Infra Play 134: Founder series with Advocacy AI

The value of vertical SaaS in the age of AI

One of the most interesting trends in software today is how many operators from both tech and adjacent industries are jumping into the fray of building their own companies. While I typically focus on analyzing larger organizations based on publicly available information, I think that in order to understand these new startups, a conversational approach matters a lot more. There is less public commentary to work with, and the real story is usually in the founder’s thesis, how they see the market, and why they believe their specific experience qualifies them to build something different. So rather than layering on my own analysis, the goal here is to give the platform to the people building and let readers draw their own conclusions.

This is a conversation with Téo Doremus, co-founder of Advocacy AI, a litigation-focused AI platform launching out of stealth. Téo is a former securities and M&A litigator at Robbins Geller who left Big Law to build what he describes as the intelligence layer for litigation. We covered the product thesis, how it differs from Harvey and Legora, an early customer win, go-to-market strategy, and what it actually costs to leave a legal career behind.

What is Advocacy, and why does it need to exist?

If you say “legal tech” right now, two names dominate the conversation: Harvey and Legora. Both are overwhelming in terms of mindshare, and trying to win attention and distribution today seems incredibly difficult. So the obvious question for any new entrant: why now, and why this?

Téo Doremus, a former securities and M&A litigator at Robbins Geller, has a specific answer. The core thesis behind Advocacy comes from a realization he had while practicing: transactional lawyers and litigators are fundamentally different animals, and no one was building for the latter.

“As a transactional attorney, I used to do many deals, and then I used to litigate many deals, and I realized how different transactional lawyers and litigators were. In terms of writing, in terms of posture, the work is fundamentally different. A transactional lawyer is trying to make something happen. Of course you have some level of adversarial, but it’s a deviation from the main goal. In litigation, the main goal is to actually go and fight. You disagree on everything. And then you disagree on how you disagree about things that you disagree on.”

The implications for building software are significant. In transactional work, timelines are compressed: you negotiate, you do due diligence, you either deal or you don’t. Litigation is more like a story, a book. “It’s really hard to predict chapter eight if you haven’t read chapters one through seven. You need to have everything from the moment this thing started all the way to where you are right now, because the analysis is going to change so much.”

This is where Advocacy’s architecture diverges from the rest of the market. What Téo calls the “atomic unit” at Advocacy is the matter. Everything is nested at the matter level. Users cannot even access the app until they train Advocacy’s AI on their specific case: core documents, theories, strategy, multiple data points. That context then evolves constantly, informed by every new document, every chat, every calendar event.

“Advocacy is a case memory platform. Everything that you do in the app for that particular matter has that context. It’s ever evolving. Which allows you to largely reduce the need for prompt engineering and the risk for hallucination, because you’re essentially grounding the AI into the case context.”

So where does Advocacy sit relative to the competition? Legora focuses on application add-ins, editing, mobile work, and collaborative workflows. Their play is making AI available where you already work, inside your document environment. Harvey is running the enterprise SaaS playbook: security with Vault, automation workflows, and a basic assistant layer. Both are also pulling in external legal databases and compliance frameworks for advisory.

Advocacy takes a different position: both of those approaches are great for transactional workflows, but they’re not built for the deep, evolving, adversarial context of litigation.

Téo is careful to draw the distinction precisely: “I don’t think that Harvey or Legora cannot handle complex cases. They can, especially in transactional. But litigation is very different. The requirements are very different. As a litigator, a lot of it is a moving target. The theories change and shift. The goal is not always super clear. A lot of it is investigative research. That’s why I talk more about enhancement than rigid workflows, because workflows presuppose that you know what the output should be. A lot of what I found myself doing as a litigator was exploring. Litigation is a contest over context.”

Legal AI and product strategy

The natural follow-up is: why not just wait for the foundation models to get smarter? Every litigator will eventually have access to Claude, Chat GPT, or whatever comes next, with a million-token context window and the ability to ingest documents. Why build a custom platform?

“Multiple reasons. One, on the security and safety side, we are single-tenant deployed. Your documents are physically, literally away from others. That’s not the case on Claude or ChatGPT. Two, when you own the entire contextual universe, you can do a lot of things. Yes, you can be on Claude and connect your document manager or Google Drive and then ask it to pull documents and open Word. But now you’re involving three tools, three different surfaces of attack. And they don’t talk to one another. It’s just a file being passed from one API to the other, with zero context other than whatever the user has written.”

With Advocacy’s system, documents live in a single contextual universe. They can be reused across workflows, emails, and analysis. When Dossier (Advocacy’s AI powered document storage system) communicates with Associate (their AI chat assistant), it already knows at ingestion what a deposition transcript is and how it relates to the case. “Your search becomes better because you’re not just throwing keywords through an API and hoping whatever is on the other end understands what you mean. Every single piece of our app is talking to one another in a way that nobody else can reproduce, because it’s designed by us.”

