Alex Yaseen: 0:03

And that ability to own something yourself, tinker on it regularly and involve a lot of other people on your team and not just do this kind of like off to the side secretly in a spreadsheet is what ultimately enables people to automate a whole class of use cases, a whole class of processes that they've previously resigned themselves to just being totally manual

Tom Raftery: 0:21

Good morning, good afternoon, or good evening, wherever you are in the world. This is the Digital Supply Chain podcast, the number one podcast focusing on the digitization of supply chain, and I'm your host, Tom Raftery. Hi everyone and welcome to episode 337 of the Digital Supply Chain Podcast. My name is Tom Raftery and I'm excited to be here with you today, sharing the latest insights and trends in supply chain. Before we kick off today's show, I want to take a moment to express my gratitude to all of this podcast's amazing supporters. Your support has been instrumental in keeping the podcast going and I'm really grateful for each and every one of you. If you're not already a supporter, I'd like to encourage you to consider joining our community of like minded individuals who are passionate about supply chain. Supporting the podcast is easy and affordable, with options starting as low as just three euros or dollars a month. That's less than the cost of a cup of coffee, and your support will make a huge difference in keeping this show going strong. To become a supporter, simply click on the support link in the show notes of this or any episode, or visit tinyurl. com slash dscpod. Now, without further ado, I'd like to introduce my special guest today, Alex. Alex, welcome to the podcast. Would you like to introduce yourself?

Alex Yaseen: 1:39

Thanks. Great to be here. Yeah, Alex, CEO at Parabola, where we are a collaborative data tool, helping operations people across a ton of awesome supply chain freight logistics companies do a lot of automation of some of their most repetitive manual tasks and kind of move their whole companies forward along the way.

Tom Raftery: 1:59

Okay. How and why?

Alex Yaseen: 2:03

How? Well, we, we believe that, you know, this is a really interesting time in the world in general with a lot of new technology being available to not just the most technically enabled companies, but really to everybody for the first time. And then supply chain world specifically with the huge amount of operational complexity that exists, there's a whole lot of process and work that's been happening really manually. Past few years, companies have been throwing bodies at problems, humans at problems to solve a lot of these types of issues. And that's a difficult proposition in general. People, when, when doing things really manually, work ends up getting really siloed. It's really costly. People don't usually like to be the person who's manually holding things together through sheer force of will. But in 2023, it's also like really not tenable. Companies can't keep doing that. The economy is not set up in the way to just throw bodies at problems. And so all companies are starting to really like try to do more with less. Like trying to ask their existing teams to support more customers and grow revenue and do all these amazing things. But you know, you can't just work 20 more hours per day. And so we are trying to help all of those awesome behind the scenes operators start to actually benefit from working with technology for real and not just being people who manually work on a computer. But people who can build things that scale kind of much more like how an engineer does or literally like how an engineer does and that's because we believe that these behind the scenes operators are just as creative and curious and analytical and capable as software engineers or data scientists on data teams at these companies, and we're here to equip them with with the tools they need to express all that creativity and curiosity.

Tom Raftery: 3:50

Okay, before we get into the how, let's just take a step back and tell me a little bit, a little bit about the genesis of the company. How did it start off, you know what was the kind of flash of insight or whatever it was that led to the setup of Parabola?

Alex Yaseen: 4:10

For sure. To go to go way back for a second, I feel like I have engaged with technology through most of my life with a bit of a lens of cognitive dissonance, where I've always, you know, I learned how to code when I was a kid, I built computers, I did lots of technical things. And so I always experienced technology through a lens of feeling empowered and productive and really enjoying it. But I kind of always had a foot in both worlds, where most of my friends and most of my family, like, really weren't that way, and so I spent a lot of my time trying to encourage them to use technology more, or explaining why it wasn't scary, or when your computer takes you to a place, or your browser's acting up. It's not fighting against you, it's just, you know, it's not receiving instruction correctly.

Tom Raftery: 4:51

Sounds very familiar. Were you asked to fix printers a lot?

