519
And the many, many hours of those two very high-value members of your organization to just meet in the middle, it's it's not worth your time.
00:28:03.599 --> 00:28:06.400
And also, it's not gonna make a good shred claim.
00:28:06.480 --> 00:28:08.880
So I won't digress more from there.
00:28:09.039 --> 00:28:16.000
I want you to take the big speech on this one, but I'm so happy that you named that one too, because again, that's I think our biggest thing.
00:28:16.079 --> 00:28:17.359
It's the time saving.
00:28:17.599 --> 00:28:19.920
And also, again, the claim is gonna be accurate.
00:28:20.079 --> 00:28:28.160
You're not gonna have more time drains down the line because you got an audit because your finance guy didn't understand what the developer was saying, or vice versa.
00:28:28.319 --> 00:28:37.359
So everything you're saying, Paul, is true, but I'm gonna reframe it a little bit from our from our side of the fence, which was this.
00:28:37.440 --> 00:28:40.960
So I get the email, I'm like, okay, we do need to look into this.
00:28:41.039 --> 00:28:43.599
Uh, we don't have currently have somebody that we're working with.
00:28:43.759 --> 00:28:45.200
So I'm gonna take the call.
00:28:45.440 --> 00:28:52.480
But you you, like anybody that's listening to this, you yourself as well, you know what it's like when you're meeting people for the first time.
00:28:52.640 --> 00:28:54.960
What's what was present for me was skepticism.
00:28:55.119 --> 00:28:58.720
I know we need this problem solved, but is this the is this the group we want to go with?
00:28:58.880 --> 00:29:02.880
Because there's no shortage of of people that can help you with your shred claim.
00:29:03.039 --> 00:29:24.319
So, you know, we took the first meeting and the first call, and my questions were all around the automation and all around the degree to which we could relieve the look, there's a there is a burden in doing a shred claim, okay, of course, because you have to present the case for why your work is research and development and why it should even be funded through the tax credit.
00:29:24.480 --> 00:29:25.440
But that's fine.
00:29:25.519 --> 00:29:26.240
I don't mind that.
00:29:26.400 --> 00:29:28.319
That's fair, that's what's required.
00:29:28.480 --> 00:29:39.200
But the thing that that is problematic is that if it's taking a thousand hours to demonstrate a hundred hours of of shred work, that does that equation doesn't work.
00:29:39.359 --> 00:29:47.279
So the degree you can automate the data collection and the evidence for what you're saying, that that that's that was top of mind for me.
00:29:47.359 --> 00:29:50.160
That's the question that needed to be answered for me.
00:29:50.400 --> 00:29:55.200
And when we were in that meeting, I started to get more and more comfortable about the approach.
00:29:55.359 --> 00:29:57.440
So that was one place that I was listening for.
00:29:57.599 --> 00:30:03.039
And then, as I said earlier, we don't treat any of the people we work with as anything other than partners.
00:30:03.119 --> 00:30:06.240
So I was also really interested in who these people are.
00:30:06.400 --> 00:30:07.680
Like, what's the approach?
00:30:07.839 --> 00:30:15.440
You know, everybody says client service, how are they being on the call, which is an intangible, like who's who am I talking to?
00:30:15.839 --> 00:30:19.680
All these questions when we furt when you first meet somebody that are there in the background.
00:30:19.839 --> 00:30:21.519
Uh, and then how are they answering?
00:30:21.599 --> 00:30:23.599
And what questions are they asking as well?
00:30:23.759 --> 00:30:35.599
And you know, I I left the first call with a with a really solid feeling, like this there's a this group of checked boxes on so many uh different levels, most importantly the automation piece.
00:30:36.480 --> 00:30:37.279
I love that.
00:30:37.359 --> 00:30:40.720
I'm gonna pull up on something that you said that I had to draw down to again.
00:30:40.799 --> 00:30:46.720
It if you're spending a thousand hours to validate a hundred hours of work, that's not gonna math out.
00:30:46.880 --> 00:30:48.400
I'm like, the math needs to make sense.
00:30:48.640 --> 00:30:50.880
It's like that's a very easy question.
00:30:53.039 --> 00:30:59.440
Yeah, so again, I'm like, and I think a lot of people don't even bother, honestly, with the RD tax credits.
00:30:59.599 --> 00:31:06.079
This is something I see in the States, is this something we see in Canada as well, just because they think it's just a hill we cannot climb.
00:31:06.160 --> 00:31:09.440
Um, it's just like we're so focused on building the business.
00:31:09.920 --> 00:31:16.720
And so I'd love to know, could you put maybe a number on the time savings per claim that you gain from both?
00:31:17.119 --> 00:31:45.279
I know we said a thousand hours, but uh I would say, look, so you want actual numbers, I would say the uh gain was we probably spent between 15 and 20 percent of the time we normally would have spent on this, and that we spent more time thinking through the quality and the nature of the research and development, so it's not even just the hours that we saved.
00:31:45.440 --> 00:31:56.079
So if it was a thousand hours of work, which I don't think it was, maybe let's say it was 500 hours of work, we were down to I think probably less than a hundred hours of work.
00:31:56.160 --> 00:32:07.359
So that was a big savings, but it also meant that the time we were spending on was the time really to make a strong case for the work we did.
00:32:07.599 --> 00:32:10.400
Look, I like to keep things very simple.
00:32:10.559 --> 00:32:14.960
If we did RD work, I think and it it qualifies, great, let's submit it.
00:32:15.119 --> 00:32:18.240
If what we're doing isn't RD work, don't submit it.
00:32:18.319 --> 00:32:21.359
Like don't waste our time and don't waste the government's time.
00:32:21.599 --> 00:32:37.279
You know, so I take a pretty pragmatic view of that, but we were really able to focus the time in the place that made the most value for us in terms of the breadth of the claim, but also in anybody, I feel like anybody reviewing our claim, there wasn't fluff.
00:32:37.359 --> 00:32:42.160
We could really focus on what was true true RD work, and that was really important too.
00:32:42.480 --> 00:32:44.960
I don't know how you quantify that second piece, by the way.
00:32:45.039 --> 00:32:49.279
The first piece is easy, but that second piece was really, really important.
00:32:50.400 --> 00:32:53.359
Again, I I have a soft spot for my team.
00:32:53.440 --> 00:32:56.079
Um, I'm thinking immediately about Matt Rudershauser.
00:32:56.240 --> 00:33:06.559
I'm not sure if you've met with him directly, but he is one of our solutions consultants, and seeing him hop on a call and just immediately be able to understand exactly the nature of the work that's been done.
00:33:06.720 --> 00:33:19.279
I'm like, these people have talked to so many different providers, and also they've talked to their own internal teams, and they don't know what's going on as well as Matt from my team in five minutes talking to that person.
00:33:19.519 --> 00:33:24.240
So, again, making it personal while also still boosting the business.
00:33:24.400 --> 00:33:27.200
I think that our team is very in tune.
00:33:27.359 --> 00:33:32.319
And I think again, you can hear that time was precious to us, right?
00:33:32.559 --> 00:33:41.680
So we worked with one of your team members and he was terrific because we would submit our first cut at things, and he was very cut and dried.
00:33:42.000 --> 00:33:43.759
This qualifies, this doesn't qualify.
00:33:43.839 --> 00:33:48.079
I need more around this particular issue, and then I would just go around and execute.
00:33:48.160 --> 00:33:55.759
I listen, I was working for boast for that period, all right, in some respects, because this is not anything that I have a deep expertise in.
00:33:55.920 --> 00:34:04.480
I have expertise in the work we do, but not in what's required to present the research quality of the work, what qualifies, what doesn't qualify.
00:34:04.799 --> 00:34:22.639
So the no so it wasn't just the systems, but then as you said, the knowledge of the key project manager that we had the pleasure of working with, who I've raved about, by the way, that was really important because you know it just lent for speed, you know, it just it just lent for speed and focus and value.
00:34:23.280 --> 00:34:25.599
Yeah, it gives you the ability to actually perform.
00:34:25.760 --> 00:34:29.119
So again, this is if let this enough, let's not skip over that.
00:34:29.199 --> 00:34:30.000
It really does.
00:34:30.239 --> 00:34:32.480
It just gives us the ability to perform.
00:34:32.800 --> 00:34:40.880
Yeah, it's like you you know what you need to change or what you need to adjust or what you need to work on, and this just gives you more runway to actually do that.
00:34:41.840 --> 00:34:44.880
I'm gonna stop puffing my own chest here, but all right.
00:34:45.119 --> 00:34:47.440
What did the shred refund actually fund?
00:34:47.760 --> 00:34:49.920
So uh a hue fun question.
00:34:50.079 --> 00:34:58.000
And again, go as deep or as shallow as you want to get on this one, but hiring, the AI reporting work, runway, something else.
