SC-104 · Founder
Transforming Music Metadata with AI – A New Chapter with Noctil
Guest: Jacob Varghese, Founder of Noctil
Summary
Jacob Varghese is a technologist who moved into music in 2012 while reviewing a proposal for a global repertoire, then founded the metadata management platform Noctil in 2016 and built its first product by 2018. The core problem Noctil solves is matching: matching usage reports from DSPs, radio and TV against sound recordings or musical works, and matching the same song's metadata when it arrives from ten different publishers representing ten different co-writers.
The industry has traditionally solved this with rule-based logic, which Varghese says is inefficient and needs constant manual upkeep as new data sources appear. Noctil instead trains supervised machine learning models per client on their previously confirmed matches, which he says lifts matching accuracy by 10 to 15 percent, translating directly into more money reaching rights holders. Data quality still varies a lot by source, since Apple Music and Spotify report cleanly while YouTube, radio and public performance data is far less reliable.
Unmatched or disputed rights create black box money, revenue a collecting society holds because it cannot determine who owns a track or how ownership splits. Varghese says the deeper fix requires simplifying licensing itself, since better software alone will not solve it, and predicts change will come from a mix of new business models, AI-driven direct licensing and shifting policy. Noctil has stayed fully bootstrapped since it built its first product for a paying client instead of pitching investors, and now employs a small team including an office in India.
As of the episode's release on 8 April 2025.
Key takeaways
- 01Matching is the core process behind royalty collection: matching usage logs to recordings, and matching the same song's metadata when ten co-writers send it through ten different publishers.
- 02Noctil trains a supervised machine learning model per client on that client's own confirmed matches, which Varghese says lifts matching accuracy by 10 to 15 percent.
- 03Data quality depends heavily on the source: Apple Music and Spotify report cleanly, while YouTube, radio and public performance data is often unreliable or incomplete.
- 04Black box money sits unpaid at a collecting society when it cannot determine who owns a track or how its ownership splits between writers, artists and labels.
- 05Noctil built its first product for a paying client rather than pitching investors and has stayed fully bootstrapped, reinvesting revenue instead of raising outside capital.
- 06Varghese believes fixing music metadata at scale needs simpler licensing itself rather than only better technology, and expects change to come from a mix of business models, AI and policy.
Guest
- Jacob Varghese, Founder at Noctil
Questions this episode answers
What is black box money in the music industry?
Black box money is revenue a collecting society or licensing company has received but cannot pay out, because it cannot determine which rights holder actually owns a track or how ownership splits between multiple writers. The money sits unpaid, sometimes for years, until the territory's own rules decide what happens to it.
How does Noctil use machine learning to fix royalty matching?
Noctil trains a supervised machine learning model for each client using that client's own previously confirmed matches, then keeps learning from what the client accepts or rejects afterward. Varghese says this lifted matching accuracy by 10 to 15 percent compared with the rule-based systems the industry traditionally used, meaning more money actually reaches rights holders.
Why is matching such a core problem in music royalties?
The same song's metadata often arrives at a licensing agency many times, once from each co-writer's different publishing company, and usage reports from DSPs, radio and TV also need matching against the correct sound recording or musical work. Entity resolution across these fragmented sources decides whether royalties actually get paid out.
Why did Noctil stay bootstrapped instead of raising investment?
Noctil's first product came out of a company's request for proposal, with the MVP started in 2016, so the company was bootstrapped from the start instead of raising money on an idea. The team reinvested revenue from each new client instead, deliberately building product-market fit and an enterprise-grade platform before ever pursuing outside funding.
Why is reported streaming and performance data often unreliable for royalty matching?
Varghese says data quality varies heavily depending on where it comes from. Platforms like Apple Music and Spotify report cleanly, but reporting from YouTube, radio stations and public performance venues is often missing identifiers or complete rights information, leaving gaps he describes as a lack of data, incorrect data and insufficient identification data.
