25. Challenge Accepted. with Harini Janakiraman

Harini Janakiraman



Piotr Karwatka: [00:00:00] Today my guest Harini Janakiraman, global Head of Technology and Associate Partner at Antler, Early Stage VC. Her professional journey started in Austin, Texas, then she moved to New York to work at BlackRock and building the Investment Data Platform. Maybe the story so far will be interesting enough, but it didn't end up here. Now Harini lives in Sydney, leading the Technology at Antler. And I'm super excited to ask her about the firm, how it works, how they harness data to have the portfolio companies, to find the product market fit. Hi Harini, thank you for accepting my invitation.

Harini Janakiraman:  Thank you excited to be here and love to talk to you about all the things you mentioned.

Piotr Karwatka:  [00:01:21] Awesome. So let's start with the beginnings of your career, how it all started.

Harini Janakiraman: I'm totally from an engineering background. So I did my Masters in UT, Austin. And then that's where my career started. I did a bunch of things in Austin was teaching assistant for algorithms, was in Intel as software engineer. But then I moved to New York to join BlackRock. In case you didn't know, BlackRock is the largest Asset Management firm in the world. And there I got into an exciting role where it was in different teams, across different parts of the organization for the first two years. In various Software Engineering, Distributed Computing type of roles, and they have like a really exciting platform there that makes a data-driven investment platform across various asset classes. So that's where I was for about 10 years of my career. Growing up in that career ladder to be VP of Data Engineering, building all the Data Engineering infrastructure for BlackRock across New York and Asia Pacific. So that's kind of my kinda career defining role. I would say where I learnt a lot.

[00:02:25] And with that role, I moved to Asia Pacific group lead for Data Engineering in Sydney. There I kind of continued on building the team here in Sydney, Singapore, and India. And that's when I jumped on board and joined Antler because I have always been building startups as a side hustle and things like that, and that each kind of translated into helping early stage founders and also work with early-stage founders here.

Piotr Karwatka:  [00:02:51] That's amazing experience before we get more into the Antler, your current endeavour, I'm wondering about this about your role at BlackRock. I mean, data driven investing since like for some sounds like rocket science, for some sounds like magic. Can you tell us more about how it works? And what was your role like how, how you build this platform?

Harini Janakiraman: We had a really interesting platform where they use a lot of Open Source Technologies to derive a lot of Data Sources from either let's say Bloomberg, Morningstar and Reuters, and other data sources, but essentially it goes into an ETL type of fashion with either real-time or streaming data.

[00:03:32]So we use things like Spark budget, Storm, and CAFCA for queuing and other things. So essentially that, yeah, that's kind of drives into various apps. So Aladdin is a platform which has various apps embedded in it. Things like a Risk Assessment simulation and Portfolio Management public trading you know, using broker audit placement systems and things like that. And all this data kind of feeds into that. So the data engineering team essentially is like the central core that feeds data to different applications, and those applications are used by individual teams, whether it's portfolio managers, risk assessment teams, or like actual trading teams. So they cannot use these applications to execute on those traits.

Piotr Karwatka:  Wow. That sounds very interesting for different reasons. I mean, for instance, scalability. You mentioned tools like CAFCA and others, so super exciting. So, does it work like you are gathering signals from all different sources? In regards to different models for different assets categories? I mean, probably there's different models for stock, different model for real estate. Different model for materials right? Does it work this way?

Harini Janakiraman:  Yes. Exactly. So most of the asset classes where obviously like in public sector, but yeah, different asset classes by different kind of sector types, as well as trading frequencies. Like some, some pricing data is very like high-frequency, whereas some of these Passive like equity non-equity type of assets are very like not changing that frequently. So yeah, different asset classes different frequency of data updates. Those kinds of data sets are pretty interesting.

[00:05:17] And given that BlackRock actually has around, I think now it just keeps increasing. It's like 7.5 trillion USD under assets, under management. So you can imagine like the scale of these, the scale is really massive.

