Rethinking ERP: AI-Driven Workflow Innovation

In this episode of The Disruptor Podcast, host John Kundtz sits down with Harish Chandramowli, founder of Flaire Software, to explore how AI is revolutionizing traditional ERP systems. 

Drawing on his background in cloud security at MongoDB and Bloomberg, Harish shares how his deep technical expertise and hands-on experience led him to create Flaire, a lightweight, AI-enabled workflow optimization platform specifically designed for the fashion industry.

Harish walks us through how most SMBs are stuck trying to force their business workflows into rigid ERP systems built for enterprise-scale complexity. 

Instead, he advocates for a radically different approach: utilizing AI and platforms like Shopify to create customized operational workflows, without the overhead and cost associated with traditional ERP deployments.

We discuss: 

• The evolution from cloud security to fashion-tech entrepreneurship
• What a “schemaless ERP” really means—and why it matters
• The hidden costs and traps of customizing legacy ERP systems
• Real-world examples of AI-driven workflow automation, including SKU creation and product launches
• Why knowing your internal workflows is critical before automating with AI
• How AI is changing the speed, cost, and expectations around operational software

Harish’s philosophy is clear: Don’t overbuild. Instead, understand your business deeply, build simple, iterate quickly, and only automate what’s ready for it.

Whether you're a founder, operations leader, or curious technologist, this episode offers a fresh take on how to streamline your back office with modern tools and AI.

To learn more about Harish and Flaire Software, visit their website (flairesoftware.com) and connect with Harish on LinkedIn.

Chapters

• 0:00 Rethinking ERP: AI-Driven Workflow Innovation with Harish Chandramowli
• 1:05 Meet Harish: From Cloud Security to Flair Software
• 3:17 The Birth of Flair Software
• 4:06 Understanding ERP and Its Challenges
• 6:12 Flair Software's Innovative Approach
• 14:03 Real-Life Applications and AI Integration
• 21:18 Advice for Founders and Entrepreneurs
• 25:32 Conclusion and Final Thoughts

Transcript

John Kundtz: 0:00

Disrupting traditional ERP implementations with AI-driven workflow innovation. Hi everyone, I'm your host, john Kuntz, and welcome to this edition of the Disruptor podcast. For those that are new to our show, the Disruptor Podcast. For those that are new to our show, the Disruptor Series is your blueprint for groundbreaking innovation. We started this podcast in December of 2022 as a periodic segment of the Apex Podcast. Our vision was to go beyond conventional wisdom by confronting the status quo, exposing the raw power of disruptive thinking. Confronting the status quo exposing the raw power of disruptive thinking. Today, we will be talking with the founder of Flare Software as we explore how organizations are optimizing their operations without investing in traditional ERP systems and how AI is making all that possible. Welcome to the show, harish. Glad to have you on. Thanks for having me. Welcome to the show, harish. Glad to have you on. Thanks for having me. Tell us a little bit about your background, from cloud security to Bloomberg and MongoDB, and what led you to start Flare Software.

Harish Chandramowli: 1:13

I started at Johns Hopkins for cloud security. It was really amazing, right. But I also noticed that a lot of my friends went on to go and start, like some of the amazing companies out there. At the time, entrepreneur journey was not suitable for me due to personal reasons, but that's when I joined Bloomberg. One of the things I came while coming out of cloud security at Hopkins for me that stood out is that, like I never wanted to be a consultant, I always wanted to build and I wanted to be a developer, so I joined Bloomberg. When I joined Bloomberg, they said like, hey, there is this niche field where you build authentication system, you build the encryption systems, so you need the security knowledge, but you are a software engineer. So that's how the journey at Bloomberg started From there.

Harish Chandramowli: 2:02

Mongodb Atlas just came out and they were trying to hire someone for security so that they can start building the security team, and that led to MongoDB. Again, the team was pretty much the same, like building products over, like saying that I'm an expert in something, I guess. So I joined them as a security engineer. I moved around different teams based on like what security? What they needed some security expertise. That means I got the breadth of knowledge. I got to build products for different teams, which led me to be joining as a lead engineer for one of their core Atlas cloud team and during those few years as a lead as on-call person, I also observed how data is being used by my customers and some of our biggest customers are in retail. So that made me more and more curious on how data is being used in the retail industry and why that's a big thing in retail industry. And obviously when I looked at NetSuite, oracle being like the biggest player in like an ERP industry made me even more curious. It's a database company. Why are they shedding so much money in this industry? That's kind of like my curiosity around the industry. The way it started was that I was in this shop called ONS in Soho, new York downtown. I was observing how they were operating. I was talking to their founders, I sat in some of their team meetings and the more I observed it felt like the back office of fashion is a workflow and data problem.