Alex Yaseen: 4:54

Yeah, I nonstop debugging and jumbling. I kind of really like it actually. It's fun. I find it really fun to take something that feels kind of like not deterministic. So like a printer is an example for people where they feel like, Oh man, I have no idea why one day it works and the other day it doesn't. And try to fit it into a set of rules and say, actually, no, like here's the thing, here's the input you're doing that is causing it to work this way. And here's how you can make it way more predictable and like the thing you can rely on in your life. This cognitive dissonance and that feeling of trying to make things deterministic is what led to Parabola. So I ended up working in the strategy consulting world for a little bit out of college. And I had a really sharp version of this cognitive dissonance where I was working with people who had 20 or 30 years of super deep subject matter expertise. They were in really interesting CPG or retail or you know, logistics businesses, some of the largest in the world that I was working with they were really close to problems. They were total experts on their specific domain, and they were come up with these really creative processes. They were usually documented in these really long standard operating procedures for like how to hold a process together and how to solve one of these gaps within the business. But they were doing it manually or their teams were doing it manually, so they could only support, you know, a tiny little bit of process. And I think the end result of having done that is like their impact was really blunted. They could only do so much. And so they were hiring consultants or relying on internal engineering teams, hopefully to extend their impact. But I started, you know, getting to know these people. It was like, wow, like they would be so much better served if they could just do more themselves and they could iterate on everything in their own heads and take all of the like ideas of the thousand other things they wished they could be doing if they only had the time and actually do them. So Parabola comes out of kind of that, that let's figure out how to actually empower those people not be scared of technology, but to wrangle and control technology the way a software engineer does. And along the way, because there are so many of those awesome ideas that they have, they're so familiar with all their process, they can actually drive, you know, their whole company forward, they can help that company to support more revenue. I think probably most exciting for us and what has really panned out is as an individual, somebody who's adopted Parabola, brought it into their company, helped build and deploy process. They end up having a really steep career inflection themselves. They've driven a huge amount of impact in their company, far more than before. And they kind of change their relationship with work a little bit, and start to feel much more the way a software engineer does, of being able to have huge output per person, as opposed to just being stuck in this constant, like, treading water, being stuck in quicksand feeling that a lot of operators tell us they feel prior to using Parabola.

Tom Raftery: 7:33

Okay, so from a practical perspective, how do you do that?

Alex Yaseen: 7:39

So practically parabola is a drag and drop tool. You go to our website, you can sign up and check it out where you pull in steps that describe a data or spreadsheet related process. So you can pull in data from pretty much anywhere it lives. You know, really simple if you're pulling in from you know, a database like Snowflake or a third party tool if you're interfacing with e commerce companies, there's probably some Shopify data somewhere along the way. Any kind of tools like that. We also let you do really interesting generic connectors. So you can email us a CSV file or a PDF and we'll pick it up and we'll make it parsable and usable in your flow. You can then describe all the transformation you would do on that data. So, you probably have ten different data sets, like a few CSV files, an internal database you have a transportation management system, maybe a client Shopify store. You want to merge all of that data into one. So you have to clean it up and standardize the column names, you delete some columns, you probably do a few find and replaces to get rid of some prefixes and then you need to join that data together. So in Excel, that would be a VLOOKUP, which everybody hates, if you're using... a more technical tool is, you know, engineering, maybe you're doing like a SQL join or something. Parabola, this is all just drag and drop and you say, hey, I want to just combine this data and the there's a SKU in each, or a container ID or something in each of these data sets. So just use that as the common thing, it'll automatically join all the data together. And then at the end, you can describe where you want your data to be sent. So you can build a report that's viewable in Parabola that you can share with everybody on your team. And they can see you know, all of the places where the data came from and the end report. Or you could build more of an automation where you're sending it off into an existing internal system, or automatically emailing your customers, or making a Slack notification for your team. Kind of whatever the manual version of thing that your per your people are doing, there should be a related parabola step that can that can do it automated. So that's like the simple way that we automate process. But there's also a... You get added layer there where a lot of the teams we work with and a lot of the things we help them automate are things that today they're stuck doing manually. And one of the things we like to tell them is like, that's, it's not because you know, it's not because they're dumb. They're, they're really smart. They're, they just have tried and failed to automate for very structural reasons. Usually a process is really bespoke for a company, so they can't just go buy software from, from, you know, off the shelf. It's really complex and logical, so simpler tools won't work, and it's really painful to do manually. But most importantly, the data tends to be really dynamic. So you get a PDF, you know, a photo of a PDF maybe, like, coming in with, like, handwriting on it. And it changes every time you get it, and you can't write code that really predictably unpacks that piece of data. Or you're a team that's being a little bit experimental, trying to launch a new business line, and so you're, you're testing out something new. And in any of those cases you, an engineer, even if you had one on your team, can't write code that adapts that quickly. And our whole philosophy here is the end user, the operator, the non technical person needs to be able to not just build that automation themselves, but tinker on it on an ongoing basis. And our best users are daily in Parabola, tinkering, moving things around, kind of like owning the machine that they've built. And then collaborating with the rest of their company on both how it works and what the outputs of it are. And that ability to own something yourself, tinker on it regularly and involve a lot of other people on your team and not just do this kind of like off to the side secretly in a spreadsheet is what ultimately enables people to automate a whole class of use cases, a whole class of processes that they've previously resigned themselves to just being totally manual.

