The following interview is a conversation we had with David Plon, CEO & Co-Founder of Portrait Analytics, on our podcast Category Visionaries. You can view the full episode here: $10M Raised to Build an AI-Powered Thought Partner for Institutional Investors

Brett
Welcome to Category Visionaries, the show dedicated to exploring exciting visions for the future from the founders who are on the front lines building it. In each episode we’ll speak with a visionary Founder who’s building a new category or reimagining an existing one. We’ll learn about the problem they solve, how their technology works, and unpack their vision for the future. I’m your host, Brett Stapper, CEO of Front Lines Media. Now let’s dive right into today’s episode. Hey everyone. Welcome back to Category Visionaries. Today we’re speaking with David Plunt, CEO and Co-Founder of Portrait Analytics, an investment research platform that’s raised 10 million in funding. David, how are you? 

David Plon
Doing great. Really excited to be in the conversation. How are you? 

Brett
I’m doing great and I’m super excited as well. So let’s go ahead and just start off with a high level overview. What does Portrait Analytics do? 

David Plon
We are building a thought partner for institutional investors that really can add value throughout the investment research process, particularly on tasks that take humans a lot of time today. So specifically, our platform helps investors identify new investment ideas that they’re a nuanced qualitative and quantitative criteria for what they’re looking for. We help investors rapidly build context on new investment ideas. We help them stay aware of all the important data points that could be impacting a position in our portfolio. So that’s what we do with Portrait. 

Brett
Take us back to the founding story. How’d you end up in this world? 

David Plon
Yeah, so look, I’m someone who loves investing and still I’m a lifelong investor. I did my first traded my dad’s E trade account when I was in seventh grade. I bought shares of Apache Oil. I remember very clearly and throughout my career worked at a few different investment firms in various kind of analysts or principal roles. But really the aha moment here was when I was at Stanford Business School and this was 2015 to 2017. So deep learning is kind of getting into the Zeitgeist and the Transformer paper which underlies a lot of the foundation behind language models today had just come out and I’m certainly not an engineer by background, but was just very curious about the idea of large scale pattern matching that machines were doing being similar to the large scale pattern matching that great human investors do. 

David Plon
So when I was at Stanford I spent some time taking independent study and teaching myself enough to be dangerous with respect to using machine learning models and just continued to kind of poke around with these models until GPT3 came out and the API and once I got to a point where I was fine tuning very early versions of GPT3 to recreate, analyzing or summarizing earnings calls to the point where I couldn’t tell the difference between something the model was writing and what I was writing, it became really clear to me that this technology would be wildly impactful to any knowledge based domain. And I could imagine so many ways in which it would impact investment research, which is the one I I know super well. 

Brett
You have this idea, what do you do next? What do the first three to six months look like? 

David Plon
A lot of the first to three to six months are experimenting because it was such a novel technology at the time I’d say actually the first thing I did was find a tremendous Co-Founder who is a deep technical expert and just a really strong full stack engineer. And that was super important because it gave me the capability to experiment and iterate quickly to start testing out some of these concepts I had at that point. 

David Plon
A lot of it really was some combination of experimentation, both like between my Co-Founder and I, as well as with very early trusted kind of design partners and just understanding the capabilities of these models where they can be useful and how they could impact research workflows beyond just my own experience since I’m certainly have my own set of biases and whatnot as a potential user of this technology. And so yeah, a lot of what were doing there was just speaking with these potential customers, iterating, experimenting, prototyping and ultimately starting to collect some angel checks to kind of get us off the ground and ultimately lead to a pre seed. 

Brett
Let’s talk about that first customer that you’re able to get across the line. Take us back to that first deal. How’d you make it happen the very. 

David Plon
First customer got across the line? Well, first I was working with a bit of an advantage in that I had been in the industry for a bit, working at a few different funds. So I had some relationships with people that trusted me and I certainly leveraged those relationships early on, which was important because this was pre chat GPT. So AI as a thought partner on an investment team was something that felt very far fetched. And so a lot of my kind of credibility in working and having worked with some of these folks or been colleagues with them helped me like kind of bridge that gap of uncertainty and a commercial relationship in a way that kind of would build trust over time. 