Black box money is that you receive the money as a PRO or CMO, a licensing company, but you are not able to distribute that money. You don't know whom to give.
Episode notes
Metadata may be the unglamorous backbone of music, but it decides who gets paid. In this episode, entrepreneur and technologist Jacob Varghese explains how his company Noctil uses supervised machine‑learning to link millions of usage logs with the right ownership data, turning “black‑box” royalties into real income for creators. He traces his journey from a 2012 consulting project to a fully bootstrapped, modular platform that now supports publishers, labels, and collecting societies.
Along the way, you will hear why blanket licenses and legacy systems leave gaps, how AI can lift match rates by double‑digit percentages, and what policy changes could finally simplify licensing in the streaming and AI era. If you want a clear, jargon‑free look at the hidden infrastructure behind every play button, this conversation delivers it.
Learn more about the platform here; https://www.noctil.com/
Sponsored by Allfeat: Decentralized Blockchain Solutions for the Music Industry - https://www.allfeat.com/
Produced by Amplitude Ventures Consulting: Partners in Early-Stage Music Tech - https://amplitude.ventures
Topics
Transcript
Transcribed from the recording by the production team. Names and terms may be misspelled.
Read the full transcript
Jakob
Hey guys and welcome back to the Sound ConnectionsPodcast. Today we have Jacob in the studio. Welcome.
Jacob Varghese
Thanks Jacob, thanks for having me.
Jakob
We're going to talk about something very, very timely today, and it's going to be your founder journey with Noctil, but also AI and metadata in the music industry and rights management and all the complexities that has had and have to come. But Jacob, for the people who don't know who you are, who are you and what do?
Jacob Varghese
Sure. My name is Jacob Varghese. I'm the founder of Noctil. It's metadata management platform. Been in the, technologist. I'm in the technology side of the world for the last 24, yeah, 24 years. In probably 2012, I started working in the music industry.
Jakob
Yeah, and he started working with the music industry as a founder as far as I can see. How did that come to be?
Jacob Varghese
Not really. I have founded different companies before and in 2012, I happened to be working with one of the projects in the music industry and that got me interested in the industry and 2016 I started the company. Actually, we built the product, it like two years.
Jakob
Mm.
Jakob
Hmm.
Jacob Varghese
Built a product in 2018. It's actually, yeah.
Jakob
Great. Well, I think it's very important to sort of set the conversation because we're to talk about your journey with your company and sort of what you address. But could you explain a bit more in detail what Noctil is?
Jacob Varghese
So Noctil is a platform for simplifying the metadata management in the music and audiovisual industry. It's not specific to any industry, but we are focusing on this industry and we built a lot of modules that helps solve different problems in this metadata management side.
Jakob
And one of the interesting things when you start sort of talking with music industry people is that some of the very unsexy ideas that's like not in public knowledge, public sort of focus is oftentimes those where there's the biggest issues and metadata has been an issue for many, many years, I guess since digitalization and sort of how that whole journey went into it. But how did you sort of notice the problem the first time you heard about
Jacob Varghese
Yeah, like I said, when I started in the industry, I was asked to review some of the proposal for building a global repertoire for a consortium of licensing companies. I didn't know anything about rights. I didn't know anything about the music industry, how it works, how the royalties being collected and distributed and things like that. When I started looking into it and this, how the metadata is managed and matched. Matching is one of the key process of collecting and distributing the royalties. So that's how I found there's a real issue there with the metadata and how the metadata is managed because one reason is there are lot of inconsistency. Not complete, inaccurate data, a lot of problems because in the music industry the data is fragmented. Fragmented means there are lot of different right holders and they have different interests and they only care about those part of the metadata. The full picture is not there. It's just completely fragmented. When you put it together, it's really difficult to find everything in one place. So that's where we see there is an opportunity to, one, improve efficiency, and second, improve the accuracy of the, and also improve the transparency so that everyone knows where they are getting their money and how they are getting their money and things like that.