00:34:58.159 --> 00:35:01.920
What did take a minute, or do you want to just know what it funded?
00:35:02.320 --> 00:35:11.280
I would love both if you're willing to yeah, our first RD tax credit was$250,000, which was considerable for us.
00:35:11.440 --> 00:35:17.920
We're not a big company, we have eight employees, so you can do the math on our revenues, we are profitable, but we're not a huge company.
00:35:18.079 --> 00:35:28.960
So that made an enormous difference because we were transitioning from an old platform with limited capabilities to this new platform with very wide capabilities.
00:35:29.280 --> 00:35:38.960
And the thing that we struggled with, and we had talked to another company that had had a similar journey in the US, and they said you've got to build use cases.
00:35:39.039 --> 00:35:48.159
So we had to go out and build use cases with clients at very reduced rates because we didn't have it, we didn't have evidence or proof for what we were saying we could do.
00:35:48.400 --> 00:36:04.480
So the money from Shred allowed us to basically fund the business and fund those initiatives until we were able to demonstrate, which with the tool we were able to demonstrate really quickly within weeks, rather than typically a quarter or even a year.
00:36:04.639 --> 00:36:06.400
Within weeks, we were able to make that progress.
00:36:06.559 --> 00:36:08.480
That was the whole point of the platform.
00:36:08.639 --> 00:36:10.480
But the Shred Credit funded that.
00:36:10.559 --> 00:36:24.800
It also funded our ability to further develop the platform, to take the uh speeds down faster and faster until you know we were really hitting the marks and clients were getting this flexibility without any concession on the speed.
00:36:25.039 --> 00:36:26.960
So absolutely critical.
00:36:27.119 --> 00:36:33.840
And then in the second year, the Shred Credit raised to 350, reflecting an increased focus that we had in RD.
00:36:33.920 --> 00:36:36.079
And again, it's just been really critical.
00:36:36.239 --> 00:36:40.880
And we've had a 60% growth in revenue this year, and we're not even at the end of the year.
00:36:41.199 --> 00:36:47.519
Now, I mentioned that earlier that our client base was very steady, but our growth was slow.
00:36:47.679 --> 00:36:52.639
We were steady tracking 5% growth, you know, 7% growth.
00:36:52.960 --> 00:36:59.920
So this this tax credit plus the new platform has now changed at 20 years of very steady growth.
00:37:00.079 --> 00:37:03.679
We've suddenly had a jump this year, 60% revenue growth.
00:37:03.920 --> 00:37:08.159
That got us onto this whole idea of well, if we can do that, can we 10x the company?
00:37:08.239 --> 00:37:10.079
And what does that journey look like?
00:37:10.559 --> 00:37:19.199
So that's also one of the byproducts of the the having the funds to do all of this is we can now make the investment to really see if we can have the business.
00:37:21.760 --> 00:37:27.039
But that's probably a good analogy, but really expand dramatically, right?
00:37:27.360 --> 00:37:29.360
So that's always my hesitations.
00:37:29.760 --> 00:37:31.519
What the right word I want to use.
00:37:32.000 --> 00:37:33.519
Well, welcome to my life, you.
00:37:34.639 --> 00:37:39.840
No, that is again music to my ears, because again, you had you were steady.
00:37:40.000 --> 00:37:43.440
Um, but again, that 60% figure is unreal.
00:37:43.519 --> 00:37:44.159
Like that is hand.
00:37:44.559 --> 00:37:49.199
Well, it's not it's unprecedented in the history of of our company, this this group of people.
00:37:49.360 --> 00:37:55.920
So you that's all and listen, without the tax refund, honestly, we I'm just gonna be really we couldn't have done it.
00:37:56.000 --> 00:37:58.159
We just couldn't have demonstrated that growth.
00:37:58.320 --> 00:38:09.840
And this is this is where I'd this is where as a Canadian I feel really good because it's our tax dollars collectively, Canadian tax dollars that are getting passed back to into this case to refined data.
00:38:10.000 --> 00:38:16.559
And I'm really proud that we're able to then reflect that back in growth in the organization and growth in in the company.
00:38:16.719 --> 00:38:19.920
So I'm that for me, like I don't know, that's meaningful for me.
00:38:20.000 --> 00:38:21.519
That means a lot to me.
00:38:22.159 --> 00:38:22.719
I love it.
00:38:22.880 --> 00:38:35.039
Again, because it's part of your now strategy and your funding strategy and your growth strategy, and it's not just something that's uh like nice to have or uh jury on Sunday, which I think a lot of people just assume RD tax credits are.
00:38:35.199 --> 00:38:41.599
No, it gives you again, you guys are building something net new and you're gonna explode broadband.
00:38:41.840 --> 00:38:43.920
I don't know if it's uh that's right.
00:38:44.079 --> 00:39:06.559
Yeah, and um, I think again, like it's just uh yes, like this is exactly the kind of use case that I want to make sure that we're highlighting because people will be remiss to something that could transform their business, which like I don't think it's an understatement to say we are on this journey with you, and we're gonna continue to be on this journey with you, and we've helped you guys in a very meaningful way.
00:39:06.719 --> 00:39:22.800
So I know we only have about five minutes left, but I'm gonna hit you with two more questions that I'd love just maybe a concise can you point some one initiative that really got liftoff because you worked with BOST and you got shred funding.
00:39:22.960 --> 00:39:23.760
Yeah, yeah.
00:39:23.920 --> 00:39:28.639
So we've been able to explore a lot of different avenues because the platform is so flexible.
00:39:28.719 --> 00:39:34.719
And now what's happened is we've been pulled into an avenue that we think is is of most interest to clients.
00:39:34.800 --> 00:39:40.239
Because if clients get a choice about where they're going to use you, they're gonna use you primarily where the biggest problems are.
00:39:40.320 --> 00:39:42.800
And we're seeing that that is in finance, right?
00:39:43.039 --> 00:39:55.519
Now, the data, as I said, was fast, but in finance, we're now pulling in massive amounts of numbers, but we're also pulling in a very high degree of context as well.
00:39:55.679 --> 00:40:04.000
So it's like every major number now has an analysis or has a why that number is the way that it is.
00:40:04.320 --> 00:40:13.280
And then month over month, we're giving our clients the ability to track how that both that number is changing and that why is changing.
00:40:13.440 --> 00:40:17.440
And that is now starting to get it into huge data sets.
00:40:17.760 --> 00:40:32.000
So again, we've got the performance, but because everybody's managing those reports and those numbers differently, you know, they might all be using up the YADI or MRI, but that's really where the story begins, not where it ends.
00:40:32.159 --> 00:40:38.400
Uh, about how they analyze, report, and create strategy around those numbers.
00:40:38.559 --> 00:40:51.280
So in supporting that, we, you know, and also in expanding our offering and now looking into the US as well, where typically the firm sizes are anywhere five to ten times larger, even though we're with some Canada's largest here.
00:40:52.079 --> 00:41:00.400
You know, we're preparing ourselves then for scale, not just scale in terms of revenue, but scale in terms of use and application of the platform as well.
00:41:00.559 --> 00:41:04.079
And so that's been a prime technical focus and business focus.
00:41:04.639 --> 00:41:16.000
I love that you close that out on the scale picture of it all, because everything that you were saying, too, from the risk to finance kind of pit it that the uh platform is taken and that your focus is taken too.
00:41:16.400 --> 00:41:21.440
Month over month, being able to say how and why things changed.
00:41:21.760 --> 00:41:25.519
This is all so actionable again.
00:41:25.760 --> 00:41:32.480
And it's context, and it's things that again, like, yeah, the sky seems like the limit.
00:41:32.559 --> 00:41:38.960
And I'm sorry, but I'm like, I actually do not see any barriers to this 10x that we've been talking about for a little bit.
00:41:39.599 --> 00:41:42.400
Expect to see one, but I can see one sitting right in front of you.
00:41:43.039 --> 00:41:45.280
You said the person sitting right in front of you.
00:41:45.440 --> 00:41:47.920
Look, we're getting a lot of coaching and support and training.
00:41:48.000 --> 00:42:03.119
We're entering the program that's really focused around this because there's a great, and I heard this recently from the program we're getting into, which I just love because it's it seems to so resonate for me, is that if you look, the bottleneck is always at the top of the bottle.
00:42:03.519 --> 00:42:17.119
And uh, and so you know, it I've got to alter how I think about things, and I feel very much like one of those trapeze artists, you know, that's let go of one bar, which is kind of everything I've known to create a business from nothing to you know, two, three, four million dollars of business.
00:42:17.199 --> 00:42:18.880
Now, how are we gonna have that scale?
00:42:19.039 --> 00:42:30.079
And I'm spinning around in the air right now, and I'm counting on the program we're in and being rigorous with myself to to grab that bar on the other side, but we are we're not there right now.
