Bectran: Order to Cash AI Insights with Ali Kidwai

Podcast Interview Transcription (Cobalt Intelligence & Bectran)

Jordan: Hello and welcome everyone. I'm here with Ali Kidwai from, uh, Bectran. He is the Director of Product and Engineering and Ali. We're like almost at our two year anniversary. Yeah, so it's kind of cool the fact that we've been working together for about two years. That's when we first started talking and now we've been partners. I would say really closely. I have some of my favorite people to work with. Always have great questions, uh, really good feedback. Our product has certainly improved, um, by working with you. Um, really appreciate your time and I appreciate you coming on today, Ali.

Ali: Yeah. No, definitely. Thanks, uh, thanks Mr. Jordan for having me. And like you said, we've been partners for a long time. You're one of our closest partners, uh, you know, uh, so definitely, uh, appreciate, uh, appreciate you, you know, giving us the opportunity to come here and talk, and I'm excited for the conversation today.

Jordan: Yeah, Ali, so just to start us off, gimme a little background on yourself and kind of ease us into Bectran, tell me about Bectran. So let's just start with you, kind of what's your background and, and why are you here?

Ali: Sure. Uh, just give you a bit of background about myself. So I, I was born in India. I, uh, in this place called Lucknow. Uh, I went to high school there, uh, and I came to the US in 2013. I went to Indiana University. Uh, I have a bachelor's in finance and I have a master's in technology. Now I work for a FinTech company, so I guess it makes sense.

Jordan: Works perfect.

Ali: So, uh, so yeah, Bectran was my first job outta college. Uh, so now it's been, be close to eight years. Uh, kind of a funny story. I was, uh, the program that I went to school for, for my master's program was actually built for consulting. So, you know, you go to this program and then you end up in, you know, one of, one of the big four or any sort of consulting house, but I guess, you know, I did all my interviews and I was not chosen for one of the, any, one of the consulting firms, and they kept telling me that I'm not a culture fit for consulting, which makes sense. I I'm much happier here now. Uh, so, uh, so yeah, that, that's sort of how I ended up here. I, uh, you know, we're, we're a much different company then, and you know, the role that I'm in now is not the role that I was hired for.

So I, I, I was hired more on the implementation customer success side, so that would've been more, you know, when a new customer comes on, you know, implementing the solution as well as, uh, working on like the daily, you know, tickets as well as, you know, any question that our customers may have. We're obviously a different company than in terms of technology as well as the integration we offer. We certainly didn't have you or anybody for that matter. Uh, but yeah, over the years it's been, it's been very interesting. I, I, I would say that there's been a few key developments in the company and as well as in my career. So I can sort of run you through that and I can then sort of give a background on the company.

Actually, I'll give a background. The company's make sense. Uh, Bectran is an order to cash platform for B2B credit. So when businesses buy from each other, they buy on credit. We, uh, facilitate that process. Everything starting from the order or actually from the original credit origination all the way up to the cash application.

So I'll take a very simple example so that the audience can understand. Let's say Mr. Jordan has a bar, Jordan's Bar. Jordan's bar to run every day needs whiskey and beer, steak and, and potatoes and salt and pepper and pan, uh, and, you know, uh, or whatever else a bar sells. Yeah. Onion rings and whatnot.

Jordan: Different. I like, I like onion rings at a bar.

Ali: Yes. You know, you need this like every week and you're not gonna go every week to Walmart to buy all this. You're gonna get it from a food distributor. Uh, let's say you get it from one of the largest food distributors in the country, most of whom are our customers. Uh, I'll just take an example of US Foods, right? Uh, you know, you go to US Foods and you say, hey, I need some credit, right? For this stuff. So the difference here is they're not giving you money. They're giving you material and you're getting credit for that material. So you get, get it on terms like net 30, net 30, right? Something like that.

Jordan: Mm-hmm. Yeah. Net 30. I mean, food is different. Food you get much less.

Ali: Much still. Uh, same thing, but they're not gonna look at your pretty face, Mr. Jordan, and be like, oh yeah, let's, let's give you, let's give you some credit.

Jordan: So you don't think I could convince them with this? A loan. Yeah.

