
We started as a vertical SaaS company in insurance. We grew into operating our own wholesale brokerage and a programs group. Through that, we've lived the operational reality of applying AI in insurance — what works in production, what breaks, what compounds. We've just closed fresh funding to scale this operating model to retail insurance agencies across the country.
Our CEO, Joey Bouchard, built and ran applied AI teams at Palantir. We bring that technical rigor together with years of hands-on insurance operations experience to help agencies deploy AI that works in the real world. (Why insurance is worth this attention.)
Why this role exists
Brokerage execution runs on heroics. Service quality depends on who owns the account, how overloaded they are, and what they happen to remember at the right moment. A submission gets marketed broadly, or it doesn't. A renewal gets the same rigor as new business, or it doesn't. A missing detail gets chased, or it stalls.
We don't think that's inevitable. Brokerage execution can be made systematic: software, automation, and AI carrying disciplined follow-through alongside human experts, inside clear bounds, in a regulated environment.
The hard part isn't the model call. It's governability — making AI accountable enough to trust with real deal flow. Who is the AI acting for? What is it allowed to do? What happens when it's wrong? In a regulated industry, those aren't side questions. They're the engineering problem.
We've been answering them inside our own wholesale brokerage for years. That's where we test whether the model actually produces more reliable, scalable, and consistent service. If it doesn't work for us, it doesn't deserve to be packaged for the market. Now we're packaging it.
The role
Applied AI Engineers sit at the point where Terminal, our platform, meets the messy reality of a customer's agency. You own the outcome at the customer. You also own the signal back to the rest of the company.
A typical month: you're embedded at a retail agency, working alongside their operators. You're identifying the workflows where heroics break down — submissions, renewals, follow-up, the work that today depends on someone's memory — and you're building governed AI execution into those workflows on Terminal. You're calibrating where humans stay (judgment, relationships, accountability) and where the system carries the standard (follow-through, coordination, attention).
The hardest and most interesting part: making it trustworthy. How AI decisions get reviewed, attributed, and defended. That work is what determines whether any of this can actually live in production.
Then you bring it all back. Engineering, Product, and Design need to know what generalizes and what doesn't, so the platform compounds across every customer instead of fragmenting into one-offs. You're the conduit.
What you'll do
What we're looking for
What you get
These first few roles set the cultural tone for everything that comes after. If you want to understand what this role actually is, what it isn't, and what it likely means for your career — apply and reach out.
Product
Austin, TX
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