We mapped the signals before anyone called it AI.
Long before "AI" became a pitch deck bullet point, we were mapping the data underwriters needed but couldn't get to fast enough. The work was unglamorous — aggregation, enrichment, source verification — but it was the foundation for everything that followed.
We didn't start with a model. We started with the question underwriters were actually asking: why is this risk priced the way it is? Everything we built was in service of answering that faster, with better evidence.
A decade later, the question hasn't changed. The tools just got better.
We didn't build around the regulation. We built inside it.
Every decision faces audit. Every finding needs a source. We built inside those constraints from the start — not around them. That's where most AI-in-insurance stories break. Ours didn't, because the audit trail isn't bolted on after the fact — it's the architecture.
The man in the machine.
Marvin is what's running underneath every snapshot — the system that aggregates from regulators, the public web, and a decade of our own proprietary data layer. Then it reasons across all of it to surface what matters. Findings stay cited to their primary source. The thinking layer is Marvin's. The longer we run, the sharper the thinking gets — and the harder it is to replicate.
The goalposts move. So do we.
We're building AI for one of the most regulated industries on earth — which means we're solving problems nobody has solved yet. Marvin learns from every submission, every edge case, every team that pushes it. The platform compounds in value the longer you use it, backed by a team that's been in this space for a decade.
Book a demoStill here. That's the proof.
Eleven years of commercial underwriting research — long enough to have seen most workflows, most audit standards, most renewal rhythms. Yours included.