Ali: But, uh, but yeah, so the, the way it would work is you'd fill out a credit application, you'd give your references, you'd give, uh, you know, people you do business with. You'd, you'd give like your owners, uh, you know, and all the different things that go with it. Then once that credit application submitted, it would go to somebody in the credit department. They would decision it. Right. The key here though, is, take another example. Let's say Mr. Jordan now has a tire shop, right? The way that Michelin, for example, looks at Jordan's Tire Shop is very different than the way that US Foods looks at Jordan's, uh, bar, completely different. Jordan's Tire Shop... Jordan's Bar for example, would buy maybe $2,000 a week, $3,000 a week. Jordan's tire shop would put in like a half a million dollar order, one order for tires, right? So different levels of security and risk analysis we're talking about. So that's the first piece.

The second piece comes with, let's say then, you know, by some miracle with your pretty face, you get approved, you know, then you need to be invoiced. So you know, you need to send you an invoice so that you can pay it. Then you need a portal to pay it. Right. And let's say that you fall on hard times or you know, you start gambling and you're not paying your bills. Okay. Uh, I'm just taking you the whole lifecycle.

Jordan: Yeah, right. Let's just go down this route. Let's just keep going. Uh,

Ali: Yep. So let's say you stop paying your bills, then you need a system to first remind the customer. But the second piece, which is the most important piece I would say, is any large organization, especially the companies that, um, I mean, majority of the companies that we deal with in, in different industries. So we deal, we deal with every industry you can possibly imagine. We deal in food, media, materials, construction. Obviously there are some that we are very good at, some that, you know, we're obviously need more presence in. But, you know, we deal with just about anything. We have a customer that all they sell is helium for balloons, so, uh, you know, just about, just about anything.

But the biggest thing is they don't have one customer. They have thousands of customers. Like when they wake up in the morning, they have to figure out who is good and who is bad, and who's gonna pay them and who's not gonna pay them, and which customers are good and they look like they're good. But there are, most of the times, there are cases where you have customers that look like they're good, but they're not good. They have customers that look like they are bad, but they're not actually bad. For I'll, I'll take a very simple example. You have Jordan's bar that, you know, you invoice them Monday, right? And they, they pay you next, let's say the next Monday they're supposed to pay you, but they always pay you the following Thursday. This person's been paying you on the Thursday for the last 30 years, right? If he's couple of days past due. He's not really risky, he's just being Jordan. Versus you get a guy and you know, he goes past due the first time. Right now you need to look into, okay, why? So that, you know, we need that kind of nuance.

So that's on the collection side. Then, uh, I talk about payments. You need a way for Jordan's Bar to come in and make payments. You need a portal. Then let's say that we, Jordan's bar bought... uh, what did I say? Some steak. Let's say you ordered some steak, right? And you ordered like 10 pounds of steak. But instead of 10 pounds of steak, I send you five pounds of steak and five pound of apples. Right? Now you need to raise a dispute 'cause you're not gonna pay for the apples. You don't sell apples unless, I don't know what kind of bar this is, but, you know, by mistake, you got apples, right? Or let's say there was a shipping delay and whatnot. So, uh, you need to raise a dispute. So you need a system for like disputes and claims and settlements, right? Just like, like you would return stuff at an Apple store, right? Similar to that.

Uh, then you have the order management. So B2B credit is very different in the sense that they let you go over the credit limit, but within reason. We offer solutions wherein, let's say Jordan's Bar is doing very good. He was given a $10,000 credit limit, but he's doing so good, he keeps running that credit limit, right? So you need a system to assess, okay, is Jordan's bar good? Can I release his orders? And then ultimately, can I automatically increase his limit if his order behavior as well as his payment behavior is good?

And then finally, uh, the cash application side. So, believe it or not, and even in this day and age, Jordan's bar would send a physical check to their supplier, a physical check, right? The problem that happens with that is, uh, let's say Jordan's Bar has five invoices for a thousand dollars each. So $5,000, right? You send a check for 3000, right? And there's a check, a physical check.

Jordan: Yeah.

Ali: You send it to what is called like a lockbox or a bank PO box. Right? They cash the check. The problem that occurs is the supplier, they don't know which invoices to apply it to, but they already took your money. So now we have this problem of like, oh, the money, your money is in our bank account, and your account is showing past due. Figuring all of that out. That's sort of the lifecycle. And there's obviously a lot of things that go with, like, I just gave you like a 30,000 foot overview. There's a lot of things that go there. There are risk analysis where you come in, where you help us to do a lot of the risk analysis. Uh, there's AI modeling and predictions, so like, how can we figure out nuances for the customer, whether it's like numerical risk based, and like what are the customers gonna pay, or it is assigning some value to the customer at a risk value or it's sentiment, right? I'm on the phone with the customer all the time. The customer sends me emails. I can read those emails and figure out if they're gonna pay me on time or not. Does that carry nuance?