David Plon
So a lot of it was really early prototype demonstrations of what we could build, examples of like how that could impact specific aspects of the idea generation and context building process and yeah, turning those into initially kind of letter of intents for when we had a working MVP and then converting those letter of intents once were able to start, you know, delivering a real product to some of these early users. 

Brett
You know, I started a new startup in January and we’re at that point now where we’ve sold to all of our friends, everyone in my network, we’ve sold to all of them. And now we’re at the stage of if we’re selling to people we don’t know, it’s a very different pitch, it’s a very different go to market motion. What’s that been like for you as you started to sell outside of your network and to people that you didn’t have an existing relationship with. 

David Plon
Yeah, and we’re, you know, fortunate in that we’re just been doing that quite recently. I think the most important aspect of it, especially as an early stage company with a novel technology that is rapidly changing, is to have a fairly prescriptive definition of who the ICP and early adopter is. And so a lot of our messaging and a lot of the people we’ve been letting in off our wait list and connecting with have been folks who look a lot like our, the friends or net connections that we have. Right. And so we’ve over time built a fairly strong understanding of what is going to resonate with these users. 

David Plon
What are the really burning important problems and jobs to be done that they have and how can we make sure that our technology and solutions are 10xing, whatever else is out there in terms of solving those. And so because we’ve worked so closely with those initial partners to build conviction on each of those points, both what problems we should be solving and how we can be solving them from both a technology and a UX perspective, it sets us up really well to tell that same story with incremental, full incremental customers and look like we’re a little bit lucky in that. Generative AI is quite topical and a lot of investment firms are quite focused on it. So we’ve gotten a lot of inbound. 

David Plon
But again, I think we’ve been very, I’d say specific around who we end up speaking and working with because we want to make sure that we’re continuing to build and sell into folks where we believe there’s going to be a natural fit for a product like this. 

Brett
You mentioned early adopters there. How do you find early adopters? That’s something that I think anyone who’s building novel technology struggles with is, you know, most people don’t have that listed on their LinkedIn that, hey, I’m an early adopter. How do you find people? How do you make sure that they are ready and open to new technologies like this? 

David Plon
So there’s no magic formula, of course, but I think there’s a few things you can kind of point to. The first is, especially when the technology is quite immature, is when you’re in conversations with somebody, an initial qualification type conversation, you can tell quite quickly based on their tone and the nature of their questions that they’re asking and the reactions as they go through the demo, whether this is someone who’s leaning into this and is seeing the positive side of, or maybe the possibility and the potential of what the product’s going to do versus someone who is, you know, going to be quite skeptical. I think there’s just a little bit of that you pick up quite early on when having like an initial qualification conversation. But really the biggest tell is actually just the revealed preferences of what someone’s doing. 

David Plon
So for instance, the customers I love meeting are those who have hacked together their own basic implementation of using something like anthropic projects or GPTs on OpenAI with prompts they’ve created to try to recreate some of the very basic aspects of what we’re doing in terms of leveraging language models for investment research. I think there’s no better telling someone that cares enough that they’ve invested time and money into actually trying to do some of this themselves. And at that point, your solution is so much more relevant for them because they’ve run into the friction points that having a fully built, purpose built product for this job gets rid of. So that’s a little bit how we’ve kind of approached that. 

Brett
If we just zoom out a little bit, let’s imagine that we’re doing those, you know, like street call out videos that you see on Instagram and TikTok where you just, you know, stand outside and like, ask people quick questions. 

Brett
If were to ask like a hundred of your ICPs, how would they summarize the state of AI and their general sentiment towards AI right now? 

David Plon
So it took me a second there because sadly my TikTok and Instagram consumption these days has taken a dip. It’s a lot more machine learning papers than anything else, unfortunately. For better or worse, I’m not much fun at a cocktail party these days. But I guess the question being if there are a hundred both of our ICPs and yes, I’m not the state of AI. I think what they would broadly say is if ChatGPT, Claude, Gemini pools out of the box are at a point where if you give them context, so filings, documents, news, things like that and ask them to reason over it will do an adequate job for very basic question answering over document sources. 