Tom Raftery: 11:11

Okay. And can you give me a couple of practical examples of workflows that can be quite easily automated using Parabola?

Alex Yaseen: 11:54

Yeah. So, supply chain world is, is kind of rife with just really manual multi step process. And so, when we get into talking with you know, supply chain leader at you know, an e-commerce business or at an operations leader behind the scenes at, you know, one of our freighter logistics companies we work with they usually have kind of like a ton of these, wow, like, I have teams that I've set up, you know, 10 or 20 people who all handle this type of document that we ingest or generating this report of all of our in transit inventory and how it's moving across warehouses. There's like a ton of different things that they'll have. We'd like to try to focus ourselves and to them initially on something that is going to be really, really high value for the company and going to be a key source of like complexity in their business, that if we can remove, they can start to either do a whole lot more of it so they can bring in a lot more customers, add revenue much more quickly, and actually see some business model impacts. Or they can free themselves up to go focus on something else or, or, or maybe both and that helps them realize not, not just that it's cool that you can automate these things, but that's really impactful to their, to their business and it can actually help them pursue real like business model innovation even if they're a company that's been around for a hundred years. So, for my example, perspective of those meteor use cases maybe two, two initially come to mind. One probably more in like, you know, freight forwarding world. Flexport is one of our large customers. We work with a few other freight forwarders. There's tons of process around handling customs, duties and other pieces of moving goods around the world. I mean, so an example would be if you're moving you know, a good from China to the U. S. It passes through Germany temporarily. You might pay a customs duty in Germany, but if it didn't get sold there, you can request a drawback on it in the future. And so tracking all of the internal data that you, the freight forwarder has on where all the containers are moving. So you have a list in your database and maybe multiple databases of all the containers, all the places they moved, and then customs duties your clients have paid. And then there's a bunch of government databases that are also tracking you know, government's version of the same piece of information. And you kind of have to line those things up in order to create some evidence for, hey, I want to request a refund on, on this container. Super manual process because all of those data sets look different. They change how they report on things pretty regularly. And there's just tons of inconsistencies all the time in, in how like a container ID is, is reported. So could be a 300 or 400 step manual process where a team of people are taking kind of like work orders off of a queue and saying Hey let's like trying to find the evidence for this one container. Data clean up every single time just for each container. In a spreadsheet and I'm doing this in Excel and if I mess up, nobody will actually ever know because it was like impermanent. I did it in Excel. I just emailed somebody and said, Hey, here's the refund to request. And then I lost all of the work that I did and it kind of disappeared into the ether. Was it when I went and, you know, deleted the spreadsheet and started with a new one. In Parabola, you can build up that whole 300 step flow in this flow chart that I'm describing in our tool. Every step along the way it's calculating data, so you can see what the data looks like each step along the way, not just at the beginning and the end, so you can introspect into exactly how it's doing it. And then you can leave a lot of comments and documentation about like, at each step of the way, here's exactly why I'm doing this, here's why I chose to... join based on these two columns rather than this third column, because I know that this third column is very like unreliable. And then at the end can do a validation and say 95% of the containers now that like flow through this flow I've made work totally correctly. I can just automatically create those refund requests and then 5% look still look funky. Because the world is complex and I can route those 5% of people back to the human team to just focus on the hardest, most complicated, most needing like a human's attention problems, and then hopefully that team can go back and work to standardize and process the ties the the work for that remaining 5%. And so it helps empower that team to now work on the way more high leverage work. They probably can also start supporting allocating more of their time towards, towards the other aspects of their job that are not so, so much drudgery. The drawback requests happen way faster. And they can actually, as a company now, that freight forwarder can support far more customers because it takes 10 seconds to process a drawback now, rather than 30 minutes of like human time churning and maybe making some mistakes along the way.