Jordan: Hmm.

Ali: So that's, again, there's another thing we won't have enough time to go through, but that's sort of the high level. Uh, so yeah, I, I sort of started on, on that piece. I, I worked there for a few years, then I was sort of moved into product, uh, basically figuring out what to build. A lot of our feedback comes from our implementations, uh, as well as from existing customers. So, you know, sort of we need an, we need an engine to take it in and we need an engine to distill it and we need an engine to sort of, uh, give it to the engineers to do and then make sure that it works with the customer.

So working on that. And then recently in the past, uh, year and a half, I've basically come on the engineering side. So I basically oversee our engineering and infrastructure team. So it's still on the product side, but like focus more on the engineering and product side, so less on the implementation. Work on basically ensuring that our deployments are smooth. So we do deployments every three weeks. I, I make sure all our deployments are smooth. Um, all of our infrastructure is good. Uh, working on a lot of our new AI infrastructure, uh, you know, uh, for internally for ourselves as well as for our customers. And then also working, uh, on the, uh, just general, how to make our engineers' lives easier, how to make them faster, uh, as well as have them spend time on things that they enjoy rather than like the nitty gritty work, like the admin work, right. That sort of, yeah.

So now I'm, I'm, I'm, I'm sort of doing this, which I, I, I would say, you know, it's, it's been a quite an interesting year and a half. I, I've gotten to learn a lot about, uh, engineering and infrastructure and uh, you know, my day is very varied. I, I always take this example of like, my day would start with like, oh, we have a new product we have to build and we have this really hard technical problem that we need to solve. And, you know, it's like some problem about, you know, some scale issue and, you know, race conditions and whatnot. And then I would... remove that. The second half of my day is figuring out, is this button too green or is this button too blue?

Jordan: Let's...

Ali: Right. So that's sort of, that's sort of high level on, uh, on, uh, on that, uh, there's just a bit about the company. So we've been in business about, uh, 15, actually 15 and a half years now. So our leadership, um, our CEO, he, he, he used to do this work. What we sell. So he, he was at, uh, he was in different companies doing it. So, um, um, he spent a lot of time at Dow Chemicals, so he used to do this, and he got fed up and he quit. And he is like, you know, there needs to be a better way to do this. So he started it. So we have like a practitioner, uh, basically that guides our, uh, sort of product development to make sure that, you know, we're, uh, we're catering to the right people.

And then, yeah, like we, we have an office here basically in the Schaumburg area. Uh, so where we basically, like I said, we deal with, uh, you know, product engineering, implementation, infrastructure, DevOps, whatnot. Uh, but yeah, it's been an interesting ride. I've always, I mean, I've been with the company, like I said, almost eight years. It'll be eight years soon. So I've seen a lot of, uh, shifts in technology. We have, certainly as a company, dealt with a lot of shifts in technology. I can definitely talk about that, but that's, that's sort of the high level of, uh, you know, sort of the background, the company and the role. Happy to, happy to, uh, expand on anything that, uh, that you think I should?

Jordan: Yeah. We are going to talk about a few different things. I have some questions, but one of the things that stood out the most as I spoke more with you, with Cory, um, with your team is the 2010 thing. Bectran started in 2010. 15 years is not a lot for a lot of companies, but for someone in FinTech, it's a long time.

Ali: Yeah. Oh yeah.

Jordan: There aren't that many fintechs that have been around for 15 years. And I really want you to take like just a few minutes and talk about what Bectran has done different to allow you to just live really the last 15 years. Yeah. It'd be good to keep innovating. Um, what do you feel like made you stand out?

Ali: Yeah, I, I would say a few things. Uh, number one, always having the focus on the customer problem and forgetting the technology. I, I think, you know, even before my time, and definitely in my time, we have gone through a lot of technological shifts internally. We are the longest, uh, running SaaS, uh, solution in the industry like order to cash. So, you know, we have competitors that are more, that were on-prem and they sort of moved to SaaS, but you know, at least that's been in our favor where, you know, generally just moving through technological shifts, whether that's the underlying infrastructure, the languages that we use, uh, as well as our, our own infrastructure. Uh, that's one.