Jakob
So how specifically are you doing this? Like is it specific ways of using technology or maybe let's start with if I know new almost nothing about the music industry. Can you tell me about what you do specifically and how you do it? Just really to understand the fundamentals.
Jacob Varghese
Yeah, let me start with where, when we start at the, so like I said before, the usage matching is the core of this process of collecting and distributing royalties. So you have licensing companies, they license the music and these, whether it's DSPs or radio stations or TV stations or public performance, whatever it is, right? So you get all these logs from these different, exploitation sources, right? And then you have to do this matching against your, you know, your sound recording or the musical work, if you are working with musical, what's the publishing side? And that is where the matching is the core part of it. Not only the usage matching, but also when you manage the metadata. Let's say if you have a... you are working with the publishing side. And for one work, typically, let's say you have 10 different writers, and these 10 different writers might have 10 different publishing companies. And when they send their metadata, they send from 10 different publishing companies to this licensing agency. So you get the same thing 10 different times. They need to find out which one is this, right, so that they can put it together in one place, right? So it's a matching is a core process, not only the usage, but the whole bilateral between different territories, how do you communicate the rights of different right holders. Even that, you need to match your members. Like the writers, the right holders, like for example, the writers or the artists or even the labels, a lot of matching happens. So we see there is a problem there and traditionally they do the rule-based matching because there's different type of data's coming in. They just have different rules to match it. And we see that a lot of inefficiency in that.
Jacob Varghese
And accuracy problems there operationally. They need to change it when the data is different from different sources. And some companies have a lot of resources, time, so they can put that effort into building technology to solve those problems. But... In general, they don't have the time and the source to solve these problems. So everyone is trying to do the same thing. And we thought, you know, it's probably better to invest in the technology using machine learning and other technologies to solve this and scale it for other companies. So that's how we started. It's a match IQ as our first product module, which is basically the matching engine. It's basically an entity resolution. How do you find songs? How do you find musical work? How do you find an artist? How do you find a label, a record label? It's all matching. It's an entity resolution. How do you identify one?
Jakob
Mm.
Jacob Varghese
A specific entity.
Jakob
If you listen to these problems as an outsider, it just seems like a very inefficient way to have an industry. When did this get decided that it was done this way? Is this also the only and best way to arrange an industry? I know we can't touch it now, but it's just an interesting question from my side. Did something go wrong at one point where this system became overly complex?
Jacob Varghese
Yeah, it's already complex. So when you think about it, there are a lot of technologies there for identification site, like the music recognition technologies, MRTs. But you still need to connect that with your rights information. Who owns what?
Jakob
Mm.
Jacob Varghese
So there are some gaps in the process, right? So that needs to be identified. How do you, the one problem is that the licensing, when you do the licensing, it's typically a blanket licensing. You're not licensing for one track per track, right? You're just licensing everything. And these companies will, is going to report what they played. It's a list of things and could be anything what they know typically, right? So what they have in their systems and may not connect with what actually in your licensing companies or in the right holders data, right? So that is where the challenge is.
Jakob
Hmm.
Jakob
Yes, so basically, again, you mentioned machine learning. Is your role to then bridge the gap from the reporting side to the right told as database? So you sort of like tie the ends together? How does that work?
Jacob Varghese
So the machine learning, we use it for just the matching. So how do you create, we use supervised models, supervised machine learning models to do the matching. So it's basically we train the data based on our clients' So they have the previously trained, confirmed labeled data, then we use that and. Create the models for them and use that for doing the matching. And then continuously they are changing, are accepting and rejecting things. And we put that into a loop where we can learn from the changes or learn from these decisions. So it's a continuous learning model. So the platform is really basically how easily we can build these models and deploy it for production. That is making that process simplified.
Jakob
And is this like, is this a model per client? You find your per client or how does this normally work?
Jacob Varghese
So right now it's a model for clients because we work with very different, you know, type of entities within the industry. We work with the publishing companies, we work with the labels, we work with, you know, collective licensing agencies from the neighboring right side. We work with publishing administrators. So it's different and we also have AV side.