David Plon
I think where especially our ICPs who are actually using our product get most excited is when they think about scaling that shallow reasoning ability over a very large surface area. So for instance, I think where there’s a lot of skepticism with respect to something like hallucination is financial modeling, right? Like when you’re building a model in Excel as analyst, there is just so much way, there’s so much. I’d say the stakes are just so high with respect to the importance of accuracy and it’s really hard to audit and spot mistakes. So you end up a bit with a self driving car problem where 99% accurate is actually 0% useful. But I think where our ICPs are more favorable from like a, you know, capability perspective of AI is something where it’s more creative in nature. So idea generation, right? 

David Plon
Or spotting of data points that could be relevant things where, like tests where you’d be okay giving it to a junior analyst or a team of junior analysts because being accurate at any one data point isn’t what makes or breaks the value proposition. If I can serve you 10 potential investment ideas that fit a really nuanced way until into the types of investments you like to make and five of them are potentially relevant. I mean that’s incredibly value added for a user. And I think to the extent that AI can lean into those types of use cases, people are really excited about it. 

Brett
How do you stand out? How do you rise above all of the noise? I’ve seen this in every single industry right now. Every company has AI, everyone’s talking about AI. It’s a lot of buzz, a lot of hype. What are you doing to really stand out and separate yourself from all of that noise that’s out there in the market. 

David Plon
I think the biggest thing you can do is focus on the user to the point where the fact that your product is AI isn’t actually part of the messaging. 

Brett
Right. 

David Plon
Like, it’s a little bit like what the user really cares about is does this product meaningfully improve upon solving a job to be done that I have. Like our user shouldn’t care whether Portrait is being powered by an AI or powered by a hundred analysts who are like constantly slamming on their keyboard to respond to our users. Right. Like, ultimately what the user should get is really value additive insights and research outputs. And so to the extent possible, like, look, I’m sure there is branding cachet and value from just a relevance of SEO perspective of leaning into the AI aspect of things. But AI is a how, not a what. And I think focusing on the what and the why is how you really stand out. 

David Plon
And in particular for us, that means being really prescriptive around who we want to serve and what are the specific research tasks or jobs to be done that we can do better than any other solution out there, human or otherwise. 

Brett
Jobs be done is one of those things that I see a lot of people talk about, but I think there’s some confusion about how to actually make it work in practice. Can you take us through how you approach jobs to be done, the framework that you followed and there’s that whole process kind of behind the scenes. 

David Plon
Sure. I’m a longtime fan of Clay Christensen and all his books and speeches. So that is one of the many concepts I borrow from him. And I’m sure I’m misusing it in some way, but the way I think about it is, and this is I’m sure paraphrasing his definition, but effectively, what is some sort of progress that somebody wants to make in a given way? And I think it’s important that jobs we’ve done is different than a problem. For instance, let’s go back to ChatGPT. I don’t think pre ChatGPT anyone would have thought that Google was a problem they had. Right? Google is the best search engine out there. But really what ChatGPT did, among many other things, was in a lot of ways it solved the job to be done of getting information and answers faster. 

David Plon
Or I guess that is a job to be done right, like getting information that is highly relevant to what you are looking for. And actually in a lot of instances, either whether it starts GPT or Perplexity or something else, like those tools do that job to be done better than Google and so as a result they’ve become quite popular. And so within the context of portrait, what we really did was take a look at the investment research process as I understood it and people similar to me and broke down each step. What is analyst trying to do here? Like what are they essentially trying to learn or what questions are they trying to answer? 

David Plon
And based on that, how can we build a solution both from the model level to the backend layer to the UX layer that really nails that specific job to be done? So it could be like a classic job to be done is, you know, finding what the key tension points or debates are around a stock. So something we spend a lot of time on is how do you discover what those key debates are? And that is an example of like a research workflow that our system is really good at. So that’s a little bit how we’ve kind of thought about that. 

Brett
What about your market category? When I did the introduction, I introduced you as an investment research platform. After hearing you talk, I’m questioning myself. I don’t know if that’s the right category for you. What’s the market category? 

David Plon
I don’t know. It’s the honest answer, I think. Look like we are serving. You come at us from a few different ways. If you want to talk about, define it from the perspective of who we’re serving. We’re serving folks who spend a lot of time today investing in publicly traded companies and doing fundamental research around those companies. That being said, ultimately I do think we are building a different category of solution if you want to define it from a product perspective. And I think broadly speaking today there are a bit of a bifurcation. There are AI products out there that tend to be co pilots. So something like GitHub, Copilot or whatever is like a classic example of that where it’s like, you know, something that is auto completing for you or a chat bot. 