But I would say the biggest thing is always focusing on the customer problem, working the technology backwards. I know that's a Steve Jobs quote on it, but like, my job here is not to implement technology, right? My job here is to solve customer problems and whatever technology we have to use, like I have no problem using it. That's number one.

Number two would be always leading with a mission, uh, and leading with context for our internal employees because I've seen that like, especially since I've taken like charge of engineering, like people will work much harder and much better for you if you give them context on how they can solve customer problems. So I, I actually make it a point that all of our engineers, like, at least when we go to our customer sites, that we have like engineers go with us because like imagine I go, I come back, I distill it, and then it gets distilled and mm-hmm. Goes to the engineer rather than like, you know, I don't have to spend that much time. Always leading with context.

Never screw the customer ever, ever.

Jordan: No.

Ali: I mean, you laugh.

Jordan: Yeah.

Ali: But like we have, like, we have seen that, and I, that's one of the reasons we do business with you. I mean, in essence, we are your customer, Mr. Jordan.

Jordan: Yeah.

Ali: Like never screw the customer because you know, at the start of the company and our CEO tells us that, and we didn't have much marketing. I mean, now we have very, you know, established and well run marketing department and you know, we have all the things that go with it. But like before it was like all word of mouth, right? And we are able to have a lot of success because like our customers would tell us like, look, even if you tell us no, but for the things that you tell us yes for, you do a hundred percent. Like there's no doubt about it.

So our CEO, he always says that. We don't wanna be in the business of like making money. We want to be in the business of like solving customer problems. Money, it all automatically comes after that. There's no doubt about like those, that's just how the market works.

I, I would say these three things in the past year, I, I would say it's definitely from an engineering and product side, I, I would say a few different things. The one is having the right tools in place to get people access to information is very important. Um, I'll actually talk about that in a bit here on how we've actually been able to consolidate all customer feedback now and present it to everybody in the company. Executives down to like, like somebody doing an implementation.

And two is always building for scale. Like, so we deal with, as you would imagine, we deal with a lot of data every day, like data in, data out, customers loading their own data. Like I said, right? Like a large company would have millions of Jordan's Bars, right? So like dealing with that data. Data coming in, and then data going out. The system itself creates a lot of data, right? Credit decisions, AR decisions, order releases, uh, you know, disputes and settlements decisions. That data needs to go out. Always building for scale. So anytime like we have a feature that comes in, I'm like, they're like, oh, there's not gonna be a lot of data here. I'm like, no, no. This needs to be built for a hundred billion rows. Like, that's like the start of anything.

So I, I would say those things, um, those are some important tenants. And obviously on the people side, I would say let them figure out the problems themselves. 'Cause I've realized that sometimes I know the answer, but I don't tell them and I let them figure it out and they come back with a better answer. So I kind of enjoy that. I play dumb, like, oh, I don't know. Uh, you... so those are some of the things I would say, but I always just like sort of in every, every different area. But like that's sort of some of the, some of the things that I would say. And I would say obviously always keep moving forward. I mean, that's like, that's like our motto, but like always keep moving forward. You know, you're not gonna win all the time. You know, every product that you make is not gonna be a hit. You know, I had to sunset a lot of products like, just 'cause they didn't work how we thought. Uh, you know, sometimes you think, okay, you put something with the customer, it's gonna be a great success and the customer even thinks so and ultimately goes to production and it doesn't work out. So, you know, always keep moving forward. There's all, there is always like, you know, it's kind of like basketball. It's just, you gotta keep taking your shots, man.

Jordan: So yeah. We talked a little bit about technology. You mentioned it briefly. Technology certainly changed a lot, especially with AI. So I'm kind of curious, you know, take on AI right now, how it's impacting Bectran. Where do you see it going? Tell me more about your, your approach to technology and...

Ali: So two, uh, ways we can sort of structure that conversation. One is how we use it internally, and then one, how we use it for our customers. I, I sort of think of it from two angles. One, for, for our customers, uh, you know, 'cause we are in FinTech, we have to follow a lot of regulation. I cannot go as crazy with it as somebody like, you know, Spotify could. Although I wish I could, but, uh, you know, I, I couldn't. But we do have, obviously, you know, especially in the last year or so, I've delivered a lot of solutions for our customers. I, I'll run you through a few so you can sort of get an idea of it.