Jakob
Hmm.
Jacob Varghese
Audiovisuals, it's a very similar thing. So it's all different because the musical work is different than the sound recording metadata. Audiovisual metadata is different than anything else. So it's a very specific to industry segment and sometimes it's blind.
Jakob
Mm.
Jakob
Mm. Very interesting, and I think it's also a very technical approach to solving the problem, which might also be the best one. There's always this sort of different angles from people joining the music industry as founders, and oftentimes it's out of love for music, and that sometimes goes well and sometimes it doesn't. But you come from outside of the music industry and sort of have a technology angle. The machine learning part that you're describing, which again for me, I haven't heard of that approach before, there might be, but I haven't heard of it. Was that sort of the original intent that you sort of looked at machine learning from when you started to say this is a good way to approach this?
Jacob Varghese
Yeah, yeah. So when we started in 2016, I built a lot of matching engines for these companies before with traditional rule-based, logic-based systems. So I have an experience in that. So based on that experience, I see that, we have this new technology that's available. Now we can scale with lot of different
Jakob
Hmm.
Jacob Varghese
Products available in this industry, the machine learning and AI. Why don't we use that and solve this problem in a way we can reduce operational costs. We don't need to maintain these rules. You don't need to maintain the process itself. And it can continuously do the improvement and give you an edge in the accuracy. It's improved 10 to 15 % increase in the matching person. That means more money, right? More money to the right holders. This is continuously, you know, this was the original idea of using the technology. So basically, our goal was to create this accessibility to technology and advancement in technology and research to these companies in an affordable way. That was originally our idea to start with it.
Jakob
And I can imagine back in 2016 and 2018 running this technology well would be more expensive than we see now. Is that sort of what you're experiencing also that like GPUs and different models you're using are just becoming cheaper and cheaper and basically you're getting a better and better business or like what's what you're seeing there?
Jacob Varghese
Yeah, so when we started, we didn't have a lot of tools, ready-made tools available at that time. So we had to build it from our own. And then it was expensive to process also. So we need to find a, even if you have a solution, we need to make sure that it's cost-effective, that it's financially feasible, commercially feasible. To give to companies, right? So we try to build things as much as efficiently and cost-effectively as much as possible, right? So when we started, we didn't have those tools available from now, if you look at AWS or Microsoft or Google, have a lot of tools right now after, you know.
Jakob
Hmm.
Jacob Varghese
In those, you know, 2020, 2018 and all, there a lot of tools released by these companies. So then we had to, you know, we tried to find things which are more, you know, commercially, you know, viable, changing and making that changes. Initially, was some of the technology we were using was expensive and so was not really making sense at that time.
Jakob
Yeah, I can imagine there must have been some interesting fundraising conversation with investors. I don't know your investment story, but again, you are working with Machinery in 2018. That was not cheap. What was your fundraising journey going into this? Do you feel like there was people who believed you or supported you? What was that journey?
Jacob Varghese
That's really interesting. So we are bootstrapped. Yeah, so we did not start with the fundraising. Because we started the company, we started this product itself is based on a proposal, a request for proposal. So some company asked us to give a solution and
Jakob
Really?
Jacob Varghese
You know, they have the same problem and they want to do that. And we started this MVP in 2016. So it's not kind of, you know, we have an idea and we have to sell it because we already have this, know, somebody already wants to use some solution like this. So we started going from that journey and then eventually we started building like, you know, other customers coming into the platform and basically we reinvested our revenue into the company and it's an organic growth for us.
Jakob
That is so interesting. There's many different angles you can tackle starting a company. And one of the things that I'm getting to sort of be more and convinced in is really making sure you have revenue or you have traction from the start. And there's many that preach that gospel. But what I think is particularly correct with the music industry is that
Jacob Varghese
You
Jakob
If you intend to start a business, getting capital for anything would be most likely statistically even more difficult than usual. Which means I have started to believe more and more in bootstrapping in music or something like very close to it. Just because it's so incredibly difficult raising capital and you guys have been, I wouldn't say lucky, but like you've been, you've been not using two years to try to chase other people to believe you is a good place to start in music. Have you guys ever raised capital? Has that just been bootstrapped all the way, or never pursued it?