00:42:30.159 --> 00:42:32.960
We're still spinning, and I think it's but I think it's healthy.
00:42:33.840 --> 00:42:35.280
It's incredibly healthy.
00:42:35.519 --> 00:42:45.039
And yeah, Hugh, again, so many title options from everything that you just said there for you know what this podcast episode would be and what the webinar would be.
00:42:45.119 --> 00:42:47.119
But again, bottleneck is always at the top.
00:42:47.280 --> 00:42:57.760
I think that's a big point that I want to make sure again gets repeated because it's ever been phrased that way, even though it should really be inherent in the entire analogy.
00:42:58.000 --> 00:42:58.880
That is the case.
00:42:59.119 --> 00:43:03.760
But I think that a lot of people just think, like, oh no, I'm just hitting a roadblock.
00:43:03.840 --> 00:43:08.480
But no, it there's a lot that is unlocked when you get there, so don't give up, chip away at it.
00:43:08.719 --> 00:43:12.639
And then also, again, the trapeze artist of it all.
00:43:12.800 --> 00:43:19.599
That is some injury that I can totally relate with, and also really visualized and made peanut butter and jelly what you guys are doing.
00:43:19.760 --> 00:43:22.159
Because yeah, again, you're taking risk.
00:43:22.639 --> 00:43:27.920
But but you do see where you want to land, and uh it's not without foresight.
00:43:28.000 --> 00:43:29.760
So, Hugh, this has been fantastic.
00:43:29.920 --> 00:43:32.159
I cannot thank you enough for joining us on the show.
00:00:03.600 --> 00:00:11.039
Welcome to What the Tech, the show where we talk to the founders and operators building innovative companies across North America and dig into how they fund the work that matters.
00:00:11.199 --> 00:00:21.600
Today I'm joined by Hugh Molineau, co-founder and president and CEO of Refined Data, a Toronto software company that's been helping commercial real estate firms make sense of their data since 2007.
00:00:21.760 --> 00:00:26.399
If you've ever been told that RD tax credits are just for startups, today's conversation is for you.
00:00:26.559 --> 00:00:28.960
My guest has been building software since the mid-90s.
00:00:29.120 --> 00:00:31.600
He co-founded Refined Data in 2007.
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For nearly two decades, the company has been the operating layer behind some of the biggest names in Canadian commercial real estate.
00:00:38.320 --> 00:00:41.840
Think Colliers, Morgert, King said, Benzel Green Oak.
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And here's the interesting part.
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Instead of coasting, Refined Data is in the middle of reinventing its product, moving from compliance software into AI-assisted investor reporting, built on simple promise.
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Every number traces back to its source.
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Numbers you can defend.
00:00:55.520 --> 00:01:00.000
That's a promise that takes real engineering to keep, and it's the kind of work Shred was designed to fund.
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Hugh has been claiming with Boast, and he's here to talk about the RD tax credits behind the pivot, what the claim processes actually look like, and what non-dilutive capital has meant for a mature company that's still betting on innovation.
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Hugh Mono, president and CEO of Refined Data.
00:01:14.560 --> 00:01:16.239
Welcome to What the Talk.
00:01:17.680 --> 00:01:22.400
You've been building software since the mid-1990s and co-founded Refined Data in 2007.
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Can you introduce yourself and describe what your role as president and COO actually looks like day-to-day?
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Yeah, well, it's evolving.
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And, you know, obviously with AI, it's putting a lot of pressure on technology to grow quickly.
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You know, it feels a lot like the mid-90s when I first started, when the internet was new and exciting.
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And now obviously we have AI, and that's really exciting.
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So my role is evolving in that it used to be very tactical, high contact with clients, delivery of projects, and being really involved in that.
00:01:54.959 --> 00:01:59.439
But now we are looking to grow the business by a factor of 10.
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And everything that worked for me in terms of the hustle and all the founder mentality, you know, that stuff I'm discovering is working against me now.
00:02:07.920 --> 00:02:21.039
And so really what I'm what I'm doing is moving the operations down into the into a delivery group that we've created and also getting the programmers more involved as well, so that they're more client-facing too.
00:02:21.120 --> 00:02:23.039
And I'm really focusing now on strategy.
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Like what is it going to take to 10x our business?
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And what do I need to focus on to make that happen?
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Which is going to be a transformation both of myself, but also of the business and processes around the business too.
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So 10X, that is a lofty goal, but also something that I think is achievable with all of the not only prowess that you have from working in the space for as long as you have, but also with the new tools that have kind of emerged in our space and the ability for AI to give you superpowers to really make sure that this data can get collected and then actioned upon.
00:02:53.759 --> 00:03:01.520
So I'd love to build a little bit more about why you pull what pulled you towards real estate technology, I guess, in the first place.
00:03:02.080 --> 00:03:10.240
Yeah, so way, way back at the beginning of my career, which is now more than 30 years ago, my degree was in uh environmental sciences and engineering.
00:03:10.319 --> 00:03:22.560
So we were doing it, it was just the late 80s where there was this big focus in real estate on redeveloping sites that were contaminated or that had historic environmental issues.
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And so that was a very exciting and new field.
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It's where I started my career.
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Uh so I did a lot of engineering work in terms of things like the remediation and cleanup of Canadian Air Force bases in Germany, also the remaining cleanup and remediation around assessing liabilities for big mergers and acquisitions in the US.
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And I noticed sort of about 10 years into that career that there was an enormous amount of data that was getting generated, which I'm really dating myself, but was sitting in filing cabinets.
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And so the whole entry into technology was to take all of this information that real estate companies were storing and digitize it and make it available more broadly, particularly so that people could get a view across their portfolios of what was happening.
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So that was really, and that was so founded in real estate that the technology piece followed that.
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Yeah, and that makes perfect sense too.
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I mean, there's a treasure trove of data, and I'm gonna date myself too.
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Uh, I remember back when I started my career as a content marketer, digital transformation, and turning those file cabinets into actual actionable resources for your team was the name of the game.
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So uh very much the same journey when you look at it, right?
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Extremely similar, honestly.
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And I think it's just a through line throughout our entire industry.
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And when I say our industry, I just mean the innovation space.
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Making sure, again, whether it's real estate or whether it's any place where you're collecting all of this data, you can actually make it actionable.
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I think that's really what, again, we at Bhost are trying to do.
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We're not just collecting it for our own health, and we don't just need to be busybodies and know all about your finances.
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We really want to make sure that you can take the efforts that your RD teams, for instance, are doing and deliver better products to your customers.
00:05:07.600 --> 00:05:13.759
And I think that's a sympathy between refined data and at BOST because you guys have made the data very actionable.
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And while it's for one sector, the ethos is very similar.
00:05:16.879 --> 00:05:22.720
Um, I think it just really is about being smart with it, which again, I mentioned AI earlier.
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It gives us superpowers.
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I'm probably a broken record when I'm on the podcast talking about how AI isn't replacing anybody, it's just making them more efficient.
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I'd love to talk a little bit more about that and the business.
00:05:35.839 --> 00:05:39.920
So we talked about you, but let's bring it back to refined data a little bit.
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What is the elevator bench?
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Yeah, so essentially the focus of the company is we work a lot with our clients around aggregating and managing their finances.
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So the big accounting system in real estate or the big ones are MRI and YARDI, and particularly in Canada, yeah, YADI's really down.
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And so uh nobody ever questions the numbers coming out of YADI.
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What they're questioning is the why behind those numbers.
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So if you have a building and NOI has slipped, as net operating income has slipped in that building, uh, nobody's questioning if the numbers are accurate.
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People are questioning, well, why did that happen in that particular building?
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And so our software is really taking the numbers and then coloring those numbers with the why, the narrative, the views of the site accountants, the views of other operational teams inside real estate.
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And that's allowing the CEO to really see what's happening behind the numbers that are telling the true story of how the portfolio is performing.
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Oh, that makes perfect sense.
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And I actually think my next question kind of tees you up to expand on all that, if uh you won't mind me asking you a little bit of a repetitive one.
00:06:48.160 --> 00:06:55.680
But again, Refined has started an EHS in compliance software for real estate and is now positioned around financial planning and investor reporting.
00:06:55.839 --> 00:06:57.279
Walk us through that evolution.
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So you kind of just stood there, but I'd love to hear what the market tell you on that.
00:07:01.680 --> 00:07:16.160
No, well, I think you know, it's an interesting segue into our relationship with BOST because uh essentially what happened was in 1994, we kind of hit the edge or the end of the technology lifecycle for our core platform.
00:07:16.319 --> 00:07:26.800
So our platform is enterprise great, it's used by some of Canada's largest real estate companies, uh REITs and uh also investment companies as well.
