LTN Startup Spotlight is an ongoing series that aims to amplify the stories of startups in the legal tech space, highlighting what they’re working on, how they’re adding to the legal tech market and changing the conversation around legal tech innovation and what lessons they’ve learned in their startup journey so far. If you’d like to be considered for inclusion in a future profile, click here.
Name of company: Advocat AI
Founder(s): Pradnya Desh
Date founded: Jan. 10, 2019
Date(s) and amount(s) of funding received: $1.8M total in 2021 and 2022
What does your company do? Advocat AI enables companies who work with physical goods to generate, negotiate and keep track of contracts and contract data. The platform also generates granular data about expenses and environmental footprint, alongside strategies that both increase profits and improve sustainability. It was founded by CEO Pradnya Desh, international trade attorney and former US diplomat.
What need did you see in the legal tech market that wasn’t being filled? Contract AI with a focus on physical products companies and their environmental impact. Physical products need to be shipped, stored, tracked and there are finite amounts. Software that combines the digital and physical often misses the very important differences. My background as an international trade attorney enabled me to see the world differently.
There is also valuable data trapped in contracts, and while it must be protected, it should be available for the data owners to meaningfully use. That’s where I see enormous potential for physical goods companies to improve their business outcomes while also making strides towards improving climate outcomes.
What have been your biggest struggles as a startup? Engineering a product that meets my standards. We as attorneys tend to be perfectionists with good reason. I was not willing to release our product until I knew that it worked well, protected data and was highly accurate. In startups, we’re told to release early and often, and also if you’re not embarrassed of your first release, you released too late. That kind of thinking is irresponsible when you’re dealing with contracts and legal data. Working against that conventional wisdom has been my greatest challenge.
What advice would you give other startups when it comes to securing funding? I think that the investment climate may finally be improving so just make sure that you’re solving a real world problem that you believe in, and have a great team around you.
Legaltech News recently sat down with Pradnya Desh to discuss her thoughts on raising funding, investors’ evolving appetite for legal tech, and how she transitioned from being a U.S. diplomat to a legal tech entrepreneur.
The interview below was edited for clarity and length.
You’re a former U.S. diplomat who represented the U.S. on foreign policy issues at the UN and the WTO. Tell me a little bit about the transition from a government role to launching your legal tech startup?
Pretty much the day after graduating, I started the U.S. Foreign Service. I went through the program of being trained on speaking a few other languages and what it means to represent the United States all over the world…. I loved being able to see the impact that we could make on the world as well as on the people of the United States. And so that’s pretty much how my legal career started. I started out in Washington, D.C., working in trade policy, and then did a tour in Geneva, Switzerland representing the U.S. at the World Trade Organization.
It allowed me to have a really practical view of just the goal—like what is it that we’re trying to do? The laws might all be in different languages, they may come from different cultures, but we’re generally as countries trying to maximize the same things when it comes to peace and prosperity, fairness. So that really resonated with me and that was really my favorite part of being a diplomat.
Are there any lessons that you’re able to bring from this experience into Advocat AI?
I think it gave me a really practical point of view because as a lawyer, the part that I guess maybe didn’t love [was] the small details and the regulations and all that. Because yes, those are important and we must make sure that we will fulfill those correctly and yet, we have to always keep our eye on the goal. And so that applies to business negotiation where, at the end of the day, these two companies are trying to do something together. And while the process might be hard, the goal is ultimately important.
That’s what I brought from my time in policy is that we have to always look to that. After I was a diplomat, I ran a law firm, which at first felt extremely different because it felt very, very specific to a particular client, a particular matter, a particular page—that granular. But I liked to be able to look at, again, the goal of why the clients are doing what they’re doing, why are they in that business? What are they trying to achieve? And so I think it brought just an overall broader view.
Advocat AI has participated in many different competitions, winning several of them. What was the impetus for participating in these different endeavors, and what have they brought to the company?
It came from the need to fundraise. As a lawyer in a law firm, it was a pretty unfamiliar thing to do to get investors to invest in your company. And so I approached it the way that I knew how to do it, which is just build a big platform and bring a lot of attention to what we’re doing. And then that brought in some investors. It actually brought in some really great investors. And so we had Morgan Stanley as an investor, Spark Growth Ventures and a few others, really just by being visible.
You noted that the investment climate “may finally be improving.” How so?
This year has really been a mixed bag. We heard so much that ‘AI companies are easily getting investment and everybody else is in trouble’. And that was maybe a little bit of a simplistic point of view, because that’s not quite what we saw. Over the course of the year, it really has been a very challenging environment for fundraising. We found in the fall, things really started to loosen up. … Maybe it was because funds were trying to reach their goals. And it very well might be that because we do have a few there that are trying very hard to close as quickly as possible with us. And we’re in negotiations right now.
But it’s also that I don’t think that any investor or any tech company has the option of moving slowly. AI is moving so fast that we not only have to keep up, we have to make sense of what’s happening in the world and shape it. And investors have been trying to make sense and to make the right bets. So they are investing again, at least toward the end of this year.
How do these fluctuations in activity from investors impact your strategies to raise funds? Do you change anything to make sure to strike while it’s hot?
If I knew it took this long, I would have most certainly raised a lot more. So back when we raised our initial round, generally the advice was don’t raise more money than you need. However, what you don’t know is the future. So you might not know how much money you’re going to need because it might take longer to raise from entirely external events. And so I would have raised more money.
Your current CTO and co-founder, who is also your husband, Chetan Desh, formerly worked in AI at Microsoft. How much of an impact does having someone with this kind of experience have on your ability to innovate compared to other startups who may not have access to such talent?
It was very important to us. Even before launching, it was really important to me that I could find the right talent, because it’s one thing to tell a great story on stage with a microphone about what we’re going to build. But it’s really important to actually be able to build it. And so before even starting to fundraise, I made sure that we had the right people.
Even though AI is moving so fast … it’s important to be able to deliver on the promises that you say we’re going to do, it’s not just to investors. It’s the promise that you’re making to your customers that this is going to work in a particular way. And so we started out with a very solid tech background, and a very solid tech structure. We’re not a company that just builds on top of what GPT does…we built some solid knowledge graph-based technology that has an accuracy layer on a [large language model]. And it prevents private information from going into an LLM…I feel like that is also a really important responsibility of somebody who is a lawyer as well as an entrepreneur.