Couple of things that we have done for our customers is we start with, okay, what are some of the manual nuance work that the customer is doing? Like, for example, we have documents that are submitted from a customer, right? Like let's say Jordan's Bar wants some credit, right? And let's say you submit a tax certificate that instead of Jordan's Bar it says "The Lease Bar." Right? Now somebody has to look at their tax certificate and tell you, right? Versus let's say that just generally on the document validation side, but on the fraud side, right? Like let's say that your domain is cobaltintelligence.com, right? And then somebody creates a fake domain to impersonate you and they write cobaltsintelligence@orwhatever.com, right? Small things using AI to catch that is very effective. But again, it's a recommendation so that we can surface these closer, like easier to the customer. That's one.

Two, I, I spoke about briefly is sentiment analysis for payment. So being able to figure out, okay, I've been, I talked to this customer, we pull an email and whatnot from the customer. So, you know, figure out, okay, is this customer talking to me good, talking to me bad, has he historically talked to me well, and every time he pays me late, he talks to me like this, so therefore he will talk, he will pay me late this time versus every time that he pays me on time, he talks to me... it's some, some stuff, it's nuanced.

Uh, that's one. Um, it's some easier things, uh, you know, you use more machine learning for, but like predicting when, like, the day that they will pay or the day that they will pay. So it helps like in collection efforts. For example, if a customer, like I was taking an example, a customer pays you like 30 days late, but then when they're paying you 30 days late, like 30 days late, like 30 years. That guy is not a problem.

Jordan: Yeah.

Ali: You don't need to focus your energies on that guy. Right. But let's say that same customer is now deviating to 60 days. That's a problem. Mm-hmm. Uh, there's some problem there. Uh, being able to automate a lot of the write-offs, uh, as well as the things that the user types. So let's say a collector needs to send an email to the customer, "Hey, pay me." Right? Why do you have to write that email all the time? I have all the invoices, I have all the data. All the easier things, you know, write-ups for, let's say I want to increase the limit for Jordan, like, right, like usually what happens in the industry is that you have to do a write-up for your manager. You know, I have all the information. I can give you a temporary review, you can edit it if you like. You know, those are just some examples.

There are obviously crazier examples with agents. Uh, so let's say a lot of, uh, accounts receivable, uh, like the, you know, the order to cash side is figuring out trends in data. Like, and different companies have different trends to monitor, right? A construction company would look at different trends than a food service company does, right? But ultimately, it's basically waking up in the morning and an AR manager or collector is like looking at dashboards to figure out like what I should focus on, right? But ultimately all comes down to that insight. Right. You can have a system that you could configure in natural language to get you these insights every day and just work on that. Because what happens is with that insight, they take action. You could configure the system to give you that insight. 'Cause it's like all the data we have, it's not like data we don't have to give you the insight and connect it to recommended actions and then, you know, uh, makes it easier.

Or, I have learned though, which I will say in our industry, and I think in most industries, even when I use it, I don't want it to do the work for me though. I wanna wake up in the morning and like I should have a way to like review the work and like it should get me to the last step, but I still wanna be the one making the decision. I mean, that holds true in our industry too.

So, again and again, like I said, a lot of the things we pride ourselves on having like better than industry grade security as well as following all rules and regulations because obviously, I mean, obviously we're SOC compliant and we have like some of the largest companies in the world that use our software. So we, we can't be messing around like, you know, I, I don't have the luxury for that, right? They'll take me to jail right away. So, uh, right away. Uh, right away, uh, yeah, a lot of it is like making recommendations. So in that case, what happens is that you still put the onus on the human.

Jordan: Yeah.

Ali: Uh, because, because, you know, this comes as no surprise. It's not gonna be right a hundred percent of the time, but if I can get it right at 95% of the time, and then you can tell the system where it's wrong 5% of the time, and it can learn. Uh, and obviously these use cases that I told you are just some simple ones. There are crazier ones in cash application. Like for example, reading, reading remittances, like, you know, in different languages or reading handwritten remittances, financial statements, reading those. Like we have a customer that does business internationally that gets like different kinds of financial statements, but it's handwritten. Right. Uh, extracting that. I could go on and on. So that's sort of, that's sort of on the external side.