00:07:27.040 --> 00:07:30.079
So it's enterprise great, and it had hit the end of its life cycle.
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So we were looking at a complete redesign of the platform or re-engineering of the platform as well as redesign.
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And so AI's arriving just at the same time.
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You know, we're really focused and always have been on the data management, data acquisition, data treatment.
00:07:48.800 --> 00:07:53.199
Um and so these two opportunities intersected at the same time.
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And we decided uh essentially to change the platform into something that was massively configurable.
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Um I could talk more about that after, like the decision behind that and what that's made available, but there was a dramatic change in our foundational view.
00:08:09.279 --> 00:08:11.439
So I will actually talk a little bit about that now.
00:08:11.600 --> 00:08:19.439
So before we had built the industry, what we felt was the industry best platform for dealing with risk in real estate.
00:08:19.920 --> 00:08:24.240
But it was very much about explaining to clients why they needed our platform.
00:08:24.639 --> 00:08:32.159
The transformation was saying, well, okay, let's put that to one side and let's build something so flexible that we can actually meet the clients where they are.
00:08:32.720 --> 00:08:42.559
And that transformed the business because it suddenly was us arriving at a client's site and saying, Oh, offices and saying, what are the problems that you're most dealing with?
00:08:42.799 --> 00:08:49.200
And so we got pulled out of risk and into finance, which is obviously a big focus in real estate.
00:08:49.440 --> 00:08:52.399
And the kinds of problems that we were solving changed dramatically.
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The platform had to change dramatically to deal with that, those new that new environment and the kinds of demand that financial uh systems need to meet.
00:09:04.960 --> 00:09:14.720
Also integrating AI in a way that was responsible, that made sense, because AI can't be 98% correct with finance, it has to be 100% correct.
00:09:14.879 --> 00:09:28.879
And so using AI in a very um thoughtful, I would say, thoughtful way, recognizing its limitations, let recognizing the opportunities that it built as well.
00:09:29.120 --> 00:09:33.440
That again, a lot of research and development, a lot of new ways of doing things.
00:09:33.679 --> 00:09:40.720
So that's a long answer maybe to your question, but it maybe provides a little bit of a flavor of the world that we were in, you know, two years ago.
00:09:41.039 --> 00:09:47.120
No, I think it actually tees up a bunch of points that I want to put a fine line on from everything that you just said there, too.
00:09:47.279 --> 00:10:02.080
Again, when you're moving from risk to finance, maybe on paper you do that as a different buyer, but it is still fundamentally it's the budget, it's the actual ability of the business to be able to be effective and have the funding that they need for everything in place.
00:10:02.320 --> 00:10:12.080
So I think, too, you listened and you met the buyer where they're at, and you made a pivot that was going to deliver something valuable for this customer, for your client base.
00:10:12.320 --> 00:10:14.000
That wasn't just reinventing the wheel.
00:10:14.080 --> 00:10:23.840
So again, you saw the limitations of the previous platform, you listened to what your ICP actually needed, and you started developing something that actually builds towards that.
00:10:24.000 --> 00:10:26.720
And you weren't just, again, taking something off a shelf.
00:10:26.799 --> 00:10:37.600
It was very much building something net new because there wasn't a prowess for or a product that could deliver what you guys are delivering in terms of that intelligence and really actioning.
00:10:37.679 --> 00:10:42.080
Again, I keep on reusing the word actioning, but I think that's the critical piece here.
00:10:42.240 --> 00:10:44.720
It's something that people can actually do something with.
00:10:44.879 --> 00:10:53.440
And um, I think a lot of products tell people sycophantically sometimes what they want to hear, or sometimes just like, oh, I need data for the sake of reporting.
00:10:53.519 --> 00:10:56.320
It's not for the sake of reporting, it's for the sake of the business.
00:10:56.480 --> 00:11:04.799
So you're evolving because it's what your customers need, and you're not moving away from your buying audience, you're just making sure that you're serving them even better.
00:11:04.960 --> 00:11:06.799
So wanted to put a big thing.
00:11:07.360 --> 00:11:10.960
I think that's you that's very insightful.
00:11:11.039 --> 00:11:20.159
And I think that it raises a whole series of interesting challenges technically, but also interesting challenges from the business perspective as well.
00:11:20.320 --> 00:11:34.240
Because, and this is where the re research and development came in, because in developing a really see if you focus on one business set from a technology platform perspective, you can actually really optimize to meet that set.
00:11:34.399 --> 00:11:43.840
So, you know, for us we were dealing with large amounts of data, and so we optimized for speed, and we optimized everything in the platform, the early first version of the platform for speed.
00:11:44.080 --> 00:11:47.440
Now we're saying, okay, we're not going to focus on that anymore.
00:11:47.519 --> 00:11:49.279
We're going to focus on flexibility.
00:11:49.600 --> 00:11:53.279
And what we had to give up for the flexibility initially was speed.
00:11:53.360 --> 00:11:55.440
Because, you know, the app was incredibly flexible.
00:11:55.519 --> 00:11:58.399
It was almost like a spreadsheet that was also a database, too.
00:11:58.559 --> 00:12:04.720
But the problem was the first iteration of the software took 60 seconds to load the first page.
00:12:04.799 --> 00:12:15.120
You know, there was real challenges in providing flexibility, but then also managing the fact that it has to operate, you know, at a cadence that's gonna serve businesses.
00:12:15.200 --> 00:12:17.360
You know, it can't take 60 seconds to open up.
00:12:17.600 --> 00:12:31.039
So that was the technical challenges that we confronted, but then also rewiring ourselves to stop talking about what we did and what the platform could do and start to listen to it for clock to clients.
00:12:31.200 --> 00:12:33.279
What is the what are the issues they're facing?
00:12:33.440 --> 00:12:36.399
You know, where is the bottlenecks in the workflow?
00:12:36.559 --> 00:12:44.320
Who's starting to ask questions and questions and questions and and then this interesting issue of the clients saying to us, well, what do you have?
00:12:44.399 --> 00:12:45.600
What does your platform do?
00:12:45.759 --> 00:12:47.200
And we're saying, Well, what do you need?
00:12:47.360 --> 00:12:52.639
And they're saying, Well, we could tell you that, but we're interested in what do you do so we can map it onto what we need.
00:12:52.720 --> 00:13:00.320
And so a lot, you know, we again can talk more about that if it's if it's um if it's if necessary, if it's interesting.
00:13:00.480 --> 00:13:03.840
But you know, it's it's been change on so many foot fronts.
00:13:03.919 --> 00:13:17.600
Uh, and again, the ability to capture that, capture the challenges was one of the things that I was really, really happy about with uh Bose AI because we were focused so much on the business changes that we were dealing with.
00:13:17.919 --> 00:13:24.480
We also had to capture the technology changes, but in some respects those were secondary in terms of what was on the table for me.
00:13:26.240 --> 00:13:29.440
I'm moving this seamlessly into my next arena of questions here.
00:13:29.840 --> 00:13:31.840
That's exactly where I want to dig into next.
00:13:32.000 --> 00:13:42.879
So, I mean let's dig into the innovation story too, and about what's changed, because I think the context we've laid a good groundwork for, but I'd love to know what is the core technical innovation behind the current platform?
00:13:43.039 --> 00:13:45.600
What did you have to build that didn't exist before?
00:13:45.759 --> 00:13:46.799
Or off the shelf.
00:13:47.039 --> 00:13:48.559
So again, a basic cliche.
00:13:50.000 --> 00:13:56.159
One of the things that we were really interested in is technology's been around, as we both said, for 30 years.
00:13:56.240 --> 00:14:01.039
And then how come companies are using spreadsheets so prolifically?
00:14:01.200 --> 00:14:07.200
And what is it that we're just not able to provide people that forces them to use spreadsheets?
00:14:07.360 --> 00:14:12.000
And one of the things that we could see was that spreadsheets are massively flexible.
00:14:12.159 --> 00:14:16.000
You can fire them up, you can apply them to whatever business problem you're faced with.
00:14:16.080 --> 00:14:22.799
You're not in no way, shape, or form are you constrained from solving an immediate problem very quickly.
00:14:22.879 --> 00:14:25.200
And then there's very rich graphics you can generate.
00:14:25.440 --> 00:14:26.480
It's that it's just great.
00:14:26.559 --> 00:14:27.679
Spreadsheets are amazing.
00:14:27.919 --> 00:14:28.320
They are.
00:14:28.799 --> 00:14:36.159
With all the drawbacks that anybody listening to this would know about uh spreadsheets, they're not great at sharing data, they're not enterprise grade.
00:14:36.320 --> 00:14:42.399
Somebody can walk out of your office with the spreadsheet on the computer, you know, and who knows where that file will end up.
00:14:42.480 --> 00:14:46.639
So there's a there's a lot of considerations that you trade off for that flexibility.