Harish Chandramowli: 3:35

When we think about fashion, fashion has a lot of technical softwares involved in it. Starting, which is a little more obvious to everyone, is design, because you kind of try to figure out what is the embroidery, how bunch of different embroidery styles can get put together and visually how it looks before production. The moment that happens. Then the process of procurement, ordering, negotiating different factories, figuring out how to order, how much cash you are committing to different products All those complexities come into place and that's where ERP lifecycle begins.

Harish Chandramowli: 4:06

You can think of ERP as something that brings various moving parts inside the company together. So the first part I talked about how ERP comes brings the data from the design team into production lifecycle. The ERP again, once a product is received is the one that pushes and helps maintain your inventories and then, like, put listed in different websites shopify, amazon or whatever it is and final piece is like you make an order in your website. Then erp again comes into play on, like making sure your order goes to warehouse and warehouse ships it properly and then you get back the tracking number. So erp kind of plays a very crucial role in your whole workflow and how data moves, even to place a single order in your in someone's website.

Harish Chandramowli: 4:52

And that made me more curious and, having coming from a data background, having built a lot of data products, I felt like this area is ripe for innovation and some of the questions you usually ask, like I was happy at mongodb, I was happy at bloombergs.

Harish Chandramowli: 5:06

So the first question you always ask if I want to do this, why I want to do this. My question always was like shopify has done a tremendous job of e-commerce, especially when it comes to sales platform, and I kind of don't want to do something over there because when someone has done a great job, I know I cannot as a single person, as a startup, do a better job than shopify. Oracle has done a great job for bigger industries 100 plus million band who needs, like a lot of engineers, customizations. Yes, I am not going to disrupt the engine, that industry, at least on day one. But then I also realize that there is like smb market who can't spend that much on software engineering but can a lot out of data by bringing in innovation in this field, and that's what led me to start Flare.

John Kundtz: 5:52

One of the things I love about this podcast is I interview people that have these backstories, that sort of go weaving around, and eventually they get to where they are today. That's fascinating. Eventually they get to where they are today. That's fascinating. And it's cool that you kept building upon your knowledge and your desire to figure out how things work on the back. So describe to me Blair you sort of went into this a little bit but describe to me the way you use your software to avoid this. Traditional ERP systems because, as we know, as you mentioned, I mean ERPs for large enterprises is hugely expensive. It's uber, time consuming and it's also, once you're in, you're sort of locked in right. So if you go down the Oracle path, the switching costs are huge once you implement it, Certainly from my experience in the larger enterprises. Talk a little bit about what kind of mistakes you see organizations making when they try to scale with traditional ERP platform.

Harish Chandramowli: 6:55

So I'm going to kind of spread it out. At the beginning I'm going to talk about how Flan came up in terms of innovation and make it easy. But obviously over the two years with it and everything what we are building as well, like how I see the industry keeps evolving. That's not the important thing, right? You need to keep up with the industry and bring in that innovation. Let me start with when I started and why it was very useful for my customers to come into us and even do that migration. You are right, doing an erP migration is a big, big step and trusting a startup to do is like even bigger step. Our customers like Alala, rta3, all of them are like 30-50 million brands, not like a new shop that came up. So this is how it started, right?

Harish Chandramowli: 7:38

One of the thing I noticed when I was in back office is that people see each of your dress and want to analyze whether to buy it, whether to replenish it in a different. If it's a t-shirt, it's just size and color, so you want to see which color is moving fast, which size I need to order more, which size I need to order more for me, or so that makes sense, whereas when you come to female athletic wear, then it becomes more complicated. You have cup size, you have torso length, you haveor and the regular sizes. So now you are looking even more matrices to make your plan to do analysis around what is selling fast, what is not selling fast for a beginning season, what I have to do. So that's why the attribution came into play. That was one of the biggest reason people are moving In traditional ERPs, even in like see, if you want these custom attribution, you need to pay for a software engineer, you need to customize it, and when I looked at it, for me just felt like an extension to MongoDB.

Harish Chandramowli: 8:35

In typical database you think of primitives as numbers and your alphanumeric parameters, but when I speak more to these vertical, specific industries color, for example it's like a primitive data type to them, barcodes, which you see at the top it's like a primitive data types. So what I ended up doing is like extending MongoDB's functionalities to include these primitive types which each of the brands, and from that we built a schema-less ERP. That means we don't charge people for customization. You come to us and say, hey, this is exactly how I look at my business, these are the properties I want to be tracking for my clothes and we set them up. That was compelling enough for them. They were willing to take the risk to do the data issues. That's pretty much the reason why people started and kind of like it's important that when you are you, when you are in your business, you get to make decisions that makes your business better and not change yourself for the software that's available, software that's cheaper. That's first one. Second one we did.