Jacob Varghese
It's all the way bootstrap. So what happened is that, you know, when we started getting traction, we also build products because of different segments within the industry have very different needs. Our goal was to build the product and solve these problems before we reach to any investors. We need to make sure that the product and the product market fit is there and we should have an enterprise level, enterprise grade platform, so before we can scale.
Jakob
Hmm.
Jacob Varghese
So we were like each time when we get a new customers, we were building and reinforcing things and building new features, new modules. So that's kind of our journey. But this is not a, our product is not a music or entertainment industry specific. So we knew that this industry is small, very niche, and getting the funding is difficult. We didn't spend time on chasing for fund or investors, right? Because this is when you build this complete vision, right? We can go to any industry, right? So it's not, we are industry agnostic as a product, but we are focusing on this industry. We understand this industry. We understand the different problems in the industry. So we can customize it, focus on solving their problem using these tools, using this platform. And so we made it very configurable and no code platform and very modular. That means you can plug and play different things to solve different problems, different segment within the industry.
Jakob
It's just an interesting way of building the companies. And I think one of the things that I find in common with most founded journeys is that it borderlines insanity. And it's very, very nice sometimes to hear journeys that, you know, most likely are based insanity or like, okay, we need to make sure that there's actually business here to be pursued before we sort of jump into it. Has that always been a personality trait for you? Why was this the approach you went into and not that typical, let's just bet everything at once kind of mentality?
Jacob Varghese
This is my third attempt at building something as a company. I was always interested in building things and always interested in making financially viable businesses. So this is my experiment, my journey to show that, okay, we can build something, make financially viable and create economy back to the community. So without, you probably need, you need in order to scale, to scale into another level, probably need funding, need lot of resources. But you still can build things, you can still build sustainable business from the bottom and then you can grow. That's just to show that is possible.
Jakob
Hmm. But what is the status in the industry right now? Obviously metadata is something that's talk of the town for, well, couple of decades now and really hasn't been solved at scale. And I don't know the depths of as of why. I think there's probably many things this plays in. But what is the status of metadata? Like, is this becoming a more streamlined thing because when we talk about for example the black box maybe you can also explain what that means that's obviously something that hasn't been solved at scale so what is the status?
Jacob Varghese
So I think how we consume the music has changed the last 20 years and that created lot of billions of billions of data points and records. And that created some of these companies have a real problem because they created us the business itself is based on very legacy business concepts. What I meant by that is like, for example, some of these agencies, some of the PROs and things like that, basically looking for, you know, they started as the business probably when people saw street music.
Jakob
Mm.
Jacob Varghese
Today, you don't sell anything without getting any revenue, without a recording that is played somewhere, right? Otherwise, you don't just sell, you can't sell anything, right? You have to have a recording. Some of the puros right now, they don't even register just the song, just the music or work, unless you have a attached recording. So you need to the value, right? Otherwise, it's a waste of time, a waste of data, right? So it's the whole thing. And the process is different. Process is legacy, and they have the systems. It's also, in some cases, legacy inefficient. I hope, I hope in some point, there will be a simplified licensing model, simplified usage and model that will happen. But right now, this data is a mess. That data is in the, you know, but there are a lot of initiatives coming up to solve many problems within the industry. So, there is no silver bullet to solve everything. I have seen so many companies coming up, like tech companies, like when, probably when we have this, the boom of blockchain and all that. A lot of companies came and gone. They tried to solve this with blockchain. So many companies, think many startups at that point, I don't think they understood the complexities. It's not the technology problem, it's the process problem, it's the business problem that needs to be solved before. How the licensing today happens, needs to be revised. The policy needs to be revised. So there are a lot of things there. of that, the data is in a mess. Data is in a frack.