Tom Raftery: 16:12

Okay, so the idea is kind of previous to this, in this company, one person could get through maybe 10 claims or documents or whatever it is a day, whereas now they could get through 100 or 200 or more, depending

Alex Yaseen: 16:28

At least. And sometimes we see like larger impacts than that. And I think that it's more than just they do more of the same type of work. They start to be able to be more creative so they can focus their time on how do we do this process better. And even once it's automated, there's still improvements to make to the consistency of what that automation does or the you could tweak tweaks. You can get a larger percentage of drawback like requested. Maybe you're actually getting more money back to your customers or kind of making them happier. You can focus on the quality of what you do, as opposed to just every day trying to keep up with the manual work.

Tom Raftery: 17:03

And what if this operator makes an error along the way, they now are doing hundreds more errors because it's automated rather than one single one in a day.

Alex Yaseen: 17:17

Yeah, I think when we first start working with companies and I think especially when we bring a technical team into a CTO, that company maybe gets involved and wants to see what their team is up to. That's, that's frequently an initial concern. And the reality is that that does happen. But it also is happening when you're in manual land and people, people make manual mistakes all the time. And as I was saying, like, the problem is they're just, they're unknown mistakes. You're not tracking all the work you're doing. There's usually no trail, like audit trail of all the stuff you've done manually. And so it's just as many if not more mistakes, but they're totally hidden, which is really scary. As any software engineer knows, bugs get introduced into code all the time. You can make mistakes all the time. You know, huge. The biggest tech companies in the world, you know, have have bugs and outages occasionally, but the important thing is being able to track when they're happening, know the second they happen, know the exact impact, and know how to fix it. And a lot of the craft of software engineering is about instrumenting your work to make sure that if there ever is an issue, you can catch it immediately, fix it immediately before there's any big impact. And ultimately, you probably end up cultivating more trust with your customers as a result of having caught it quickly and fixed it quickly. And so, we try to do the same thing, where we live in a complicated, messy world. There's no chance everything's gonna be perfect. But we give people the tools to build things, hopefully the right way, so it minimizes how many of those issues there are. The flowchart you make is very deterministic, to use that word I used earlier on, where every time I have an input, it always has the same output and so the types of manual work issues don't get introduced. And then we help you instrument with a lot of, like, validation checks or other things to make sure that the data you're outputting is safe and so that we constrain the potential issues of when things go wrong, mostly to, Oh, like maybe the flow will fail and we'll send you an email notification saying, Hey, something's going wrong. The data changed more than normal. Take a look before we do anything. And the only real impact at that point is somebody had to get involved and like double check something or fix it. And no, like real world dollars impact ended up happening. And so it's kind of focusing and constraining the places where things can go wrong to the least impactful, most like out in the open places.

Tom Raftery: 19:20

Okay, cool, cool. You mentioned Flexport. Do you have any other kind of success stories you can speak to and wins and, you know, metrics you can, you can talk about?