Harish Chandramowli: 9:42

I started it as fully apparel specific and even if you are not in Shopify, we were building it. But the more I started building it, the more I noticed is that, unlike traditional ERP, we could tightly couple with Shopify. Shopify has done a tremendous job of exposing APIs and whatever Shopify does well, I don't want to repeat. I just want to pass on that load to Shopify.

Harish Chandramowli: 10:05

What this meant at the end of the day for my customers is that they end up being something like a Shopify app. Once you migrate to us for another migration or if you want to use another new software, you can use any of the software in Shopify app ecosystem. You don't want to. Erp never becomes a bottleneck for you to try different apps marketing apps, offer apps, bundles, apps whereas with traditional erp. When you want a new app, because erp is not tightly coupled with shopify, then your erp needs to support those integrations. So that means you are not nimble, you are not fast and, let's be honest, not every erp can build an ecosystem that shop has well, the amount of ads that shoppers and that's the other part of how I see that we could build something that's different while giving users the power and bigger functionalities.

John Kundtz: 10:57

So, in a nutshell, I think what you're trying to say is you're sort of bringing together the I call it the back office and the front office right. So the back office is all the ERP, traditional inventory management and orders and all that stuff that goes in the back end. The buyer right never really sees that. They see the front end, which is the Shopify, and so you're bringing both of those together. I think and that's probably the mistake I'm hearing that maybe organizations might make.

Harish Chandramowli: 11:24

Absolutely right. Traditional ERPs used to replicate what our data is in shop. If I ask them in the back office, that is disconnects and everything we try to be like. Let's bring things together because it's a single stack. Why are you thinking as each and everything is very different?

John Kundtz: 11:38

Which sort of leads us into the next question we were talking about in the prep meeting was this whole idea of workflow optimization a better path forward? So what does that really mean in practice for, let's say, a small or mid-sized organization that you might be working with?

Harish Chandramowli: 11:55

Yes. So in terms of workflow optimizations, a lot of people have very custom workflows. Some of them print labels For some of them, baha House prints labels and each and every one look, even the headcounts they have. The type of people they have as business evolves is very different, especially the bigger the business, once you cross that 10, 15 million mark. Now you have an operation person and you have a production person who is different from an operation person. Now you have a customer service person who is different from these people. How do you bring everyone together?

Harish Chandramowli: 12:27

One of the things that a has done a really great job is being able to build these integrations based on various tooling. You use various workflow. You have the customizations at the top, the last. At the beginning, I usually tell people to change their workflow so that you adapt to the software and be more efficient. But in the last six months to eight months especially when people come and say, hey, this is my workflow, I need this customization I was able to go back and build it in two days, just like the smaller changes and workflow optimizations, and that's how I also see things evolving, moving forward and being able to build these a little more in a nimble, agile and quicker way that you can actually go to people and say this is my workflow, this is my business context and engineers come up with the software that adopts to your workflow and your business context and you don't have to pay $100,000 to $100,000 for implementation.

John Kundtz: 13:24

That makes sense. In the old days, you would try to avoid customizations, correct, because every time you did an upgrade, every time you made a change, the custom integrations were always what helped people from moving from one version of enterprise software to another. It's not here that AI is allowing you to do that quickly. Therefore, you can bring the both worlds together. You could quickly deploy customizations that aren't going to necessarily bite you in the backside in the future when you have to make upgrades or changes. Is that sort of what you're saying? Yes, absolutely right. So walk us through a real life example of how that works.

Harish Chandramowli: 14:04

For me, the real life example is SKU number generation. Everyone generates SKU number with some kind of custom logic. People were using spreadsheet to generate those SKU numbers and I was able to just write a quick custom logic in the front end that says hey, for this customer, this is how they create SKU numbers. Like, if you have color red, that's number 48. If you have that, if the cup size is x, c, add c to n.

Harish Chandramowli: 14:32

All those customizations now is so much easier that people don't need to manually do it and you get your own specific things. Number one. Number two for example, if you want people use project management tools to create designs and everything, and the moment everything gets approved, they go to shopify and then they create. The moment everything gets approved, they go to Shopify and then they create the product in Shopify. You don't need to do that AA. You can build that customization Once in my project management tool. I am approved directly, just go and create the product in Shopify. So those kind of bigger customizations.