On the internal side, we have a lot more luxury. Uh, so, you know, 'cause we can be wrong and it's fine. Like, you know what will happen. Right. Internally, we have done a lot of great things. Some of it actually, you'll see an article come out in a week or so. I, I'll give you sort of a, sort of a high level overview. So the way that product, like I, I was touching back to like the, the way that product feedback comes is basically I have a team of product analysts and implementation people, right? So they are on the calls and then they're on the calls with the customer. Customer will say, "Hey, I need this, and that, and that." And they'll be like, "Oh, we don't have this. Let me talk to our product team and we'll let you know."

The way that this happens today is it has to flow up the chain. Somebody needs to write it down. Uh, then somebody needs to at least make a small spec for it, then give it to their manager. Then ultimately bubbles up to me. Right? And then we need to have a discussion with the engineering leads and everybody, and then whatnot. And while that process is required, it needs like a product, like feedback must be sanitized and it must go, like, if we're gonna build a feature, we have to go through all the necessary steps. The ingestion of that information today is slower than I would have liked, but it's all information that we have. All the meetings that we have with our customers are recorded, right? Like, 'cause we need, we use that for our own, like, uh, we have to have it so that we can go back and see, okay, this is what the customer says so I can make these changes. Right?

What we've built now is an engine that as soon as a meeting happens, it transcribes it to like a vector database, and then we then connect that to our, uh, JIRA tickets. Uh, basically. So now in essence, I have a dashboard that can tell me in a given week, all the requests that the customers ask for.

Jordan: Yeah.

Ali: All the pending tickets that we have already for it and requests that we don't have tickets for, but hasn't been discussed before, and requests we don't even have tickets for.

Jordan: Oh, that's genius. It's a smart idea. Yeah, yeah. Yeah.

Ali: Yeah. So I'm, uh, and then, I mean, I'm, I'm gonna enhance that, but I, I was actually, we just actually, uh, this morning is when we, you know, now it's live. Uh, so it's kind of good timing. But, but other things that we do, like, for example, we, we have a support ticketing mechanism. So sometimes what will happen is, you know, there are different kinds of issues. Like you have a customer, "Hey, I forgot my password. How do I reset?" Right? That's one. Or there's sometimes there's an issue with the system itself, like it's a bug, right? We have a team here on rotation that does like what we call like production re-engineering, or production support basically. So that work is not like building a feature, right? Building a feature, you know what to do or, or an enhancement or a like, you know what to do. This is like investigational work. Like you need to first try to replicate it, then look at the code, but that like takes up a lot of time, right?

So what we've done in essence is we read the tickets. Then our entire code base is vectorized, reads the ticket, it goes to the code base, it figures out, okay, this is where possibly this issue could occur. So then it pinpoints it, and then again, last mile, the engineer can go and look at, okay, this is where the problem occurs. And again, that one, when we started it, it was horrible. It was like 20% accuracy we've gotten.

Jordan: And again, it's, it's not like perfect right now, but is it getting better now?

Ali: Yeah, it's getting better. Again, like I said, even if it's not great, right, like ultimately the human will come in and he will fix it. Right?

Jordan: Yeah.

Ali: But he or she will fix it, but at least it helps them, right?

Jordan: Yeah.

Ali: Uh, and then there are, there are again, there are, there are other things that we, uh, definitely are looking to automate. A lot of like what we do internally, uh, testing is one. Right? Like, you know, which areas to test even. I, I still, like I explained to you, our product, right? Product is huge. I mean, it's huge. Like mm-hmm. I don't even have, we couldn't, I couldn't even sit here and explain to you, uh, or like when we do a product demo, like we can't even do a product demo in an hour 'cause it's so huge. Right? Like, and we have customers that use, you know, module by module or this, they use the whole suite, but because, because it's so huge, it's hard for people to always figure out like where to look. So just giving them the last step, like the last mile, like just helping them a little bit right, like is helpful.

And I use it obviously all the time for my own stuff. Like, so a lot of my work is like reviewing like product requirements and then also writing up product requirements. But my product requirements are written more at the holistic level, uh, right. Like not in the nitty gritty. So, uh, I use a lot of like writing tools, uh, as well as I, I use a lot of like, uh, again, I can chat with my code base, right? So I look at like, okay, what would be the best way to do it? Right? Or, yeah, or, uh, and then what this has also enabled is I don't have to be an engineer to prototype things.