Piotr Karwatka:  That's the second thing I was wondering about is whether you have these tools for helping making the decisions, or maybe even I can imagine sometimes making the decisions on their own, I mean like high frequency trading or something like this in those cases? Where are you doing kind of benchmarking, like for example decisions made with the store versus decision on the same classes or stocks, or maybe even the same assets without this tooling to measure how it performed, because I'm always wondering how you how you task those kinds of tools and how can you improve the quality of the insights they provide.

Harini Janakiraman:  [00:06:15] Yeah. So there is definitely like the layer of benchmarking on top of it. There's also a layer of compliance on top of it. So that say someone is making a huge mistake just by clicking that button. So there's like a double verification. There's a compliance checklist. So that's a very complex system in which trades are going through and maintained in a particular portfolio strategy. So there's no mistakes that can be human error during, so those kinds of things are eliminated. So a lot of portfolio management strategy driven decision making in that sense with a layer of data on top that enables portfolio managers to execute. And also obviously things like Bloomberg data or something like that helps with, you know, latest trends and news and things like that. So it's an amalgamation of many different things depending on the use case of the user.

Piotr Karwatka: [00:07:13]  Gotcha. No, it makes perfect sense. That that's really interesting. I can probably ask you 20 minutes more just about that, but let's let's move forward. You joined Antler, you said, because you used to work in this kind of startup mode. Right. And it was, it was compelling for you. Tell us more about Antler. What makes it what makes it special? What was the USP for this accelerator or found.

Harini Janakiraman:  [00:07:36] So the reason I joined Antler is somewhat very interesting because say in BlackRock, even though it's a large organization, they were operating in somewhat like a startup mode where individual smaller teams were having a autonomy to execute. And we're also like all engineers do buidling things on the side, experimenting and so on.

[00:07:58] So that kind of really made me want to jump from a large organization to a startup space. And I felt Antler is like the perfect place because as a Venture Capital firm it is actually enabling a lot of exceptional founders at that very early stage wher, we have different ways we invest in portfolio companies at Antler.

[00:08:18] So main channel for our deal flow is founders who want to build companies, but either don't have specific, co-founders who are completely new skills to them. This is a great platform for them to find their co-founder as well as, you know, validate their idea further and hone it further. So we have, Antler has about 12 locations globally now, and it's expanding. We have re location newly added in Berlin as well, where it's a central location for central Europe. So the plan, the way it works is every year, each location has two cohorts. And in each of those cohorts, we get about like, you know, 70 to 80 founders from three different kinds of mixes technical founders, business founders. So people that have been previously exited entrepreneurs or, you know, people who have, you know, been in the consulting and operations and that kind of backbone, and also like a mix of a good balance of people from who are to being domain experts. It's specific skillset in a specific industry.

[00:09:17] So when, what we have seen is when these kind of groups of people come together I've moved from there kind of regular job to give this a short lot of interesting ideas, evolve people who are enabled to find co-founders with exact complimentary skills. This is a great platform for them to find that because we actually go to the painful process of interviewing from 50,000 plus applications globally to narrow down highly talented and exception founders.

[00:09:43] So these. Founders are pre-vetted in a way. And so it becomes like a great platform for them to find a co-founder narrowed on their idea and kind of further along with the coach from antler B, have a great advisor network. So they kind of accelerate the process of building a company. And then they essentially come to our antlers investment committee for funding at the end of like a 10 week Mark.

[00:10:07] So that's kind of our main channel. But we also actually also take founders in who are already existing as a team and want to come to antler for funding directly as well. Like a typical accelerator would. So we have two ways in which founders can come to handler for.

Piotr Karwatka: [00:10:26] That's super interesting. So what I understood is that. It's kind of the, the best deal flow. And I'm the strongest one is kind of a bootcamp program or accelerating program. So something like YC maybe, but you you're approaching them on, as I understood on the far earlier stage than, than for example YC

Harini Janakiraman:  Yeah, exactly. So a little bit earlier earlier stage than YC, because actually some of our companies do end up in YC itself afterwards.

[00:10:57] So Our hypothesis is that we coming in earlier, which is like at a founders finding co-founder stage, but at the same time we are helping them in tons of resources to enable them to, you know, not waste a lot of time in doing it themselves kind of approach, but like providing it all types of resources to help them execute faster.