Harish Chandramowli: 15:04

It used to take a week because you need to know the APIs for Shopify, you need to know the APAs for these project management tools. Aa makes it easy. Number two we always think about product, as all the edge cases, we need to fix some things. In fashion industry or in most of the vertical specific industry, people do the exact same workflow. You just need to make sure their day-to-day workflow works for these custom software and that, so that means testing is easier. Users become your testers. They're happy and you get all these kinds of workflow automations in a matter of days.

John Kundtz: 15:41

It leads me to the following question In your experience, your clients or your customers, do they understand their workflow in the beginning, or is that part of the upfront process I've been reading and listening to and observing and even experiencing is? Ai is super powerful, but you have to know what you're doing. You have to really take the time and understand what your workflow is. I mean, you can't automate something if you don't know what it is. Is that making sense? Is that right?

Harish Chandramowli: 16:11

It's absolutely right. It's one of the places that I use AI to meet myself more better at sales. So one of the things I do is like exercises I do in the first call is ask them explain to me purely in business terms, don't explain to me how people use your ERP. I don't want to hear that from business terms. Explain me what people you have and what is the functionality for each one, and then I feed it to Android, which gives me a really smart my JS workflow chart. Then I sit with my customers and be like this is a very visual workflow chart. This is what I understood from you. Can you validate? This is how your business operates. Then it becomes more easy to say how my software fit into their workflows.

John Kundtz: 16:54

So I love that for actually two reasons. One is what you're describing to me is something I would call you're co-creating with your client or prospect, so you're trying to understand their business, probably better than they may understand it I know when I've done this. A lot of times I've laid stuff out like that and the client looks at me and goes, wow, I never knew that before. I don't even know that about ourselves and I work here every day and now it also allows you to follow up with them correct and start to build that relationship.

John Kundtz: 17:29

One thing hasn't changed in the last 40 years that I've been selling is people still buy from people they trust, especially if you're new, you're an entrepreneur, you've got a startup, whatever, but it doesn't matter. You can be a large or small company. At the end of the day, they got to look at you in the eye and say, hey, Harish, I believe you can help me, and you don't do that just by sending them a bunch of emails or just assuming you know what they're doing. I think that's great. Let's circle back on the AI play. Ai is effectively being used what you do without over-promising what you can do. I mean you described a little bit about it in the pre-sales, but over on the back end or through the course of the buyer's journey and ultimately, the implementation of your software buyer's journey and, ultimately, the implementation of your software.

Harish Chandramowli: 18:24

So one of the things that was very important, even as a CTO and founder, is to keep an eye out of which evolution of AI is production ready versus which is not. We hear a lot about agents, how agents can do everything for you in production, but think of someone getting an invoice, keying the invoice in to make the payment. If an agent is doing it, if it makes a mistake, then the repercussions are big. You are not paying the right amount, your books doesn't tally, then you need to go through like thousands of invoices to do it. But so agents is not there for that specific use case, whereas the use case where, example, some people in our industry miss emails that says purchase order is getting delayed, I could easily parse every email that comes in. When I feel something seems to be off for their manufacturing communication, I can flag that email at the top so that people can go and verify it. And a is that a can do that really well. So understanding which part is hype and it and AI is that AI can do that really well. So understanding which part is high and which is production ready is really important and which is very critical to users is important. The second part is repeatability. Right, lot of times when I talk about invoice passing or uploading your customer orders to understand how they operate, some of them needs to be repeatable and you need to be sure that when you repeat it it will give you the exact same results. And AI doesn't do that. But what AI can do is if I use AI to build those customizations as an app, if I do it to build all the custom logics, then you can test it, deploy those custom logics and productions. You know that when you are using the product it's not AIs they'll go and give you the exact same results, whether it's a SKU number generation, like I mentioned, or creating some custom PDF to send to your customers for invoicing All those things. Once you use AI to build it faster, you can use it in production Agents.

Harish Chandramowli: 20:13

Yes, email passing agents exist. If you come up with a invoice or, like we call it thing, list in our industry which comes manufacturers and what item they are producing and how much, it used to be hundreds of lines. Someone manually used to enter those key in those things in erp, but now you can upload it. We pre-pass but we don't really enter the value. Rather we ask someone to verify it. Now your job of keying in for one hour changes to a job of five minutes of verifying whether the past values are right. So that's how I see ai, and obviously a month from now, like you mentioned, if you ask me, maybe agents is there, I would say agent would do the whole thing. It's all the hype. You can sell a dream, but you also need to be understanding which is production ready, which can be used in real life today, and not just what is going to come.