Téo acknowledges the risk: “It’s a big bet, because if people say they don’t want to use your platform, then I’m out of it. But the bet is that this contextual box is going to be self-improving. And that’s what we’re starting to see.”

On where AI and litigation go in the next six to twelve months, Téo sees acceleration in connectors and integrations across the market, but with an important caveat: “I would be cautious. I don’t think integration equals context. It can be an amazing way to bring context when that context is carefully selected and orchestrated. But it can also be just another slot. You connect everything and see what happens, when in reality you have no real orchestrator that owns the process. That’s what we try to focus on.”

He frames the broader vision around what he calls the “digital desk.” As a litigator, everything collapses onto your physical desk: you pull case law from Westlaw and LexisNexis, you pull evidence from your e-Discovery platform, you add your own work-product, and then you work across all of it. “That’s Advocacy. That’s why people ask, ‘Are you e-Discovery? Are you this?’ We’re the thing that was not possible before AI. And I think that’s what being truly native AI means: you have capabilities that are squarely impossible without AI. We try to have this approach for everything we do, down to our editor. Microsoft Word was designed at a time where AI was not on anyone’s mind. So there are capabilities that can be created with AI in drafting that should be created.”

This is also why Advocacy is unlikely to pursue a connector-first strategy: “I don’t think you’ll see us having a connector anytime soon, because I think you’re conceding to a degree that you’re not really a platform player. The whole premise of Advocacy is that we’re the top intelligence layer that aggregates from your own personal notes, your own case law, and your own e-discovery, and your own work-product.”

Customer stories

One of the most concrete ways to understand what Advocacy does differently is through an early customer engagement. Téo shared a story from the company’s first major proof point.

He had been invited to speak at a panel at a law school, alongside representatives from major legal tech players. He leaned heavily into the litigator-to-litigator narrative: “I really leaned into the story of, ‘I’m a litigator, this is for litigators, by litigators,’ with little details that are very hard to know or replicate if you’re not a litigator speaking to litigators.”

After the event, a managing partner from a firm in the audience reached out. They had an e-Discovery problem that no traditional platform could seemingly solve. A group of defendants in a case had been communicating through an iPhone app. Voice messages were sent within those conversations, but the voice messages were produced separately, untied to any specific chat thread. The firm had hundreds of loose voice memos and videos, plus over a hundred chat threads involving multiple defendants and third parties.

“The traditional e-Discovery world has no answer to this. The traditional response was: let’s get a bunch of lawyers, have them listen to every single voice memo, and try to figure out where they belong.”

The low moment came when the client initially pushed back, wanting integration with their existing tools hoping to use Advocacy to speed up that manual process. Téo took a different approach: “I said, ‘No worries, we can try to do this on the side. You go with the traditional way, and we’ll see who wins.’”

Advocacy’s team built an audio and video ingestion capability, transcribed every voice memo, created the contextual universe for the case, and ran a straightforward prompt: “here are all the text chats, here are the voice messages, please pair them based on context.” The AI matched the voice messages to their corresponding threads with high confidence, working across multiple languages.

“It was actually stunning to see. For example, in one of the first chat threads, the AI identified that in this conversation, two people are talking about kids and how annoying they are, and matched it with this audio that says ‘mama, mama, please go, mama.’ We were able to pair those voice messages with very high confidence. The firm became a client for virtually everything they do now.”

GTM and Hiring

Advocacy raised a $3.5 million seed round, intentionally designed around what Téo calls “the ideal cap table.” Relativity, through its innovation arm Rel Labs has joined, along with Fenwick & West LLP, prominent legal scholars, and a number of litigation firm partners. The goal was to ensure that attorneys and legal professionals have a stake and a voice in the company, not just as advisors but as investors.

“It took us some time to assemble the ideal cap table rather than just raise and announce it. Because you’re dealing with lawyers and law firms in a high-trust environment. It doesn’t happen in three weeks. We thought it was the right call for us to not launch immediately, but really take the time to introduce ourselves and make the right connections.”

The go-to-market team is built around range. Jim Watson leads sales, bringing decades of legal tech experience and relationship capital, even though he doesn’t come from a litigation background. Téo explains the logic: “When you do legal tech, you actually rarely deal with lawyers the first time you meet them. A lot of the folks doing the filtering are non-lawyers. Jim understands that. He has not only sold to lawyers but to non-lawyers as well.”

The rest of the team fills in around domain credibility. Téo’s COO Isabella is a former litigator. Other lawyers on the team can be pulled into demos when litigators show up at the second or third meeting with litigator-specific questions.