00:14:46.879 --> 00:14:51.120
So we arrived with all of those considerations handled as an enterprise data system.
00:14:51.279 --> 00:14:52.879
What we lacked was flexibility.
00:14:53.039 --> 00:14:59.759
So we said, okay, let's show constraint to the four winds, let's just look at this flexibility issue.
00:15:00.000 --> 00:15:19.039
And so that's what we did was we built an incredibly flexible prob uh platform that built in all kinds of reporting capabilities that allowed us to the ability to duplicate exactly the format of clients' existing financial and other reports, did a great job with that.
00:15:19.279 --> 00:15:22.320
That took time, engineering and expertise.
00:15:23.039 --> 00:15:31.279
And then finally, once we built this phenomenal platform, the beta version, we had to deal with that it takes took 60 seconds to load the first page.
00:15:31.440 --> 00:15:37.600
So flexible as as crazy as you'd want it to be, performance was out of the window.
00:15:37.679 --> 00:15:47.039
And so really we we just spent a year just attacking the performance issue, looking for re-engineering it while preserving the flexibility, you know.
00:15:47.200 --> 00:15:58.240
So I do like to think of us, we don't talk about this because we're so focused on the business, uh the business uh benefits that we're delivering, the what matters to the business audience.
00:15:58.960 --> 00:16:13.200
So I never talk about this, but from the technology perspective, we're a spreadsheet that's a database and as easy to use and as flexible and as you know, we produce uh board-ready reports, you know, from the data that we're tracking.
00:16:13.519 --> 00:16:15.039
Yeah, but you can hear my enthusiasm.
00:16:15.120 --> 00:16:17.519
And my enthusiasm isn't about how great the platform is.
00:16:17.679 --> 00:16:19.279
We frankly don't care about that.
00:16:19.679 --> 00:16:25.440
My enthusiasm is because of what we're able to deliver to our clients and what we're able to deliver to the market.
00:16:25.600 --> 00:16:37.039
I'm more excited about the business benefits that we're delivering, this ability to source and tell the story behind your numbers, the ability to point this at a whole range of different problem sets.
00:16:37.120 --> 00:16:55.919
We've solved for financial issues, we've solved for due diligence that used to be managed on spreadsheets, we've solved for sustainability issues, we've solved for statement of values, insurance issues, all on the same platform, you know, just like you would use a spreadsheet for dealing with all of this wide variety of business challenges.
00:16:56.159 --> 00:17:01.200
Yeah, I'm gonna stop there because I honestly got me on a topic that I could just yeah.
00:17:01.600 --> 00:17:07.519
No, it's incredible too, and your enthusiasm, again, it shows again that you're listening to the customer challenge.
00:17:07.759 --> 00:17:11.359
It's not just creating an innovative platform for the sake of intuitive innovation.
00:17:11.839 --> 00:17:17.920
You want to be sure that you're solving problems that you've heard acutely in the field, and that you're again, it's the listening.
00:17:18.000 --> 00:17:23.599
And that's something that I think really I want to say something else about that.
00:17:23.680 --> 00:17:26.480
Uh, because I think this also bears on our experience with both.
00:17:26.720 --> 00:17:34.000
So I think it's very related to the conversation we're having here, which is this you know, we our client retention has been really strong.
00:17:34.160 --> 00:17:45.279
Like we we have our original founding clients from 20 years ago, and uh you have almost no client turnover, except a couple of situations where the need for the platform changed.
00:17:45.440 --> 00:18:01.200
So but we've had this enduring thing, and so so our client relationships have been very central, but I was always a little bit feeling with our clients that while the platform was good, it wasn't ever quite what they really wanted.
00:18:01.359 --> 00:18:09.839
There was always improvements that we said were coming down the pipeline, it was slow to implement, we were choked off by you know development resources and the like.
00:18:10.079 --> 00:18:11.359
All of that has gone.
00:18:11.440 --> 00:18:20.079
And what I'm really excited about is actually not how we feel about our clients, but the delight that we're able to generate for our clients.
00:18:20.240 --> 00:18:29.200
They are really happy because finally they're able to get exactly what they want, tailored down to the T, exactly what they want.
00:18:29.440 --> 00:18:40.720
And there's been another interesting benefit, which is that as we've solved one business problem with the platform, we can then solve an adjacent business platform that's problem that's just the extension of the platform.
00:18:40.799 --> 00:18:46.960
So all that data that was useful for the first one, about 50% of that data is now useful for the second issue.
00:18:47.279 --> 00:18:58.799
And then we're tailoring that second issue, which means that the training almost disappears because people are using something that looks a lot like the spreadsheet version of what they were doing anyway.
00:18:58.960 --> 00:19:03.839
So, anyway, but it's the client delight that's got me so lit up.
00:19:04.079 --> 00:19:09.039
And and that was central to you know the journey that we took with Bost as well.
00:19:09.519 --> 00:19:16.079
Okay, so we're gonna move into a little bit more of the Bose-centric conversation because basically, like you said, delight.
00:19:16.400 --> 00:19:17.839
That's really what we want to generate.
00:19:17.920 --> 00:19:22.720
And again, it sounds a little uh trivial when we're just saying, no, I want to delight my clients.
00:19:22.799 --> 00:19:25.599
It's I'm 62 years old.
00:19:25.680 --> 00:19:26.799
I'm at the end of my career.
00:19:26.880 --> 00:19:28.319
I'm approaching the end of my career.
00:19:28.400 --> 00:19:32.720
I hope I'm gonna still have many, many years left to be passionate about.
00:19:32.799 --> 00:19:39.759
But I just but to your point, what matters to me now is my relationships, my business relationships, my personal relationships.
00:19:39.920 --> 00:19:42.240
I think that's very natural as as you get older.
00:19:42.319 --> 00:19:48.400
Those things now start to come very sharply into focus in a way that in my 30s maybe it wasn't quite so important, right?
00:19:48.480 --> 00:19:50.480
There's maybe a little bit more about me.
00:19:50.880 --> 00:19:58.240
No, but I honestly too, and it's not like clock isn't ticking or anything like that, but there's so much crap.
00:19:58.400 --> 00:20:09.039
I'm gonna use bullshit, sorry, that uh you get served up when you're in this space and where you're offered solutions and where again it's okay, I can test this out, but um, clock is ticking.
00:20:09.119 --> 00:20:16.400
Uh, we actually have to get this to market, or we actually have to make sure that our customers are happy and that they're paying for what they want and that they want to keep what they want.
00:20:16.559 --> 00:20:25.599
So, even your point about like the churn um and how you've been able to really make sure that isn't an issue at your business, it shows that you're listening.
00:20:25.680 --> 00:20:31.200
And again, you're just trying to make sure that there's so many opportunities for us to get drowned out and BS.
00:20:31.359 --> 00:20:38.079
And um anything you can do to just like put your best foot forward, show that you're actually listening, so that you're actually human.
00:20:38.240 --> 00:20:39.519
There's humanity behind it.
00:20:39.759 --> 00:20:45.599
It validates the mission of what your customers are trying to do because it shows the data that they're collecting is valuable.
00:20:45.759 --> 00:20:47.519
There's a lot that they can action on.
00:20:47.599 --> 00:20:52.559
They just need a partner who's able to make sure that it's served up in a way that is actually actionable.
00:20:52.799 --> 00:20:54.400
It's validation on all sides.
00:20:54.559 --> 00:21:00.079
So I I could go on a whole tangent about that one, but I'll think I think you phrased that perfectly.
00:21:00.240 --> 00:21:06.559
So I'm like I said, gonna jump into a little bit about Boast and your working relationship with our team.
00:21:06.720 --> 00:21:17.440
And so for everyone listening, if this is going to be on the podcast or if we're doing it in another format, you probably know what Shred is and you know what the scientific research and experimental development tax credit is.
00:21:17.599 --> 00:21:24.000
It's not the only tax credit in Canada, and it's not the only program that Boast helps customers claim, but it is our bread and butter.
00:21:24.079 --> 00:21:26.480
Um, just because, again, we're humans behind it.
00:21:26.559 --> 00:21:35.519
We're boast.ai, but I think our superpowers are in what the human intelligence is able to do because again, we listen to our customers.
00:21:35.680 --> 00:21:50.400
We're collecting the data, we're pulling all the resources that we need to craft that claim without making it a heavy lift for the folks that we work with, so that they're delighted at the end of the day, but also that they're getting everything that they're entitled to, because I think that's the biggest piece too.
00:21:50.640 --> 00:21:55.519
Shred, which is again the acronym for that long scientific research that I said earlier.
00:21:55.759 --> 00:21:56.720
It's an entitlement.
00:21:56.880 --> 00:22:02.000
You made the investment, you're tackling tough RD, you're doing something that's Is tackling technological uncertainty.