Alex Yaseen: 19:33

Yeah. Well, so let's see. So within, within Flexport you know, they're using us across a lot of their teams. There are really impressive, you know, tech enabled freight forwarder that's taken a much kind of like a modern approach to freight forwarding where they're deploying bits of technology all throughout all these most manual pieces. They're, you know, software engineering teams can write a lot of code for those types of problems, but there's this longer tail of more variable data more experimental teams who want to try modularizing some of the work they're doing and things like that. Where Parabola becomes really interesting for them to kind of like supplement a lot of the stuff their engineering teams are doing. So they've gotten some pretty big wins along the way. We also work with far like less traditionally tech enabled businesses. So a company we really like working with is a trucking company, family owned trucking business called NFI. They do a variety of, you know, freight logistics work. They have a few different business lines. And what's cool about them is they're a hundred year old or almost a hundred year old family owned business. Obviously when they started out, there was very minimal technology deployed to manage a lot of their trucking. But with things like Parabola and a few of the other kind of you know, data science efforts they're pursuing able to really leapfrog a lot of the past 20, 30 years of, you know, work and catch up quite quickly to the Flexports of the world and be able to actually compete with a similar margin, similar ability to have, you know, customers and revenue supported per person. And it doesn't actually take as much time to catch up now as it did a few years ago, because there are tools hopefully like Parabola. And there's some really cool new AI capabilities both in the world and also that we at Parabola help, help companies deploy. That solve for a lot of the messiest, most complicated technological work that maybe a Flexport seven years ago or something had to solve that somebody now can kind of just like skip and jump straight to the, straight to the good parts at the end. We also work with a lot of e commerce, retail, CPG brands like that. One, one particularly large one we work with is a big holding company called Reckitt. They own a lot of consumer products brands Durex and you know, some similar like household, household names. And they, you know, they use us across their supply chain for doing some reporting into you know, what, what do we actually have in various warehouses and what, what's on its way. And there's a lot of just keeping track of things that happens really manually in spreadsheets. It's really nice to be able to automate those processes and generate, you know, your tracking list or your forecasted you know, levels of inventory and depletion time so that you can do demand planning and all those things just being automatically up to date, not having to have people constantly scrambling to try to update these, these trackers. And then it'll extend out into some of their marketing and other teams as well, which get interesting where there's, there can be an increase in collaboration between, you know, supply chain and more traditionally behind the scenes teams and more customer facing teams like, like marketing who are saying, Oh, well, if I know that we have this inventory and it's actually arriving a few days early, maybe we want to do some X, some early promo on it, or if we're a direct to consumer brand, maybe we want to actually open up an early access to our best customers and give them a few days heads, you know, heads up that there's a cool new like drop coming. And there's, there's a lot of ways that you can start to facilitate really cool business practices via like collaborating across teams that all starts with having like really good data and really clear, consistent communication on like what the state of the world currently is.

Tom Raftery: 22:56

Nice, nice. And for the operators who are working with it, what's the learning curve like there?