Jakob
Hmm. So I'm just trying to understand in depth of what is working, what is not working. And I might be butchering this and I'd say a lot of wrong things, but I'll just make an effort and then you can correct me if I'm wrong and explain us why. So as far as I would presume, the technology companies that are facing consumers such as Spotify would most likely have pretty decent data output, at least in a theoretical level. They provide data. But there is a lot of issues with legacy systems for PRO, CRMOS that's supposed to pair that data output with what rights holder gets what. And then there's obviously also some other issues that I don't know of. What is exactly the issues you see repeating? Where are the problems, as specific as you can get? So we can sort of try to understand how you might also think about addressing the problem.
Jacob Varghese
Yeah, I think there are two areas, right? So mainly one is the reporting side. So whether the Spotify or the UGC platform like YouTube and things like that, how they reported, right? That reporting data, the quality, there is probably most likely, know, identifiers for the music or the musical world. How do you, can you rely on those? Whether it's ISRC or ISWC or any of those identified, can they rely on that? Most of the time, you can't. When you take this, you can't. So then you need to look at other things.
Jakob
You can't.
Jacob Varghese
It's like I said, these are mostly blanket license. So it's not, you're not sending the metadata to these companies or distributors or PSPs to really report back. It's the most of the companies like Apple Music, you get very good data. Spotify probably get very good data, but in YouTube, you probably don't get it right. But in public performance, you may not get the right data. The radio stations, the broadcasting stations, you may not get the right data. So there a lot of different areas. It depends on where you get the data. It's, know, confidence is different, right? The quality of data confidence is different. So that is there. That is a challenge, And then all these right owners and where they keep this data and manage the data, Whether it's a PRO or MLCs or, you know, CMOs or the publishing companies, when they collect this data, do they have all the information, right? All the information in the sense, do they have all the identification information? Do they have all the rights, you know, splits correctly? The percentage, how much, you know, For example, let's say one musical work will have 10 different writers and what is the percentage of their ownership? Do they have the right ownership? If you don't have the ownership and when you send this data between these agencies and there is a conflict in the ownership, then they will hold that money on that track because they don't know
Jakob
Mm.
Jacob Varghese
Whom to give that money. That's a real problem. That's a conflict in the track itself, the musical work itself. And similarly, in many times, like performance rights, like in the neighboring rights side, we need to identify all the artists, all the main artists, all the non-featured artists, record company, right? How do we do we have all the information to identify them? Most of the time we don't. Like when you don't have it, you can't distribute that money. So it's the lack of data, lack of incorrect data or know lack of data, incorrect data, insufficient identification data. So the lot of issues from the management of the site. The reporting side is another issue like I said that there is there's a various sources will have a different level of completeness and reliability in the data. So in order to in order to effectively collect and distribute you need to do this matching right. You need to match not only the match but also find all the right holders correctly. Otherwise, you won't be able to distribute the money. So that is typically, that is what you mentioned before, the black box money. So what is a black box money? Black box money is that you receive the money as a PRO or CMO, a licensing company, but you are not able to distribute that money. You don't know whom to give.
Jakob
Mm.
Jacob Varghese
You don't know how to split those money to different right holders. Or you have some kind of conflict in mandate. Like, who can distribute that money? Who can give that money? So there are lot of different aspects there that can affect the black box.
Jakob
Hmm.
Jacob Varghese
That money sits there in the bar, in that particular fund or wherever. And it depends on specific territories, regulations. They might keep it for three years. They might keep it for one year or whatever. And they just spend it on other things. So that is one of the problems in the industry. how...
Jakob
Mm.
Jacob Varghese
Efficiently we can show all those things and a lot of everyone in the supply chain has some kind of responsibility including the artist, including the independent producer label. So we can give the proper data, properly do the documentation, properly transmit that, that will help them. That will help the whole crisis.