But the real insight on hiring came through in a small detail: what got Téo to hire Jim was learning that he was already using n8n to automate parts of his own sales workflow. “You don’t even have to be an expert to realize that if this guy with his background is already on n8n trying to automate sales, we can work with that. That’s been the spirit ever since: people who are curious and have something they can bring to the table.”

For the early stage, Téo was explicit that this was not going to be product-led growth for a while: “That’s what I told the devs. That’s why we took the time to nurture those relationships, get on a lot of pilots and trials. Even the first six months of Advocacy was just me calling around my buddies from law school and lawyers, saying, ‘How would you think about this? If somebody came out with something like this, what would you respond?’ It’s always the process at Advocacy.”

Practitioner-first culture

“Built by litigators, for litigators” is a phrase that gets thrown around in legal tech, but at Advocacy it translates into specific organizational practices. When a new lawyer or litigator joins the company, they do a presentation for the entire team: “How I would go about my day as a litigator before joining Advocacy and before AI.” They walk through a fictitious case, step by step, and then show the same workflow with Advocacy. The engineering team sits in the room for the whole thing.

“It’s beautiful what happens. Devs will be working on something like a window or a particular feature, and they no longer have to guess or call us and ask how we would think about this. They know inherently now that litigators want maximum control. So if the question is, ‘Should I hide the citations unless somebody asks for them, or should they appear as soon as the answer is ready?’ Probably the latter, because you’re dealing with people who want maximum control. For them to make those kinds of decisions without having to speak to anyone, they first need to understand the universe.”

The learning goes both directions. Téo admits that the legal side had to learn the pace of software development: “I didn’t know what staging was. I didn’t know what a QA was. I didn’t know what regression bugs were. We had to learn the pace of software, temper our ambitions on new products, and understand that there’s a difference between coding and scaling.”

The company runs a flat structure and deliberately keeps the team small. They have an AI-Ops engineer whose job is to make sure that if they hire someone, it’s because they desperately need to. “So far, the only hires we’re making are domain knowledge experts, whether it’s infrastructure for the engineers or a new practice area for the litigators.”

One of their staff engineers, formerly a lead at Dropbox, captures the other side of the cultural equation: “He keeps saying, ‘Man, I’m so glad I’m here, because I get to fix all the stuff that drove me nuts at Dropbox. The architecture didn’t allow for it, and you had no AI. Now you can literally code at the speed of thought.’”

Téo sees team size as a signal of your AI thesis: “If you’re not able to grow without hiring all the time, what does that say about your own AI thesis?”

The personal journey

The transition from practicing securities litigation to building a technology company wasn’t a clean break. Before fully committing to Advocacy, Téo actually took on a few cases of his own after leaving Robbins Geller, specifically to test whether AI-assisted litigation could work in practice.

“We told the clients, ‘Hey, we could go against these five lawyers here, and we’re going to be using AI if you want. If not, that’s fine, but we’re trying to make a proof of concept.’ And we did. We gave the other side a run for their money. We were able to run and file motions that otherwise would have been completely cost-prohibitive.”

The insight that stuck: “We can bring $900-an-hour level of service to a non-$900-an-hour legal buyer. Some people frame this as a collapsing industry. I don’t think it is. It’s like being an accountant and then the calculator comes out. Yeah, if you charge for crunching numbers on paper, your job is over. But there’s so much more that the calculator unlocks in terms of guidance and argumentation.”

The personal cost is real, but perhaps not where you’d expect it. Téo says he works more hours now than he did in Big Law, but the hardest part isn’t the workload: “The personal cost, the trade-off, and that’s true for every single litigator that has joined us, is that you’re no longer a litigator. That thing of making a case your own, fighting tooth and nail, breathing that case, it’s kind of gone.”

The shift has been toward what he describes as a “vicarious impact.” The time spent on the product and fine-tuning and building new capabilities, they can see how it plays out in actual litigation through their clients. “We’re now deploying the tools that allow that kind of service to reach more people and just make people better. Make lawyers better. Refocus things on higher-value items, which is strategy.”

Asked whether the experience has been more energizing or draining, Téo offers a surprisingly grounded answer: “Funny enough, it’s taking less of a brunt than my legal career did. I work more hours, don’t get me wrong. But the biggest struggle at the beginning was identifying, ‘Okay, where’s my impact now as far as being an advocate?’ It has taken more of a meta impact for us.”

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Advocacy has announced its $3.5M seed round with an official launch on March 6, just ahead of Legal Week on March 9th, where the company is a finalist in the Leaders in Tech Law Awards (Litigation Technology). The AI-native case memory platform is currently deployed with paying clients and running active pilots with top-tier litigation firms. Learn more at advocacy.ai.