Alex Yaseen: 23:02

So usually, I think this is actually a unique perspective that we have. Those operators are quite sophisticated, they have to do this thing manually. And they usually know why they're doing it manually. And they know, not just how to follow the standard operating procedure that somebody gave them when they joined but they know all of the 15 edge cases that frequently come up that aren't even documented in that standard operating procedure and they have this tribal knowledge that they pass on to other people on their team. And so they're usually the best source for how these things work. And we've spent a lot of time building Parabola to essentially the right abstraction level. Meaning those steps that you drag onto your screen are written in plain English, and they do a thing that you would do manually in a spreadsheet. So it's literally as simple as, you pull one step in, all it does is add or remove columns, and another one renames those columns. And a, and a third one will do, like, the find and replace, because you know that there's always, like, a weird underscore you know, that you need to just delete, because that messes up your data. And each of those things that you would know to do on a spreadsheet, you can just have a parabola step that does. And if for some reason you need to get into something more complex or you're not totally sure the best way to do it we have this fantastic team both of support people available right through you can chat in directly through our app or you can you know, get on a call with someone on our team who can walk through and kind of spend 30 minutes just co building a flow with you and can usually build an entire use case with you and half an hour, or an hour. And I think the coolest piece at the moment is some of these new large language models. So, you know, GPT 4 is the one that obviously everybody talks about right now. Sure. They make some previously really complicated things actually easy if you just know how to ask. And so one of the things that would be most complicated for one of our operators to think about normally would be, hey, I got like a PDF and it's got handwriting on it, and I need to somehow turn that into data that looks like a spreadsheet so that I can build a process around it. That's a thing that would normally be difficult to even like wrap your head around because you would in non, you know, in manual world, you'd have to have somebody who sits there and tries to read the handwriting and like transcribes it into a spreadsheet in a standardized template. But we have a Parabola Step that does something almost exactly that, where if you send that PDF to us, we'll scrape all of the data out of it using like an optical character recognition algorithm. So you actually get text that you can like copy and paste even if it was handwriting before. And then we'll grab if there's like three different tables of maybe it's an invoice that has some metadata and some line items and a few other things on it. We'll grab all those different tables of data and give those to you as well. And then there's still probably an issue that every time that company sends you a PDF, it might be a little bit different because it's a different one of their warehouses that sent it to you, or something happens where the template's a little bit different and there's like two columns that are named something differently. And so you can use a large language model to standardize that data. And you can say, I always want these five columns in my spreadsheet that say that are, that are like called the same thing. And it's a really standardized piece of data, even if it's pulling it from a totally different physical location on that PDF. And the large language models know how to do that incredibly well. And we've built tooling around it so that as an operator in Parabola, you don't even have to know that you're using a large language model. You just pull in our step that says extract data with AI and you connect it to the PDF, and it just extracts consistent data doing all of those technical things behind the scenes, and we've handled all that complexity for you.

Tom Raftery: 26:28

Nice. Nice. And what about the issue of operators fearing change and thinking you're going to come in and take their jobs and all that kind of change management issues, essentially?

Alex Yaseen: 26:43

Yeah, that's, that's real. I think the reality is, especially in 2023, that's happening no matter what. And people kind of have to come to terms with that. And in many cases have. A lot of these new AI models are kind of changing everything. A lot of types of jobs are going to have to be different. And regardless of AI or not, just the most tech enabled companies the Flexports of the world in this case are increasingly running away with all the benefits. And if you don't pursue digital, you know, transformation yourself, you'll be left behind. And that's just kind of the either fortunate or unfortunate reality depending on your perspective on the world of 2023. And so our take is just, we want to equip you to be part of that future that's happening no matter what. And we'll do our best to make sure that you're a really powerful, like, really active participant in that future. And if you're an operator who's, you know, worried about your job going away, what we're actually helping you do is guarantee that that won't happen. Because you are the person who is building and tinkering on one of these processes. They're too dynamic to fully automate a way where you could just like leave them hands off. Like you still need somebody who's an expert to be building the things, maintaining them, building more things. And what we actually see is our users who most adopt Parabola, not only are not getting, you know, laid off, they're usually getting promoted and they're becoming kind of more highly leveraged, more senior, more like valued people within their company. Because they've built 50 flows that all automate at really important tasks and support important tasks. And there's there, you know, when, when previously manually, they can only support two. So they've literally, you know, double, sorry, 20X'd their, their like impact that they can have.

Tom Raftery: 28:15

Sure. And where to next for parabola? What do you, what are your plans for the next 5, 10 years?