John Kundtz: 21:03

Yeah, I'm glad you hit on agents, because it drives me crazy, because if you watch the external marketing and newscasts or whatever, everybody's talking about agents and, as you mentioned, it's coming, but it's not the panacea, especially today. So great advice. So, speaking of advice, tell us, as a founder, if there are any founders listening today, what advice would you give to them about building the right operational stack, particularly when it comes to technical? We're all overwhelmed with tech options these days.

Harish Chandramowli: 21:33

Keep it simple. Pick things that you are really comfortable with. I even while working at mongolian stuff, while I have seen people how they use databases, my first thought is that unless you are like the coinbase and google of the world, the reality is it doesn't matter, even if your query is 10 milliseconds slower, your users won't do it. So pick something that you are comfortable. I mean obviously. Obviously there's other changes if you're building a product, but when you are building these innovations in ERP or these kind of like very real-world problems, trying out different ways, trying out different things helps. People needs a lot of vibrations. So pick something that's so easy to you that you can trade until you see a market fit. Then, obviously, you can just go back and upgrade everything.

John Kundtz: 22:21

That's great advice, I agree. I see people, especially in the technology space, just trying to create solutions purely on the technology, and what I really have picked up through the course of this conversation is you're really focusing on which is what I advise all my salespeople to do and all the technical people that I advise the product managers of the world and stuff like that is really focus on the why. Why is what you're building important to, ultimately, your end user or your buyer or your customer or your client? And then build it quickly and simply and then iterate over and over and over again.

Harish Chandramowli: 23:04

Yes, One example I would usually give people is like when I talk about email passing, I can make sure Google can tell me when a new email arrives, or I can just query every 10 minutes on whether this person has new emails. The later one is quick to build, not the most efficient way, but until you prove out that it's useful, just in the quickest way.

John Kundtz: 23:24

That's great. Yeah, it's a classic agile methodology or mantras I like to say fail fast and cheaply. Right. So build something. Test it out. If it doesn't work, throw it out. Pivot If it works. Great. If it needs to be better, test it out. If it doesn't work, throw it out. Pivot If it works. Great. If it needs to be better, make it better. All right, before we wrap up, switch gear. What's something you've learned the hard way in your experience that has shaped you to run your business today?

Harish Chandramowli: 23:49

Personally I feel when I joined Hopkins I had to take two jobs and like the five grad courses that means I kind of got so used to working long hours and working long hours is very, very important as a founder at the beginning stages because you need to find the product market fit quickly. So that experience definitely shaped that. And over the last two years things I learned as founder is not saying yes to all the customers and being very pinpointed in the type of customers you want to onboard at the beginning. It's so exciting to get lost. I got lost where some people know who are not in shop. If I have my, I love the solution I want to onboard and end up building a lot for them. But looking back, no, I don't have the resources. I don't have the money being leased and focused on a small group, even though you can have a bigger vision, so important I think that's great advice.

John Kundtz: 24:50

I've seen that mistake that you said you avoided over and over again. Where, especially as a new company or a founder is every prospect is a good deal, it takes a maturity level to say no, we're not a good fit right.

John Kundtz: 25:05

And understand that you could get just sucked down a rat hole and blow all your cash on one client and therefore have so many small early stage companies. It's not that they don't have good ideas. It doesn't mean they have bad management. It just a lot of times they just run out of cash and if you don't have cash, unfortunately all the good things you do can't happen. All right, we'll wrap it up. Want to thank you for sharing all these insights and experiences. How can people learn more about you and your company and your services? Where do they go? What are your socials? What do you want to share with them?

Harish Chandramowli: 25:47

I always tell people they can follow me on LinkedIn. I usually update, keep my LinkedIn updated. You will see what we are doing in the company in my LinkedIn.

John Kundtz: 25:55

any thoughts I have on LinkedIn and if you have any questions, you will see what we are doing in the company in my LinkedIn, any thoughts I have on LinkedIn and if you have any questions, you can obviously DM me and always happy to reply and of course we will include those links in the show notes so you can check it out. You want to learn more or connect? And, harish, I'll give you the last word before we wrap up the show.

Harish Chandramowli: 26:15

I'll give you the last word before we wrap up the show. One of the things I would say is just have fun. If you're picking a software, any job, whatever it is, just say pretty much B-Bull, that's awesome.

John Kundtz: 26:26

All right, bud. Hey, I'm John Kuntz. Thanks for joining us on this edition of the Disruptor Podcast. Have a great day. Thanks a lot. Thank you.