Jordan: Yeah.

Ali: I can prototype things so quickly now with these tools that it's great. Like before, what would happen is when we were discussing together, right, we would have to sketch something out and then I would give it to an engineer to prototype it. That's a waste of his time. Mm-hmm. Like he should, he or she should work on the final product, not like prototyping something that we don't even know that will end up making it. So I do the prototyping now. Right. Like I can build a small HTML page. "Hey, this is how it," or look and then we can at least we can have a discussion like, and then the engineer can weigh in. So, uh, and then, you know, then you pass it off and then it makes sense, right?

So a lot of those areas and, and then obviously a lot of, lot of other things I, I'm sure like, you know, um, marketing and, and sales. Like they use, uh, a lot of, um, a lot of similar tools, but like, that's some of the ways that we've been able to, some of the ways that we've been able to, uh, use it. Because I'd say one last thing on it, what this allows me to do is scale up with the same people. Right. That's one. And two, it gives me more time to focus on the thing that actually matters, which is like talking to customers.

Jordan: Yeah.

Ali: Talking to customers. Like answering their emails, getting on calls with them, understanding what they have to say. If I have to spend a lot of time on the other stuff, writing stuff or figuring out where anything is, like it just takes away time from, 'cause I see no world where an AI is talking to my customer. I'm not gonna allow that to happen. Like, you know, I have to talk to my customers. Right. And I need to have the relationship with our customers. Right. Uh, and it will never be able to do that. I, I don't know, unless they put a chip in me and they will figure that out. Uh, but, but it gives me more time to do that. And I, I'm really, you know, obviously I'm seeing a lot of the results and I, I think that the only challenge I see right now is all these tools are in like different places. Mm-hmm. And I think long term, what will happen is they will converge.

Uh, and they will sort of, it's kind of like the example that I take of like, uh, like Amazon. It's, you know, I'm actually reading the book, The Everything Store. It's quite a great book. You should read it.

Jordan: I read it. Uh, boy. Oh yeah, I read it. I read it. Yeah, it's great.

Ali: Yeah. Yeah. I just started it yesterday. But yeah, the, like, you know, just the thing of like, we'll, we'll sell the book on kayaking and we'll also sell the kayak. Mm-hmm. Right? Mm-hmm. Like, uh, so like, I, I think, I think right now there's a lot of, like, for example, we, we are on AWS right? So we use a lot of AWS tools for, for a lot of like, uh, our AI. But then we have to use some of Google's models, uh, some of OpenAI's models. 'Cause you know, some of the models in AWS, you know, they don't have all the models.

Jordan: Yeah.

Ali: But for different use cases, we use different models and they, I can't even, even if I wanted to, I, they don't have it, so I need to go elsewhere. Then there are, there are other tools like, you know, just these orchestration layers, right? Like that get data from one place to the other. Uh, you know, that's my only concern, that the only thing I would say is long term, like they would all converge, which I don't think is good for the, I don't know if it's good for the market. 'Cause then it becomes like a monopoly, but I don't...

Jordan: Right. Anything that makes your life easier.

Ali: Yeah. I would say that's one, and I think two is, um, there's a lot of, uh, there's a lot of, uh, I would say, especially in the industry that I'm in, our customers, there's stigma about AI. Like, you know, what if it messes up or what is...

Jordan: Yeah.

Ali: You know, just, they are wary of it. But I actually did a presentation. We did a, we are part of this, um, a research foundation like for credit and, well, I, I was doing a training class for them, so like, uh, basically on AI. So it was a three segment class, and the first, my first segment on was, you are already using AI. You just don't know it. Like, you know, like, you know how much AI Amazon uses to figure out what you order next. Like it'd be crazy. There's an article on that, like it's crazy. Like you think it's easy that they figure out, oh, your dishwasher liquid is running out tomorrow. It's not that easy. They have to, or that they figure out which deals to serve to you out of the millions of deals that they have. Or how Uber prices you, you think that's easy? Like they've been using it. Like we just, we just don't even realize the amount that Netflix uses to suggest, to show you, like, they like, because I read about these things, right?

So it, it was good for me to do that class. So I think that stigma will go away and I think, I think long term what will happen is that we will all have a personal assistant that knows everything, like it's with us all the time, listens to all of our conversations, and we'll have a work version. Like I have a work laptop and a personal laptop. Like we'll have a work version and we'll have a...