Piotr Karwatka:  [00:11:18] Gotcha. And you also said that. Basically that two cohorts of the founders joining Antler are those with technical skills. And then some guys with business vision. My question was what, what pattern you see more often? Like folks joining would you would grade great idea. You assess this idea where high. You score it very high and then help them find the technical co-founders because it's very often the case that guys would business vision fail to find that the tech founders, and this is where Antler can have probably a lot.  Or maybe it's a, rather than you are. Having those faults that love to do something and you inspire them with, with some ideas you you've, you find very high potential, which one is more successful

Harini Janakiraman:  [00:12:13] It's been a really a mix of different things.  So we have seen founders who either come in with a specific idea. Like for example, we have had scenarios where there are domain experts or PhDs. Like we have someone who had an IP in computer vision and really wanted to commercialize that, wanted to find someone who has that business acumen to take it to the market and grow it in the right kind of audience.

[00:12:34] And that's kind of a pairing that has really worked well. We also have scenarios where people come into the cohort. Have a specific problem that they want to solve that they have been thinking about for a while, but not necessarily know exactly how to execute on or like what is the right way to solve that, they kind of brainstorm do a lot of design sprints, hackathon style, a quick sprints early in the cohort where they're able to then figure out what's the best way they do a ton of validation and market research, and then come up with an idea on how best to solve that problem. We also had scenarios where. People are coming in with because they specifically worked on an industry like, you know, some industry like construction or insurance and they've actually, while they are working there, they've identified something and they're really frustrated and that that's not being sold yet.

[00:13:21] And they actually come to the Antler program to solve that specifically. So yeah, we have seen various types of ways. Ideas emerge. And what's interesting is that because we invest in a wide range of industries and sectors, tech startups across a range of things, we are not focused on a specific thing. Right. So it'd be, I've seen like tons of interesting startups emerge over time.

Piotr Karwatka:  [00:11:18] Gotcha. That's that's super interesting. And sounds like a perfect place to incubate and grow new projects. So next question would be something you probably can answer based on both experience and data you process on your daily basis.

The question is how to find the right Technical Founder?

Harini Janakiraman:  [00:12:13] Yeah. That's a very good question. And it's something we constantly see. So. We always encourage people to have obviously the right people to be solving the right problem. Huh.

Piotr Karwatka:  What does it mean? What does it mean? The right people serving the right problem. Like I can understand the right problem, but the right people are passionate about this problem. Yeah.

Harini Janakiraman:  Passionate about the problem at the same time, have some experience that cannot translate into the suit. So for example, a technical founder who has been building a lot of the front end apps and like consumer facing products.

[00:14:38]Can pretty much fit into any, you know, B to C type of business, or even like a SAS company, right? Like it doesn't have to be they, they're not industry focused in a way, but they can actually have a wide variety of experiences. But whereas they suppose someone is. You know, being an expert in a blockchain space or something specific in, you know, IOT or something like that, they are somewhat fixed or like they have the specific focus area that they can work on. So depending on their past experience, we kind of encourage people to translate that into something rather than trying to, or fit into something they have absolutely no experience in and that kind of thing. Generally it helps them because they are having bringing a depth of knowledge in something that can be translated faster in this kind of a space. So obviously passion for solving the problem is obviously number one, because that's, what's going to make them last a stick on that problem for a long time, knowing, well, that startup journey for being successful is you know, takes many years. So yeah, that's number one. But apart from that, we have tons of other things like kind of checks and balances in place that allows the right people to be solving the right problem. And also mainly the co-founder relationships and other things. So we actually have like a really interesting questionnaire that be shared with the founders who take it and kind of ask all those difficult questions upfront so that they're kind of well balanced and the right vision. And if they're aligning right ahead of time, rather than, you know, going through this and then figuring out things are not working out. So we kind of try to provide as much framework and systems in place so that they can they're well-placed to find the right founder. Gotcha.

Piotr Karwatka:  [00:16:17] Antler is investing into product builders. So the folks that just do whatever it takes to make the company successful. So in early stage, when do you need to specialize when building a startup?