Jakob
Hmm. It's very overwhelming to think about all these things. And I know music quite well, and I also know a good deal about the black box. And even for me, it's so complex to comprehend all the hurdles that there is here. Because as you say it is, it's not just technology, it's different ways of working. It's different approaches, different payment systems, different registrations. And for me, it kind of seems like a problem that almost feels impossible to solve. Do you believe it's possible to solve on a macro level? Like everyone gets a hold of this or is it company by company and system by system? Is that the approach to dude?
Jacob Varghese
I think the approach is not the approach. Think there will be some kind of disruption in how you consume and license licensing and consumption of it, right? Because in order for you to a to play a song you need right now in US, you need to get license from the PROs, need to get license from producers, agencies. So you have different mechanical licensing companies. You have multiple places you need to get license to play. So it need to have a very simple solution for the licensing so that as a consumer, should be able to do that. I should be able to get that song and play very easily. And how you distribute the money, it shouldn't be complex to have these four different rules. These are the list of right holders, whether it's a producer or the writers or the artist or session musicians. So this is the person that should be very simple to do the calculation and give it away. Right. So, but I think, you know, when you, when you think about it, could be some solution, there could be some platform that can completely take, you know, content from the creators and directly give it to the, you know,
Jakob
Yeah.
Jacob Varghese
Consumers and a direct licensing process. If the policy is not changed, if the regulations are not changed, then there will be some disruption can happen and that can simplify it. But, you know, there are different entities within these ecosystems and they have very different interests in that. So it is going to be difficult, but I think it might happen.
Jakob
Yeah. And are you talking about, like you believe if there's a disruption with, this come from a company or a movement, a technology, a policy? What do you see it most likely coming from?
Jacob Varghese
I think it's a combination of it. You take an example of the AI today, think in Europe, I think you can give consent to give these AI companies to use the content. Or you can say, don't give consent to this AI company to use my content. So that's an individual level of consent, right, for each by track or each, you know. There is no business, right? So it's not going to be a, it's not driven by, that policy is not driven by actual business. The business is going to come up with something different. Because nobody is going to, an AI company is not going to change the model every time when they get a consent from a producer label or a right. So it's natural that how the technology works, how the business is evolved, right? And the policy needs to change. And I think it's a combination of the technology, how the new business model and the policy.
Jakob
Thank you for that answer, but to some degree it also makes it even more mysterious for me because it seems like there's a lot of things that needs to perfectly align and needs to be some players that are patiently placed and progressive in a space where things will happen at one point. But where are you as a company? Do you believe you will be a part of that journey? Maybe that's not the right question, but that's of course very interesting. But can you tell me about where your company is right now? How many employees do you have? Where do see yourself going and growing in the future?
Jacob Varghese
So we are still a small company and we are told people right now, told employees, and we have an office in India, so offshore. Where we are going is we want to make sure that right now there isn't a problem here, like there's a data problem. And we want to solve, we want to help that, we want to help know, increase efficiency, accuracy, and transparency. That's in plus that. So wherever we can we can do that and wherever we can help, you know, create that accessibility to the technology and research to solve some problems in the industry, that is where we want to be. And we definitely want to be part of that journey, part of the transition from
Jakob
Mm.
Jacob Varghese
Today to tomorrow. So we are continuously looking for technology and the problems in the industry and how we can solve those problems in a cost-effective way, make commercial sense for the current time. We don't think that it's going to happen in one day, it's going to happen in one year.
Jakob
Hmm.
Jacob Varghese
So it's going to take some time. But now there is a problem and we have a solution.