David Plon
And then there are also products out there that are very much agents doing specific kind of tasks in a fully automated way. And I try to think about like what Poetry is doing is a bit of it’s a different category and more of a synthesis engine that both is doing a lot of challenging research tasks at a very broad scale, but it’s doing so in a way that deeply integrates within existing research workflows. And so like I don’t have a great name for it, I probably should come up with one. And the best I’ve thought of is a thought partner, right? Like if someone was on your team as an investor. You are not going to sit next to them and talk to them every single day about every piece of work that they’re doing. 

David Plon
But they’re also not going to be like in a silo just doing work all by themselves with no interaction with the team. And that’s kind of how we think about what a thought partner is on an investment team.
Brett
I like to think about categories about the line item. So I’m guessing that there’s no one who’s sitting there thinking, okay, for 20, 25, need to go out and find a thought partner. What are you doing to, you know, evangelize this idea so that eventually people do say that’s a line item that they need to fill. 

David Plon
Ultimately there aren’t people going out there and saying, I need a thought partner. There are absolutely firms going out there saying, I need to hire more junior analysts. I think really the goal of what we’re trying to do is position Portrait as, hey, there’s not a difference here. And in fact, while as a junior analyst, Portrait won’t be able to do a lot of the deep research like tasks that require interviewing people, for instance, or financial modeling in a really detailed way, but Portrait can be a very useful member of your team that massively accelerates the speed and depth of idea generation, context building and industry level context maintaining. 

David Plon
And so from that sense, when we position this as someone you would hire onto your investment team, that tends to really resonate because like especially in a field like this where ultimately for an investment firm, their main asset is the people, right? They have IP and humans. And so it very much resonates like anything that can join the investment team and immediately add leverage to what every other person in that investment organization is doing. Much in the same way a junior analyst would goes a really long way. And I think that messaging has resonated. 

Brett
I see you have a free trial there. What’s the time to value? Can someone get started right away and immediately start saying value or how long does that take? 

David Plon
It’s quite quick and that’s on purpose. And there’s actually a bit of a spectrum throughout the application going from as simple as you type in a ticker and you’re going to get a lot of really Interesting insights all the way into you can go ahead and construct your own fairly complex, you know, templated prompt to run over a set of data sources and recreate specific analyses. So, and what’s been interesting is to kind of observe how different users engage with that functionality. You know, certainly we spend a lot of time making the time and value quite quick with respect to some of the push based content or the pre generated content we develop. 

David Plon
But we also want to give folks the ability to express their kind of their own opinion and preferences and styles with respect to how they use the tooling that we’ve created. So it’s going to be most relevant for their kind of investment research process. 

Brett
What about the marketing strategy? You know, what did it look like early on and then what does it look like today? Maybe just give us some high level overviews. 

David Plon
Yeah, so like I mentioned, we’ve been very fortunate that, you know, we’re kind of building an AI company in a time when there’s a lot of interest and curiosity around AI. And so really early on was just very much, you know, building a website, having folks, I’d say, you know, sending out some thought pieces that got kind of circulated through the industry and then having folks kind of sign up on our wait list and getting some good word of mouth. And yeah, I think we are starting to move into thinking about other ways that we can, you know, spread the word of what Portrait is doing and how it’s quite different than anything else out there through some classic channels. But I think most importantly, where we’ve been getting a lot of leverage is one through some content marketing. 

David Plon
So the nice thing about Portrait, it ultimately is, you know, generating content. Right. And research insights. And so we share those on our LinkedIn page and just get a ton of engagement. And I say the other thing we’ve been doing is again being really intentional about kind of who we reach out to in a highly relevant way. And so a lot of times, like if we’re going to email someone that we think, you know, through a connection or whatever could be a good fit on the platform. You know, one of my favorite things to do is to kind of look at like maybe some of their holdings and send them an insight or some sort of output that’s going to be highly relevant for them. And so yeah, those are some of the things we’ll be doing from kind of a marketing perspective. 

Brett
What you said there reminds me of CB Insights. I don’t know what the strategy looks like today, but I know a few years ago I was listening to a podcast that their Founder did. And you know, it’s a content business, a data business, a research business. And like they took that and they just powered this amazing both content marketing strategy and also PR and media strategy. And they’re able to just get in the media all the time by leveraging their research that attracted more customers. It was just kind of this, you know, flywheel. It just kept bringing more and more people to them. So sounds like something as similar in your case. 

