William Steenbergen, co-founder and CTO at Federato, shared insights into the company and the technology it's using.
Responses have been lightly edited.
What's the origin story of the company?
Federato started with a question we explored at Stanford: how could AI help people make better decisions in an industry as high-stakes as insurance?
When we started talking to carriers, we kept hearing the same frustration: decisions were happening everywhere, but the context wasn't. Appetite lived in one place, performance data lived in another, work lived in email and spreadsheets, and leaders were left stitching the story together after the fact.
So we built Federato as an AI-native platform for the full policy lifecycle, designed to bring decision context together in real time. The goal is simple: help teams move faster, stay disciplined, and make smarter decisions that hold up, both operationally and strategically.
When was it founded and/or when was the product launched?
Federato was founded in 2020.
Can you tell me about the founders/founding team?
Will and I both come from technical backgrounds, but we're complementary in how we build.
Will Ross is our co-founder and CEO. He's spent more than a decade building and scaling AI products, including leading ML teams at IBM Watson. He's also worked in corporate development and AI-focused investing, advised early-stage companies, and taught Udacity's AI for Business Leaders program, focused on what it takes to deploy AI in production.
I'm William Steenbergen, co-founder and CTO. I've built and deployed machine-learning systems, including recommendation models and chatbots, working with insurance and retail data at scale. That experience shaped how we build: the best results come when models are paired with decision context, clear standards and workflows that make consistent decisions easier.
Together, we founded Federato to change how insurance work gets done, not by replacing expertise, but by making it easier for that expertise to show up consistently across the policy lifecycle.
Any meaning behind the company name?
The name Federato reflects our federated approach to data. Insurers already have a lot of information, but it's scattered across systems. Federato brings that information into a unified view, so teams can make decisions with full context across the policy lifecycle.
How many employees?
200
Where is the company based?
The team is fully remote, distributed globally.
What pain points is the technology trying to solve?
A lot of insurers are still operating on core systems shaped by decades of mergers and acquisitions. That means work gets broken up across too many tools, handoffs, spreadsheets and inboxes. People do their best, but decisions get made with partial context, and consistency is hard to maintain.
Federato addresses that with an AI-native platform across the full policy lifecycle. We help teams prioritize opportunities early, reduce manual work, apply standards consistently, and understand portfolio impact in real time. That's how you move fast without losing discipline.
What funding rounds has the company had?
We raised a $15 million Series A in 2022 led by Emergence Capital, with participation from Caffeinated Capital and Pear VC.
In 2023, we raised a $25 million Series B led by Caffeinated Capital, with participation from Emergence Capital and Pear VC.
In 2024, we announced a $40 million Series C led by StepStone Group, with participation from Emergence Capital, Caffeinated Capital and Pear VC.
In November 2025, we raised a $100 million Series D led by Growth Equity at Goldman Sachs Alternatives, with participation from returning investors including Emergence Capital, Caffeinated Capital, StepStone Group and Pear VC.
What's ahead?
The industry is moving beyond AI pilots. Insurers want real operational change across the full policy lifecycle, with the controls, context and workflow integration to scale.
Our focus is on helping teams redesign work so good decisions compound into better outcomes over time. With our Series D, we're accelerating product innovation and expanding to meet rising demand for AI-native capability that isn't constrained by legacy architecture.