Harini Janakiraman:  [00:16:46] Yeah. So in that kind of very early stage, you know, in the pre-seed kind of rounds there's probably like two founders or three founders and the founding team is pretty much doing everything themselves. And we can say I mean hire the right time. Right? So you don't want to just hire for the sake of it. Just make sure you kind of going after the right problem, right. Validation. And you can start building the early versions of the product yourself. So that you can, you know, we don't encourage people to, you know, go to dev shops and build a product out because you never know, you continuously almost experimenting at this stage and reading and learning.

[00:17:11] So we encourage the founding team to do that. And then once they can start seeing that traction is when they can start hiring more team to kind of support that business in a way. So forming teams Comes in possibly at like in the next rounds when they're fundraising and so on. But at the early stage, when they're just building the product themselves only if they really need that support, they kind of start hiring at that point.

Piotr Karwatka:  [00:17:37] Gotcha. That makes perfect sense to be agile and open to the testing. So investing at such a early stage for me it sounds like super risky business to be perfectly honest. I think that most of the companies are failing it's normal startup thing, right. It's nothing very special. But do you make use of your former experience at BlackRock or, or other positions? Where are you working with big data to help mitigate this risk somehow? And if so, how would work?

Harini Janakiraman:  I mean, definitely, investing in early stage startups is risky. There's no doubt about that, but we try to do different things in our strategy as a way to de-risk this and, and enable that as much as possible. So a couple of ways we do that. Unlike a typical VC that  possibly meets the founders for a couple of meetings and makes a decision. We actually work with the founders for an extended period of time or like 10 weeks and each outcome. Yeah, exactly. So bootcamp, because actually founders actually working there full-time and they're giving it their a hundred percent.

[00:18:43]Each of these teams are assigned a coach who works really closely with them. We bring in advisors who are experts of that specific problem that they're solving as well to give them directed, focused advice. So this way we actually getting feedback from the coaches from external advisors. Mostly these teams also meet with other VCs in the ecosystem.

[00:19:04] So they can begin to get different viewpoints on what they building ahead of their investment committee. And a lot of these investment committee also has but sitting on our panels. So say for example in New York or like Sydney, or like other places, essentially the investment committee comprises of the local partners, as well as venture capitalists from the ecosystem, as well as some experts.

So we have an in depth due-diligence process that kind of helps in de-risking. And we also want to ensure we are investing in the right companies and right founders, right. So that we are giving the right signals to the founders as well, that we are investing in you. We're taking that huge bet and also like committed to helping support you as, as a part of your journey further as well after the investment.

Piotr Karwatka:  [00:19:50] When we were prepping this interview you told me that you also have a data platform that helps your founders to find the product market fit. Can you say a little bit more on this project?

Harini Janakiraman:  Yeah. So once we invest in the company, we kind of obviously try to support them as much as possible in different ways. And we kind of identify the key areas in which early stage startups kind of need the most help with. One is definitely with next rounds of fundraising. So we have an extensive data platform of investor database that helps them navigate based on sector check size and you know, things like that to identify the key investors who will be interested in the next round.

[00:20:32] And we have a global network that enables us to make those meaningful connections effectively. So we help in that process. We also realize that early stage startups, one of the biggest challenge is when they actually start to hire it's really difficult for them to compete with other big players in the market.

[00:20:49] So if the LinkedIn and other things really challenging, and one of the unique value propositions we have is like, we have a great talent network because you know, 50,000 applications that come in globally, not everybody is you know, going through to the final rounds and making it to the program. So we actually invite exclusively people who go all the way to the partners interview, who are still great candidates but maybe not found the material, but can be great early employees.

[00:21:15] We invite them to the talent network. We also invite people who have gone through the antler program in different locations, but did not get the investment to the talent network as well. So that this network is pretty powerful where our portfolio companies can tap into for their early hires. And that's a great way for you know, other people who have applied also to get exposure, to join a exciting startup at that early stage.

[00:21:40]So that's another way we help them and we also help them with various other things around enabling them find their early customers help them with tons of resources, AWS, Google cloud, and all these kinds of credits. We have a lot of tech resources and various legal and documents that helps them in the contract process and so on.

25. Challenge Accepted. with Harini Janakiraman

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