"The economy is so complex, AI will change every facet. This worked because we jumped on something narrow and understood it really well."
EvolutionIQ
Imagine if improving disability insurance claims processing with AI helped more people return to work.
Today, EvolutionIQ's AI-powered claims guidance platform is used by the majority of U.S. disability carriers to help millions of injured workers get back on their feet. In 2019, a co-founding trio saw an opportunity that others missed: in a slow-moving industry sat mountains of underutilized data.
While others saw constraints in the concentrated, risk-averse world of insurance, they recognized the opportunity to create massive value with an early vertical AI application in an obscure area. Here's what stood out to us from their journey to a $730M acquisition:
- Founders
- Tomas Vykruta, Michael Saltzman, Jonathan Lewin
- Initial Partnership
- Seed
- Categories
- AI / Enterprise / Healthcare
- Partner
- Bill Trenchard
- Location
- New York
- Founders
- Tomas Vykruta, Michael Saltzman, Jonathan Lewin
- Initial Partnership
- Seed
- Categories
- AI / Enterprise / Healthcare
- Partner
- Bill Trenchard
- Location
- New York

Inside the journey to build an enduring AI business
We recently sat down with the founding team to unpack how they went from an early AI idea to a $730M acquisition in five years. From navigating long sales cycles and building trust with risk-averse customers, to assembling an elite technical team before the ML talent wars heated up, their story offers a blueprint for thoughtfully building an AI business that works.
Start with clear criteria for finding your idea.
After years at Google as an ML engineer working on everything from self-driving cars to Maps recommendations, Tom Vykruta knew he wanted to start a company leveraging AI. To narrow from a sea of possibilities, he established specific criteria: a repeatable problem, massive dataset, manual or rules-based current solutions, not much competition yet, and an interesting mission. Insurance claims processing checked every box.

Embrace constraints in an overlooked market
Many VCs passed because insurance claims felt like too small a pond — there were only about 150 people in the United States who could make a decision to buy their products. But the team saw something different: a concentrated market where an early application of AI could create massive value. They went unreasonably deep to break into and truly understand this overlooked, insular market.

Make recruiting your #1 execution priority
The team made the unconventional choice to hire a full-time in-house recruiter in their first year, adding dedicated sourcers when they were still tiny. By the time they were 30 people, they had around four in-house recruiters. "Some investors would say, 'Why don't you have more sales people? And why are you running a recruiting agency?' But we saw it as four out of the 100-person team we wanted to be a year from now," says Mike.
If you're trying to build an application layer business using AI, you have to figure out your moat. Assume it works, and it's three years from now — what's going to be materially different about your data set, workflow, and customer relationships that another company can't replicate in a weekend project?


When the rules don't fit, break them thoughtfully.
Against conventional startup wisdom, the team moved to a co-CEO model with Jonathan and Mike sharing the top seat. "Everyone told us that it was crazy and made no sense — but it ended up being a great decision. The startup CEO role is almost an impossible job because you never have enough hours in the day. Having two people share that role gave us incredible leverage," says Jonathan.
Pattern recognition is helpful, but sometimes as a founder you just have a very strong sense of something. The conventional wisdom is there to help you — not to handcuff you.