Alex Yaseen: 28:20

Yeah, so we're, we're constantly, some of the questions you asked, we're constantly working on improving. So, we're constantly working on making Parabola easier to use and more approachable for those end users. Where increasingly they can just say, even if this task is not the most, the biggest, most important, most high ROI task. It's just a thing that's annoying that I do. Should be able to spin it up in 20 minutes, solve it in Parabola, and move on to the next thing. And ideally, Parabola increasingly becomes kind of the line of first defense for, for everybody, both within supply chain world, really, and, and in the broader, in the broader world of knowledge work, where if there's an annoying manual task, like it would be crazy to, to not at least try to use Parabola first to use it. And there's a lot of things we can do to make that easier. There's just making our tool more powerful. But I think the most exciting is some of the collaboration across teams where we can say, Hey, like we've worked with 20 different you were talking about freight forwarders before, maybe 20 different freight forwarders, or maybe 20 different you know, manufacturing teams for CPG brands who are all tracking their supply chain in the same way. We can help them standardize the way they talk about it, share, you know, a snippet of the logic that they use. And then that can become reused across, you know, the hundreds of other companies that all have to solve the same problem and can can start to really get out and and, and help communities of companies and, you know, types of jobs all start to, you know, not have to always reinvent the wheel every time. And software engineers already have those benefits in the world. And like, we think operators really should, should have all those same things.

Tom Raftery: 29:49

So are you thinking of setting up like a community of user groups or a user group community?

Alex Yaseen: 29:53

Yeah, we already have a few of those things happening you know, quietly behind the scenes. So certainly if anybody is, is interested in learning more about how other people in their, you know, domain work should, should reach out to us and we can, we can maybe connect you. And then we'll be making a few of those things more public in the not too distant future as well.

Tom Raftery: 30:09

Okay. Cool. Nice. Nice. We're coming towards the end of the podcast now, Alex. Is there any question I haven't asked that you wish I had or any aspect of this we haven't touched on that you think it's important for people to think about?

Alex Yaseen: 30:23

I think the probably like the, how, where do I get started or like, how do I, how do I actually go from as an operator? How do I go from, I think my work is manual, but also I do it and it's fine, you know, to like, what, what does this like big, bright future look like? And we find that that can be pretty overwhelming for anybody to try to think about because it's, there's so much opportunity, there's so much low hanging fruit, like, where do I just get started? And so we, that's one of the places that we most like to spend time working with companies and say you know, let's walk through a few of your most painful manual processes. We can watch you do them if you want, and we can kind of point out some of the things that are gonna be really good use cases for parabola versus things that aren't. If you're not talking to us, it's usually the places where you have the most complexity in your business. That's the most like unique to your business. So. Not, not saying you can't do it, you know, all of these things, but like, if you start on the place that's the most unique, most complex, and like really help automate that, not only are you going to have a huge impact on your business you free up a lot of your time to focus on a lot of the other areas but you also end up like those are the best cases for Parabola anyway, because they're the things that are the hardest to automate because like, otherwise your business probably would have done them already. Like, yeah. Yeah, they're, they're, they're, they're in manual for a reason. But would encourage anybody who's interested, like we love to talk, we don't charge to talk to us, so we, we usually all just kind of help people think through, Hey, this seems like a really good place to start and here's where you should get started if you want to start on your own. Or if you want us to help you, like we can, we can obviously talk about that as well.

Tom Raftery: 31:58

Cool. Cool. Alex, that's been great. If people would like to know more about yourself or Parabola or any of the things we discussed in the podcast today, where would you have me direct them?

Alex Yaseen: 32:09

Yeah, I think to our website parabola. io. P A R A B O L A dot I O. And you can either sign up to use Parabola for yourself or would really encourage setting up a call with somebody on our team. We have a, an awesome team of just total product experts who come from e commerce logistics, other, other backgrounds, and know data really well and know probably how, you know, five other companies that are similar to you have solved similar problems. And we love to just talk and share information and, you know, don't charge for it, so. Would really encourage people to sign up for you know, to talk with us as well. If that's that's of interest.

Tom Raftery: 32:46

Super, super great. Alex that's been fascinating. Thanks a million for coming in the podcast today.

Alex Yaseen: 32:51

Absolutely. Great to talk.

Tom Raftery: 32:52

Okay, we've come to the end of the show. Thanks everyone for listening. If you'd like to know more about digital supply chains, simply drop me an email to TomRaftery@outlook.com If you like the show, please don't forget to click Follow on it in your podcast application of choice to be sure to get new episodes as soon as they're published Also, please don't forget to rate and review the podcast. It really does help new people to find a show. Thanks, catch you all next time.