David Plon
Yeah. And look, we’re early in doing this. Right. And I think there’s just so much possibility with their like CB Insights is definitely a company that I look up to. The nice thing is while the technology we’re building is quite young and constantly evolving, there are so many great examples of companies that have figured out fantastic go to market motions. And my goal is to kind of learn from the best of them. And CB Insights definitely kind of stands out as one particular example. 

Brett
What are some other examples? Who are some other companies that you look at, you know, parts of what they’re doing and you think, yeah, I can learn from that or we can apply some of that. 

David Plon
So this is such a standard answer for the industry that I’m in. I guess I’ll give two examples. One is certainly Bloomberg and not so much Bloomberg. It’s obviously total behemoth and there’s not a ton an early stage startup can learn from firm a company that size I think anyways. But what Bloomberg did early on, which was really smart among many things, was develop a news service. That was not a obvious idea when Bloomberg first developed that. But what it did was just provide so much more mind share for Bloomberg kind of throughout the industry. It also improved the content within Bloomberg and from the virtuous cycle it created this ultimately it helped drive Bloomberg to be what it is today. And so I take a lot of inspiration from how Bloomberg kind of built the Bloomberg business over time. Yeah. 

David Plon
And in terms of other marketing, I think I really respect companies that are quite specific in terms of like telling a story that’s going to make it really relevant for you. I think Stripe’s marketing is just fantastic. Right. Like they understood very early on who their user was. It was the developer. Right. It wasn’t the business function and just go into like any of the content they create or platforms or the product pages. It’s just so clear like what the value proposition is and how to get started in a really quick way. And they’re the output they create like the Atlas strategy guides or is content that I Consumed. When I was first kind of ramping up on building Portrait. 

David Plon
And again, like, you know, when we first had to figure out our kind of our payment solution, stripe was the first thing that came to mind because it had built that credibility in my mind. So I think authenticity with respect to outbound and marketing in general is something that I value and I believe our ICP values as well. 

Brett
Final question for you. Since we’re almost up on time, let’s zoom out three to five years into the future. What’s the big picture vision look like here? 

David Plon
Yeah, assuming we haven’t hit the singularity and all of this is. Or not. I think the big picture vision is this. Ultimately, I believe these models within the context of an investment research firm really become like core synthesis engines that are consuming every piece of content that the investment firm has access to and generating synthesis and insights in a really high quality and auditable way. That almost becomes like a bit of the operating system of an investment firm. And one example that I think actually is quite tangible here is looking at quant firms today, Renaissance being the best example. But there are other, you know, Two Sigma and others that exist. And if you think about how those firms work, they have some level of super intelligence within them already. They’re just not language models. Typically they’re more so, you know, predictive statistical models. 

David Plon
And the role of humans in those firms is really around a strategy kind of creation and data acquisition and feeding into these systems. But ultimately a lot of the thinking and synthesis is happening at the model level with oversight from the humans. Historically, that’s only existed in quant firms because of the limitations of what like models could do with very unstructured data. The language models totally turn that upside down in their head. And so I could imagine in five years, most investment firms end up looking something like great quant firms today or some sort of hybrid thereof where the humans are doing things that are uniquely human around kind of novel idea creation and data and research acquisition. So yeah, that’s a little bit how I kind of see this evolving with Portrait being the synthesis engine for public investment firms. 

Brett
Amazing. Love the vision. I really love this conversation. Before we wrap here, if there’s any founders that are listening in that want to follow along with you in your journey. Where should we send them? Where should they go? 

David Plon
Yeah, best place to go probably is to find me on LinkedIn. That’s where I spend the most time. I also spend time on Twitter as well, so you can find me there. Awesome. 

Brett
Thanks so much for taking the time. Really Appreciate it. Thanks, Brett. This episode of Category Visionaries is brought to you by Front Lines Media, Silicon Valley’s leading podcast production studio. If you’re a B2B Founder looking for help launching and growing your own podcast, visit frontlines.io Podcast, and for the latest episode, search for Category Visionaries on your podcast platform of choice. Thanks for listening and we’ll catch you on the next episode.