The Story of Portrait Analytics: Building AI That Institutional Investors Actually Trust

David Plon made his first trade in seventh grade on his dad’s E*Trade account. He bought shares of Apache Oil. That early taste of investing never left him.

In a recent episode of Category Visionaries, David Plon, CEO and Co-Founder of Portrait Analytics, an investment research platform that’s raised $10 million in funding, shared the winding path from childhood stock picker to AI entrepreneur—and revealed an audacious vision for becoming the operating system of the investment world.

The Stanford Epiphany

The real story begins at Stanford Business School between 2015 and 2017. While David had worked at several investment firms in analyst and principal roles, he wasn’t planning to start a company. He was simply curious about an emerging technology that seemed to mirror something he’d observed in great investors.

“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,” David recalls. But it wasn’t the technology itself that captured his attention—it was the pattern.

“I 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,” he explains. Despite having no engineering background, David spent time in independent study teaching himself machine learning. He was poking around, experimenting, waiting for something to click.

Then GPT-3 launched its API.

The Moment of Clarity

David started fine-tuning early versions of GPT-3 on a specific task: analyzing and summarizing earnings calls. The results were startling.

“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,” he says.

This wasn’t abstract potential anymore. This was concrete capability that he could see and measure. “I could imagine so many ways in which it would impact investment research, which is the one I I know super well.”

Finding the Right Co-Founder

David had conviction about the opportunity but knew his limitations. “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.”

This decision shaped everything that followed. “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.”

The early days were about rapid learning. “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.”

David understood that his perspective had limits. “I’m certainly have my own set of biases and whatnot as a potential user of this technology.” So they spent countless hours talking to potential customers, prototyping, iterating, and collecting angel checks to fuel the experimentation.

The Pre-ChatGPT Challenge

Selling AI to institutional investors before ChatGPT made it mainstream meant confronting deep skepticism. The idea that “AI as a thought partner on an investment team was something that felt very far fetched,” David notes.

His industry relationships became crucial bridges. “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,” he explains. But it wasn’t about easy sales—it was about building trust over time through demonstration.

The process was methodical: show early prototypes, demonstrate specific impact on idea generation and context building, convert interest into letters of intent, then deliver a working product. Each step built credibility for the next.

Defining What They Actually Built

When asked about Portrait’s market category, David pauses. “I don’t know. It’s the honest answer.”

He can describe who they serve—people spending time investing in publicly traded companies and doing fundamental research. But from a product perspective, Portrait doesn’t fit neatly into existing boxes.

“I do think we are building a different category of solution,” David explains. The market has copilots like GitHub Copilot and fully automated agents. Portrait is neither. “What Portrait 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.”

The best analogy he’s found? A thought partner. “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. 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.”

The Marketing Philosophy

Portrait’s approach to marketing flows from a contrarian insight about AI positioning. “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,” David says.

The logic is unassailable: “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.”

This leads to a clear positioning principle: “AI is a how, not a what. And I think focusing on the what and the why is how you really stand out.”

Their content strategy exploits a natural advantage. “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.”

David’s favorite outbound tactic shows the strategy in action: “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.” It’s personalization powered by the product itself.

The Audacious Vision

Where is all this heading? David’s answer reveals an ambition that extends far beyond investment research tools.

“Assuming we haven’t hit the singularity,” he begins, then paints a picture of transformation. “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.”

His reference point is revealing: quantitative firms like Renaissance Technologies. “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.”

In quant firms, humans handle strategy creation and data acquisition while models do the thinking and synthesis. “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.”

The conclusion: “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.”

And Portrait’s role? “That’s a little bit how I kind of see this evolving with Portrait being the synthesis engine for public investment firms.”

It’s a vision that transforms Portrait from a research tool into infrastructure—the operating system that powers how institutional investors think, analyze, and make decisions. From a seventh-grader buying Apache Oil to building the potential brain of the investment industry, David’s story is still being written.