00:22:02.240 --> 00:22:13.279
We want to make sure you're getting the money you deserve because the government in Canada really wants to make sure that you're building new cool stuff and tackling these challenges on their soil and with the proper backing.
00:22:13.440 --> 00:22:17.680
So I'd love to know, Hugh, before Burst, what was your experience with Shred?
00:22:17.839 --> 00:22:20.960
What was your understanding of Shred or how did you maybe claim it in the past?
00:22:21.279 --> 00:22:32.799
Yeah, so we've always been involved to some degree in innovative work and right the way through from Shred, but also IREP grants as well, which just made a huge difference for the business in the early stages.
00:22:32.960 --> 00:22:39.119
I mean, look, we've really benefited from the support of these programs that the government provides.
00:22:39.279 --> 00:22:44.240
And, you know, they resulted in dramatic growth in the business in the early days, employment.
00:22:44.319 --> 00:22:56.640
You know, I feel that any investment that the government made, uh I mean, I'm proud to say that we took that and turned that into real dollars, not just for clients and providing services, but for employees and that and helped grow the business.
00:22:56.720 --> 00:23:02.079
So, you know, I feel very blessed that we have these programs and very appreciative as well.
00:23:02.400 --> 00:23:05.839
So the experience in the past we'd always been engaged and involved.
00:23:05.920 --> 00:23:10.720
In the early stage of the business, it was much more prevalent when we were doing more research and development.
00:23:10.880 --> 00:23:16.880
And then when that quietened down and we were focused on sales, then our IREP grants shrunk significantly.
00:23:16.960 --> 00:23:21.599
And then also for many years we didn't actually apply because we didn't feel we were doing innovative work.
00:23:22.000 --> 00:23:26.799
With this change in the business two years ago, where we did this dramatic.
00:23:27.279 --> 00:23:31.519
Now, actually really proud of kind of that we had the courage in retrospect to do it.
00:23:31.759 --> 00:23:42.480
But with this dramatic turnaround or leaping out into the air with this new approach to the platform, you know, it we were right into research and development.
00:23:42.640 --> 00:23:46.319
And, you know, we had the existing platform and the existing business.
00:23:46.400 --> 00:23:50.400
So I think that cushioned the risk a little bit from actually the business decision.
00:23:50.559 --> 00:23:52.319
But the technical risk was enormous.
00:23:52.480 --> 00:23:58.720
Like honestly, when we first looked at the beta of this, and it was taking this long to open up.
00:23:58.799 --> 00:24:01.839
And the guys were saying, I don't know if we can make it any faster.
00:24:01.920 --> 00:24:09.119
We can't optimize it because we want to keep in a way we want to, because we don't we've got to keep it broad, we can't focus down, you know.
00:24:09.359 --> 00:24:12.079
So it it was, you know, it was huge.
00:24:12.400 --> 00:24:14.079
It was massive uncertainty.
00:24:14.240 --> 00:24:26.079
And we were also diving into areas that we couldn't see products out there that were addressing both of the sides of needing technical integrity and flexibility.
00:24:26.559 --> 00:24:32.480
People were either really technical and really good but narrow, or really broad like a spreadsheet, but not depthful.
00:24:32.720 --> 00:24:39.519
So, you know, we we kind of knew with eyes open why we you had to make these trade-offs, but we wanted both.
00:24:41.519 --> 00:24:56.880
Taking some notes while we're talking, real quick, I'm so happy too that you identified IRAP at the top of this conversation, too, because I just want to put for some table sticks for anyone listening to this conversation the difference between Shred and tax credits versus IRAP and grants and things like that.
00:24:57.039 --> 00:25:01.680
You apply for IRAP when you have an idea, the government funds you, and you go off to the races.
00:25:02.079 --> 00:25:09.119
You execute that product, you if you dub become a product at the end of the day, you still did the research, so it's nothing lost.
00:25:09.359 --> 00:25:10.880
But that's the fundamental difference.
00:25:11.039 --> 00:25:14.160
Shred is something that you claim after you've already made the investment.
00:25:14.319 --> 00:25:27.359
You also can't double dip on iREP versus Shred, but truly innovative companies like refined data and like the projects that you're doing, they uncover things in the process of spending their IRAP grant that could be shreddable down the line.
00:25:27.519 --> 00:25:31.039
So again, it's a very, it's a virtuous cycle, if that makes sense.
00:25:31.200 --> 00:25:40.160
And so I want to just draw a point on that one for anybody who needs a little more education or would like to learn more about the different kinds of stages of when these programs become valuable.
00:25:40.240 --> 00:25:48.240
Because I think, to your point, perfect example of why you would want to claim IREP at that early stage, really build something else.
00:25:48.319 --> 00:26:03.039
And then when you need to make that performance uh layer actually accelerate, when you need to enhance the product and get the platform to a new phase, that's where you can really build on what you got going when you were working from the IREP pool and then claim the shred after the fact.
00:26:03.200 --> 00:26:13.759
So a little bit with the you could hear you could hear that we've been engaged in both, and yet if you ask me to explain and say it the way that you just did, no, because that's not our business.
00:26:13.839 --> 00:26:14.799
That's not what we do.
00:26:14.960 --> 00:26:23.920
We focus on the innovation, the programs we do rely on Boast to explain to us, to say where we may qualify, where we don't qualify.
00:26:24.160 --> 00:26:33.039
Even as I said, with 30 years of experience, it's just not been the core focus of obviously of what we've been doing, but it's just been an important adjunct.
00:26:33.359 --> 00:26:34.640
So, how did you please come?
00:26:34.960 --> 00:26:36.400
How did you first come across Boast?
00:26:36.720 --> 00:26:43.680
What was maybe the winning characteristics of what we were doing that was in the same ethos of what you guys are trying to accomplish over at Refined Data?
00:26:44.160 --> 00:26:44.799
Yeah.
00:26:45.279 --> 00:26:47.119
I'm gonna, it was a cold outreach.
00:26:47.200 --> 00:26:54.799
I think we got an email from Boast as a cold e-tra re uh re reach out, cold reach out, and that's how I think we met.
00:26:54.960 --> 00:27:00.480
And the timing was good because we had already been engaged in the beginnings of this research.
00:27:00.720 --> 00:27:08.240
We hadn't been actually applying for shred because the old product was mature and we weren't really doing anything particularly innovative with the old product.
00:27:08.400 --> 00:27:10.559
And so the it was just timing.
00:27:10.640 --> 00:27:37.839
The email came up, and I was intrigued by the name Boast AI because uh the key thing that had been a blocker for us in the past with uh shred credits was the amount of work that it takes to aggregate the core information, you know, and just really mundane work that wasn't automated, that took time to compile, that was a drain on the developer's time, complex to administer and grindy.
00:27:38.640 --> 00:27:39.440
Absolutely.
00:27:39.599 --> 00:27:48.240
You just made my sales pitch for me, but honestly, like that is the biggest thing, especially when you have developers working on something that is finance related, or vice versa.
00:27:48.400 --> 00:27:53.599
You have a finance team trying to figure out what your developers are doing, and they're not speaking the same language.
00:27:53.839 --> 00:28:03.519
And the many, many hours of those two very high-value members of your organization to just meet in the middle, it's it's not worth your time.
00:28:03.599 --> 00:28:06.400
And also, it's not gonna make a good shred claim.
00:28:06.480 --> 00:28:08.880
So I won't digress more from there.
00:28:09.039 --> 00:28:16.000
I want you to take the big speech on this one, but I'm so happy that you named that one too, because again, that's I think our biggest thing.
00:28:16.079 --> 00:28:17.359
It's the time saving.
00:28:17.599 --> 00:28:19.920
And also, again, the claim is gonna be accurate.
00:28:20.079 --> 00:28:28.160
You're not gonna have more time drains down the line because you got an audit because your finance guy didn't understand what the developer was saying, or vice versa.
00:28:28.319 --> 00:28:37.359
So everything you're saying, Paul, is true, but I'm gonna reframe it a little bit from our from our side of the fence, which was this.
00:28:37.440 --> 00:28:40.960
So I get the email, I'm like, okay, we do need to look into this.
00:28:41.039 --> 00:28:43.599
Uh, we don't have currently have somebody that we're working with.
00:28:43.759 --> 00:28:45.200
So I'm gonna take the call.
00:28:45.440 --> 00:28:52.480
But you you, like anybody that's listening to this, you yourself as well, you know what it's like when you're meeting people for the first time.
00:28:52.640 --> 00:28:54.960
What's what was present for me was skepticism.
00:28:55.119 --> 00:28:58.720
I know we need this problem solved, but is this the is this the group we want to go with?
00:28:58.880 --> 00:29:02.880
Because there's no shortage of of people that can help you with your shred claim.