Jakob
Amazing. I really believe that the timelines are super important for music tech companies. Me and my team have been doing a lot of research on music technology. And one of the things we see that there's sort of two categories of companies in music tech companies that does well. One are small, fast moving companies that sort of don't get a lot of traction before they get bought. Like a part of the consolidation or a part of like a whole ecosystem that want that specific technology that needs to have done enough and proven enough for them to be attractive to buy. But that's one category. Then there's one category that is normally not working. And that is the traditional startup journey is like, you know, five to seven years grow really fast, big and get bought from, you know, your revenue or market size potential. That rarely happens. And then there's the category that I also see a lot of success in music tech and that is the longing. That is solving one specific niche problem over a long time, becoming experts, and just diving, diving really deep. And that's basically what I hear you guys are saying. And according to sort of my investment thesis or understanding of it, those two categories of music tech companies are the sensible ones if you want to have success. So Play short fast, get acquired at a low amount, or go for the long run. And are you guys expecting that there's going to be some sort of, again, technology, a policy wave that somehow will make your business grow much faster? Or what is your sort of anticipation of the growth of your company in the future?
Jacob Varghese
So it's very difficult to answer that. The market is changing. I think we are going in that flow right now. Today, there lot of issues in each of these segments, especially just to manage the metadata and manage the assets, consolidating everything, having a good single view of it. And every segment, every customer has a very unique problems also. So how do we do that with a one fit all solution? So we don't want to do it. So that's how we want to be a very configurable, very niche, agile platform where we can plug and play with very specific problem, the modules and solve your problem. So I think we are looking forward to the transition and there will be a transition. And in that part, the data metadata will be a key part of it. And there are tools required, there are efficiency we can create and provide. I think that's important for us and that's where we think the future of our company is going to be helping that transition and building those tools, building those capabilities.
Jakob
I've had the luxury of, you know, speaking with more than 100 people now on this podcast and one of the things that I've come to believe in very much in music technology is the approach that you're having, which is go as deep as you can, be as customizable as you can, like solve a problem as specifically as you can is the viable way to run a business in this industry. And I think you guys are probably never going to run out of problems to solve in this space. It's going to be a long time, which is also really, really interesting to sort of see how to build a company on that. And because what is your end goal as a founder? Like, is it just, you know, continue running a good business to solve problems or are you looking to, there needs to be an exit at one point or like, what do you have in mind when you're growing a business from a standpoint of...
Jacob Varghese
You
Jakob
Do you need to cash out at one point?
Jacob Varghese
Eventually, everyone needs to do something different. I think there will be a point. But today, think at least for a foreseeable future, I think we are looking to solve problems and build this tool in a much bigger level. And it can serve in multiple industries. For example, we can go into the... healthcare. You can go into marketing, you can go into things. There a lot of problems everywhere, know, specifically, you know, doing the metadata related, you know, how do you identify things? How do you identify frauds? How do you identify, you know, how do you find your healthcare provider exactly? You have a lot of technology tools available right now. You can utilize whatever we are building here in different segments. But we don't want to go in right now, but I think because we need to have knowledge, we need to have domain, especially experience in these areas. Right now, there's things what we are building. We have a conscious
Jakob
Hmm.
Jacob Varghese
It to build it to scale. What that means is that it's not just for the music industry. It should be able to do that for very different use cases in the future. That is what we are looking for. That's how, know, that's our goal is, like as a product development.
Jakob
Amazing, Jacob. It's been really, really interesting having a different kind of founder conversation. You're working with a part of technology that I'm becoming more interested in. I think it's really interesting to use technology to solve technology problems. And I think we're going to see a lot more of that with sort of technology coming in the future and models becoming stronger and stronger. I'm always on this pendulum of being hopeful and...
Jacob Varghese
Thank
Jakob
and very negative towards the future of music. But I think it's particularly within your field and how you use AI or ML, whatever you want to call it, I think is very, very interesting for me to reflect upon that. Okay, there might also come much more issues with metadata as sort of AI generated production is becoming more evident in mainstream media and how to track rights there. But again, on the flip side, you can also use technology to help alleviate those pains. So it's very, very interesting to see what's happens in this space. And I'm very happy that there are people in there that's been trying to solve this for a while and have a sort of an idea of how to go about it. Jacob, it's been a pleasure talking to you and hear about your company. And I'm very, very excited to follow your company in this space in the future.
Jacob Varghese
Thank you. Thank you so much for having me.
Jakob
Thank you. Perfect.