Jordan: Yeah.

Ali: We'll have a personal version and like for example, I like to go to national parks and I like to take photos. If you ever go to adventuresofali.com, it's my website. I post photos there. So one of the problems that I was having is doing it manually, right? Like having to take the photo and upload it manually. So I basically created my own application that as soon as I upload a photo on Google Drive, it scans it, it uploads it to the website. It also posts on Twitter, generates a caption using AI.

Jordan: So good. Yeah, that's cool.

Ali: And okay. It cost me $180 to do it.

Jordan: Yeah, right.

Ali: Like, but I don't, it saves me so much time. So imagine like if, if there was, like right now still I have to go in and write, like tell somebody to... But imagine that somebody knew my life, right? Knew all the things I use and like, I just think it, and it happens. I was far away. But, you know, that's sort of what I envision. But who knows? What worries me though is, uh, over-reliance on it. And then also, because we have this problem where, you know, we, we interview engineers or product people, and so we give these like aptitude tests and like you can see that they're just copying from it.

Jordan: Yeah.

Ali: We actually have this, uh, test now where it's meant... like it's like, you know, have you watched Star Trek? Uh, like Kobayashi Maru.

Jordan: Oh yeah. Uh,

Ali: Like it, it's meant... like it's an unwinnable game. Like I don't, I want everybody to figure out, okay, who can just be like, okay, this can't solve it. But people still try to like tell you like, oh, I know the answer. So I, I think, I think that's one of my concerns. I, I think data privacy and whatnot, obviously figuring out like, you know, what data is good to share, what data is not good to share. You know, we take the, we take the approach right now that no data is good to share. So we pay premium for these models that, at least in writing, they give us like that, they're like, we have to pay premium for that. It costs significantly more, but like for our peace of mind, we do that. But I, I don't know other companies. Right? And I'm not talking about even companies in our space. I'm just talking about generally, like I, we use Jira for example. I don't, I, I, I don't know, like...

Jordan: Right.

Ali: You know, like what they do and what they do with our, like, you know, our product requirements and whatnot. So, you know, those are some of, uh, some of my worries. And finally, I think I, I obviously don't want to lose the human touch, like, you know, with our customers, generally in life. Like, you know, I, I don't wanna do that, but yeah. Sorry I went rambling for a second.

Jordan: No, that's okay. But Ali, you know, I really appreciate your time. This has been awesome. Um, love your thoughts on this. Where can people find you, Ali? You talked about it before. You have a YouTube, you have your own podcast, The Ali Show.

Ali: I do have my own podcast. People can find me, uh, on The Ali Show on Spotify, Apple Music, or wherever else you get your podcasts. YouTube. YouTube as well. See Bectrran Inc.

Jordan: At least I find these episodes on YouTube too, right?

Ali: Yeah. It's called The Ali Show.

Jordan: The Ali Show. Yeah. You can, you can always reach out to us, uh, you know, on, uh, on bran.com. I actually, give me my email. You have my email. You can put my email on the podcast as well, or, or something so they can, they can reach out to me. Yeah, if you have any questions, definitely feel free to reach out. Uh, and definitely thanks for being a, thanks for being a great partner, Mr. Jordan. I, I just want to take a moment to shout out to our partnership. You know, we, we found each other at a very difficult time. In our difficult time when we were trying to replace the partner that we had. You know, 'cause we were just not getting the right results. And you, you really came in and you really, uh, you really turned it around. Mr. Jordan, we get that from our customers. 'Cause our biggest, our biggest, uh, you know, like the way we judge products and like how good our customers like it. And definitely you've given us a lot of... you really, I always judge products by how good, how much peace of mind it gives me and how much I can sleep better. So you will let me sleep better than I...

Jordan: I, uh, yeah.

Ali: I I would say no better way to, no better way to judge a product. So really I would just like to shout out to you. You either have a superior product and a superior team to support the product, which is very important. And you know, really, uh, and we're obviously, you and I, we are working on new things together.

Jordan: Yes, we are.

Ali: Which we cannot talk about, but we will be releasing new things soon. So that's also gonna be very interesting and, uh, you know, hope, uh, you know, it'll be a long partnership. So, but yeah, definitely. Thank you. Thank you for all your help and support, Mr. Jordan.

Jordan: I appreciate it, Ali. Thank you so much. Have a great day.