00:29:03.039 --> 00:29:24.319
So, you know, we took the first meeting and the first call, and my questions were all around the automation and all around the degree to which we could relieve the look, there's a there is a burden in doing a shred claim, okay, of course, because you have to present the case for why your work is research and development and why it should even be funded through the tax credit.
00:29:24.480 --> 00:29:25.440
But that's fine.
00:29:25.519 --> 00:29:26.240
I don't mind that.
00:29:26.400 --> 00:29:28.319
That's fair, that's what's required.
00:29:28.480 --> 00:29:39.200
But the thing that that is problematic is that if it's taking a thousand hours to demonstrate a hundred hours of of shred work, that does that equation doesn't work.
00:29:39.359 --> 00:29:47.279
So the degree you can automate the data collection and the evidence for what you're saying, that that that's that was top of mind for me.
00:29:47.359 --> 00:29:50.160
That's the question that needed to be answered for me.
00:29:50.400 --> 00:29:55.200
And when we were in that meeting, I started to get more and more comfortable about the approach.
00:29:55.359 --> 00:29:57.440
So that was one place that I was listening for.
00:29:57.599 --> 00:30:03.039
And then, as I said earlier, we don't treat any of the people we work with as anything other than partners.
00:30:03.119 --> 00:30:06.240
So I was also really interested in who these people are.
00:30:06.400 --> 00:30:07.680
Like, what's the approach?
00:30:07.839 --> 00:30:15.440
You know, everybody says client service, how are they being on the call, which is an intangible, like who's who am I talking to?
00:30:15.839 --> 00:30:19.680
All these questions when we furt when you first meet somebody that are there in the background.
00:30:19.839 --> 00:30:21.519
Uh, and then how are they answering?
00:30:21.599 --> 00:30:23.599
And what questions are they asking as well?
00:30:23.759 --> 00:30:35.599
And you know, I I left the first call with a with a really solid feeling, like this there's a this group of checked boxes on so many uh different levels, most importantly the automation piece.
00:30:36.480 --> 00:30:37.279
I love that.
00:30:37.359 --> 00:30:40.720
I'm gonna pull up on something that you said that I had to draw down to again.
00:30:40.799 --> 00:30:46.720
It if you're spending a thousand hours to validate a hundred hours of work, that's not gonna math out.
00:30:46.880 --> 00:30:48.400
I'm like, the math needs to make sense.
00:30:48.640 --> 00:30:50.880
It's like that's a very easy question.
00:30:53.039 --> 00:30:59.440
Yeah, so again, I'm like, and I think a lot of people don't even bother, honestly, with the RD tax credits.
00:30:59.599 --> 00:31:06.079
This is something I see in the States, is this something we see in Canada as well, just because they think it's just a hill we cannot climb.
00:31:06.160 --> 00:31:09.440
Um, it's just like we're so focused on building the business.
00:31:09.920 --> 00:31:16.720
And so I'd love to know, could you put maybe a number on the time savings per claim that you gain from both?
00:31:17.119 --> 00:31:45.279
I know we said a thousand hours, but uh I would say, look, so you want actual numbers, I would say the uh gain was we probably spent between 15 and 20 percent of the time we normally would have spent on this, and that we spent more time thinking through the quality and the nature of the research and development, so it's not even just the hours that we saved.
00:31:45.440 --> 00:31:56.079
So if it was a thousand hours of work, which I don't think it was, maybe let's say it was 500 hours of work, we were down to I think probably less than a hundred hours of work.
00:31:56.160 --> 00:32:07.359
So that was a big savings, but it also meant that the time we were spending on was the time really to make a strong case for the work we did.
00:32:07.599 --> 00:32:10.400
Look, I like to keep things very simple.
00:32:10.559 --> 00:32:14.960
If we did RD work, I think and it it qualifies, great, let's submit it.
00:32:15.119 --> 00:32:18.240
If what we're doing isn't RD work, don't submit it.
00:32:18.319 --> 00:32:21.359
Like don't waste our time and don't waste the government's time.
00:32:21.599 --> 00:32:37.279
You know, so I take a pretty pragmatic view of that, but we were really able to focus the time in the place that made the most value for us in terms of the breadth of the claim, but also in anybody, I feel like anybody reviewing our claim, there wasn't fluff.
00:32:37.359 --> 00:32:42.160
We could really focus on what was true true RD work, and that was really important too.
00:32:42.480 --> 00:32:44.960
I don't know how you quantify that second piece, by the way.
00:32:45.039 --> 00:32:49.279
The first piece is easy, but that second piece was really, really important.
00:32:50.400 --> 00:32:53.359
Again, I I have a soft spot for my team.
00:32:53.440 --> 00:32:56.079
Um, I'm thinking immediately about Matt Rudershauser.
00:32:56.240 --> 00:33:06.559
I'm not sure if you've met with him directly, but he is one of our solutions consultants, and seeing him hop on a call and just immediately be able to understand exactly the nature of the work that's been done.
00:33:06.720 --> 00:33:19.279
I'm like, these people have talked to so many different providers, and also they've talked to their own internal teams, and they don't know what's going on as well as Matt from my team in five minutes talking to that person.
00:33:19.519 --> 00:33:24.240
So, again, making it personal while also still boosting the business.
00:33:24.400 --> 00:33:27.200
I think that our team is very in tune.
00:33:27.359 --> 00:33:32.319
And I think again, you can hear that time was precious to us, right?
00:33:32.559 --> 00:33:41.680
So we worked with one of your team members and he was terrific because we would submit our first cut at things, and he was very cut and dried.
00:33:42.000 --> 00:33:43.759
This qualifies, this doesn't qualify.
00:33:43.839 --> 00:33:48.079
I need more around this particular issue, and then I would just go around and execute.
00:33:48.160 --> 00:33:55.759
I listen, I was working for boast for that period, all right, in some respects, because this is not anything that I have a deep expertise in.
00:33:55.920 --> 00:34:04.480
I have expertise in the work we do, but not in what's required to present the research quality of the work, what qualifies, what doesn't qualify.
00:34:04.799 --> 00:34:22.639
So the no so it wasn't just the systems, but then as you said, the knowledge of the key project manager that we had the pleasure of working with, who I've raved about, by the way, that was really important because you know it just lent for speed, you know, it just it just lent for speed and focus and value.
00:34:23.280 --> 00:34:25.599
Yeah, it gives you the ability to actually perform.
00:34:25.760 --> 00:34:29.119
So again, this is if let this enough, let's not skip over that.
00:34:29.199 --> 00:34:30.000
It really does.
00:34:30.239 --> 00:34:32.480
It just gives us the ability to perform.
00:34:32.800 --> 00:34:40.880
Yeah, it's like you you know what you need to change or what you need to adjust or what you need to work on, and this just gives you more runway to actually do that.
00:34:41.840 --> 00:34:44.880
I'm gonna stop puffing my own chest here, but all right.
00:34:45.119 --> 00:34:47.440
What did the shred refund actually fund?
00:34:47.760 --> 00:34:49.920
So uh a hue fun question.
00:34:50.079 --> 00:34:58.000
And again, go as deep or as shallow as you want to get on this one, but hiring, the AI reporting work, runway, something else.
00:34:58.159 --> 00:35:01.920
What did take a minute, or do you want to just know what it funded?
00:35:02.320 --> 00:35:11.280
I would love both if you're willing to yeah, our first RD tax credit was$250,000, which was considerable for us.
00:35:11.440 --> 00:35:17.920
We're not a big company, we have eight employees, so you can do the math on our revenues, we are profitable, but we're not a huge company.
00:35:18.079 --> 00:35:28.960
So that made an enormous difference because we were transitioning from an old platform with limited capabilities to this new platform with very wide capabilities.
00:35:29.280 --> 00:35:38.960
And the thing that we struggled with, and we had talked to another company that had had a similar journey in the US, and they said you've got to build use cases.
00:35:39.039 --> 00:35:48.159
So we had to go out and build use cases with clients at very reduced rates because we didn't have it, we didn't have evidence or proof for what we were saying we could do.
00:35:48.400 --> 00:36:04.480
So the money from Shred allowed us to basically fund the business and fund those initiatives until we were able to demonstrate, which with the tool we were able to demonstrate really quickly within weeks, rather than typically a quarter or even a year.
00:36:04.639 --> 00:36:06.400
Within weeks, we were able to make that progress.
00:36:06.559 --> 00:36:08.480
That was the whole point of the platform.
00:36:08.639 --> 00:36:10.480
But the Shred Credit funded that.
00:36:10.559 --> 00:36:24.800
It also funded our ability to further develop the platform, to take the uh speeds down faster and faster until you know we were really hitting the marks and clients were getting this flexibility without any concession on the speed.
00:36:25.039 --> 00:36:26.960
So absolutely critical.
00:36:27.119 --> 00:36:33.840
And then in the second year, the Shred Credit raised to 350, reflecting an increased focus that we had in RD.
00:36:33.920 --> 00:36:36.079
And again, it's just been really critical.
00:36:36.239 --> 00:36:40.880
And we've had a 60% growth in revenue this year, and we're not even at the end of the year.
00:36:41.199 --> 00:36:47.519
Now, I mentioned that earlier that our client base was very steady, but our growth was slow.
00:36:47.679 --> 00:36:52.639
We were steady tracking 5% growth, you know, 7% growth.
00:36:52.960 --> 00:36:59.920
So this this tax credit plus the new platform has now changed at 20 years of very steady growth.
00:37:00.079 --> 00:37:03.679
We've suddenly had a jump this year, 60% revenue growth.
00:37:03.920 --> 00:37:08.159
That got us onto this whole idea of well, if we can do that, can we 10x the company?
00:37:08.239 --> 00:37:10.079
And what does that journey look like?
00:37:10.559 --> 00:37:19.199
So that's also one of the byproducts of the the having the funds to do all of this is we can now make the investment to really see if we can have the business.
00:37:21.760 --> 00:37:27.039
But that's probably a good analogy, but really expand dramatically, right?
00:37:27.360 --> 00:37:29.360
So that's always my hesitations.
00:37:29.760 --> 00:37:31.519
What the right word I want to use.
00:37:32.000 --> 00:37:33.519
Well, welcome to my life, you.
00:37:34.639 --> 00:37:39.840
No, that is again music to my ears, because again, you had you were steady.
00:37:40.000 --> 00:37:43.440
Um, but again, that 60% figure is unreal.
00:37:43.519 --> 00:37:44.159
Like that is hand.
00:37:44.559 --> 00:37:49.199
Well, it's not it's unprecedented in the history of of our company, this this group of people.
00:37:49.360 --> 00:37:55.920
So you that's all and listen, without the tax refund, honestly, we I'm just gonna be really we couldn't have done it.
00:37:56.000 --> 00:37:58.159
We just couldn't have demonstrated that growth.
00:37:58.320 --> 00:38:09.840
And this is this is where I'd this is where as a Canadian I feel really good because it's our tax dollars collectively, Canadian tax dollars that are getting passed back to into this case to refined data.
00:38:10.000 --> 00:38:16.559
And I'm really proud that we're able to then reflect that back in growth in the organization and growth in in the company.
00:38:16.719 --> 00:38:19.920
So I'm that for me, like I don't know, that's meaningful for me.
00:38:20.000 --> 00:38:21.519
That means a lot to me.
00:38:22.159 --> 00:38:22.719
I love it.
00:38:22.880 --> 00:38:35.039
Again, because it's part of your now strategy and your funding strategy and your growth strategy, and it's not just something that's uh like nice to have or uh jury on Sunday, which I think a lot of people just assume RD tax credits are.
00:38:35.199 --> 00:38:41.599
No, it gives you again, you guys are building something net new and you're gonna explode broadband.
00:38:41.840 --> 00:38:43.920
I don't know if it's uh that's right.
00:38:44.079 --> 00:39:06.559
Yeah, and um, I think again, like it's just uh yes, like this is exactly the kind of use case that I want to make sure that we're highlighting because people will be remiss to something that could transform their business, which like I don't think it's an understatement to say we are on this journey with you, and we're gonna continue to be on this journey with you, and we've helped you guys in a very meaningful way.
00:39:06.719 --> 00:39:22.800
So I know we only have about five minutes left, but I'm gonna hit you with two more questions that I'd love just maybe a concise can you point some one initiative that really got liftoff because you worked with BOST and you got shred funding.
00:39:22.960 --> 00:39:23.760
Yeah, yeah.
00:39:23.920 --> 00:39:28.639
So we've been able to explore a lot of different avenues because the platform is so flexible.
00:39:28.719 --> 00:39:34.719
And now what's happened is we've been pulled into an avenue that we think is is of most interest to clients.
00:39:34.800 --> 00:39:40.239
Because if clients get a choice about where they're going to use you, they're gonna use you primarily where the biggest problems are.
00:39:40.320 --> 00:39:42.800
And we're seeing that that is in finance, right?
00:39:43.039 --> 00:39:55.519
Now, the data, as I said, was fast, but in finance, we're now pulling in massive amounts of numbers, but we're also pulling in a very high degree of context as well.
00:39:55.679 --> 00:40:04.000
So it's like every major number now has an analysis or has a why that number is the way that it is.
00:40:04.320 --> 00:40:13.280
And then month over month, we're giving our clients the ability to track how that both that number is changing and that why is changing.
00:40:13.440 --> 00:40:17.440
And that is now starting to get it into huge data sets.
00:40:17.760 --> 00:40:32.000
So again, we've got the performance, but because everybody's managing those reports and those numbers differently, you know, they might all be using up the YADI or MRI, but that's really where the story begins, not where it ends.
00:40:32.159 --> 00:40:38.400
Uh, about how they analyze, report, and create strategy around those numbers.
00:40:38.559 --> 00:40:51.280
So in supporting that, we, you know, and also in expanding our offering and now looking into the US as well, where typically the firm sizes are anywhere five to ten times larger, even though we're with some Canada's largest here.
00:40:52.079 --> 00:41:00.400
You know, we're preparing ourselves then for scale, not just scale in terms of revenue, but scale in terms of use and application of the platform as well.
00:41:00.559 --> 00:41:04.079
And so that's been a prime technical focus and business focus.
00:41:04.639 --> 00:41:16.000
I love that you close that out on the scale picture of it all, because everything that you were saying, too, from the risk to finance kind of pit it that the uh platform is taken and that your focus is taken too.
00:41:16.400 --> 00:41:21.440
Month over month, being able to say how and why things changed.
00:41:21.760 --> 00:41:25.519
This is all so actionable again.
00:41:25.760 --> 00:41:32.480
And it's context, and it's things that again, like, yeah, the sky seems like the limit.
00:41:32.559 --> 00:41:38.960
And I'm sorry, but I'm like, I actually do not see any barriers to this 10x that we've been talking about for a little bit.
00:41:39.599 --> 00:41:42.400
Expect to see one, but I can see one sitting right in front of you.
00:41:43.039 --> 00:41:45.280
You said the person sitting right in front of you.
00:41:45.440 --> 00:41:47.920
Look, we're getting a lot of coaching and support and training.
00:41:48.000 --> 00:42:03.119
We're entering the program that's really focused around this because there's a great, and I heard this recently from the program we're getting into, which I just love because it's it seems to so resonate for me, is that if you look, the bottleneck is always at the top of the bottle.
00:42:03.519 --> 00:42:17.119
And uh, and so you know, it I've got to alter how I think about things, and I feel very much like one of those trapeze artists, you know, that's let go of one bar, which is kind of everything I've known to create a business from nothing to you know, two, three, four million dollars of business.
00:42:17.199 --> 00:42:18.880
Now, how are we gonna have that scale?
00:42:19.039 --> 00:42:30.079
And I'm spinning around in the air right now, and I'm counting on the program we're in and being rigorous with myself to to grab that bar on the other side, but we are we're not there right now.
00:42:30.159 --> 00:42:32.960
We're still spinning, and I think it's but I think it's healthy.
00:42:33.840 --> 00:42:35.280
It's incredibly healthy.
00:42:35.519 --> 00:42:45.039
And yeah, Hugh, again, so many title options from everything that you just said there for you know what this podcast episode would be and what the webinar would be.
00:42:45.119 --> 00:42:47.119
But again, bottleneck is always at the top.
00:42:47.280 --> 00:42:57.760
I think that's a big point that I want to make sure again gets repeated because it's ever been phrased that way, even though it should really be inherent in the entire analogy.
00:42:58.000 --> 00:42:58.880
That is the case.
00:42:59.119 --> 00:43:03.760
But I think that a lot of people just think, like, oh no, I'm just hitting a roadblock.
00:43:03.840 --> 00:43:08.480
But no, it there's a lot that is unlocked when you get there, so don't give up, chip away at it.
00:43:08.719 --> 00:43:12.639
And then also, again, the trapeze artist of it all.
00:43:12.800 --> 00:43:19.599
That is some injury that I can totally relate with, and also really visualized and made peanut butter and jelly what you guys are doing.
00:43:19.760 --> 00:43:22.159
Because yeah, again, you're taking risk.
00:43:22.639 --> 00:43:27.920
But but you do see where you want to land, and uh it's not without foresight.
00:43:28.000 --> 00:43:29.760
So, Hugh, this has been fantastic.
00:43:29.920 --> 00:43:32.159
I cannot thank you enough for joining us on the show.
