Property Intelligence at Scale: How Geospatial Data Is Redefining Insurance Underwriting | Izik Lavy and Jacob Grob
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What is the purpose of the episode and who are the guests?
LLMs do a great job if you drop a picture in doing some analysis that looks good, looks decent, but it is still a probabilistic answer. And when you look at those probabilistic answers, there is 20% of the time or more that it is a hallucination. And the answer looks great, and people want to trust it because it sounds good, it's convincing. But it could be total nonsense.
Hello and welcome. My name is Jake Harding, and you're listening to Making Risk Flow Exploring the Ecosystem, a new companion series to the Making Risk Flow podcast, where we sit down with data providers, technology leaders, and Seitora partners to uncover actionable knowledge on how insurance can achieve frictionless risk flows. Today we're joined by Jacob Grubb and Isaac Lavi of GeoX. Hi guys, thanks for joining us. How are you doing? Hey Jake. Pleasure to be here. Hey, doing well? Good to hear. So I guess to get started, would you guys mind giving me a quick introduction into yourselves, your backgrounds, and then a bit of an overview of GeoX's mission and where it sits within the insurance data landscape?
Yeah, for sure. So my name is Isaac. I'm CEO of G O X.
I'm Jacob Grob. I've been in the insurance space selling data to insurance companies for 20 years. 12 of that was at Core Logic, a lot of building characteristics there. When I joined Core Logic, they were part of First American. They spun off and started just buying all sorts of data companies. So you look at Risk Meter, which was a lot of deterministic risk scores, Equicat, which is cat modeling. Marshall Swift Beck, which was uh replacement cost. And so really exposure to kind of all of these different pieces and parts of the insurance underwriting ecosystem. And as time went on, there's a certain set of data attributes that there's just a blind spot in the market for. And that really is what led me to looking at computer vision and
Data extraction through that. And GOX is just such a natural fit. They have been doing this since 2008, are absolute leaders in Australia where they built and scaled up, in Japan, where precision is absolutely key. And they're coming into the US now with a very mature product and one that can deal with problems at scale. When I look at Us compared to kind of the other players out there, the other players are much more focused on high-touch underwriting, if you will, where you want to sit down and dissect a high-res image where you have somebody touching and feeling every single one of those policies. GOX is taking a different approach and is really focused on solving problems. That scale. So that's high volumes, high straight through processing, getting a submission to a point where the underwriter has all of the relevant detail at their fingertips and can bring that decisioning down from two hours to a matter of minutes.
So that's really what we're focused on is that high scale, high volume analysis. versus the other. And really like our mission is to be that base layer, that base information that's being extracted from imagery and plugged into all of the AI underwriting tools that are coming to market right now. All of the underwriting workbenches that are out there. Whether that's an underwriting workbench that is provided by a third party vendor, or it's one that a carrier is building on their own. At the end of the day, they need to understand what's happening at that property, what's on that property. And we've built a database that includes all of that information. And so we want to be that first choice. For that first piece of information.
Yeah, I mean, a lot of that really resonates. A few things you mentioned there, I'm really excited to kind of cover in the rest of our conversation in that how do you make it work at scale? What learnings can we take from other markets as well? Isaac, I'd love to hear from you a little bit about, I guess, how you guys differ from competition and other providers within the market, but within your kind of specific area of intelligence as well, if you could.
Yeah, for sure. So I will continue from Jacob's point.
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Chapters
8 chapters
1
What is the purpose of the episode and who are the guests?
0:06–6:15
2
How does GeoX describe its mission and the unique value of its geospatial platform?
6:15–12:17
3
Why does GeoX focus on deterministic, confidence‑scored models instead of probabilistic LLM answers?
12:17–19:49
4
What specific property‑level insights (e.g., roof condition, flood‑zone elevation) are unlocking new underwriting opportunities?
19:49–26:30
5
How does GeoX achieve nationwide scale with its compute infrastructure and AI hardware?
26:30–32:18
6
When should insurers buy a data solution versus building their own geospatial engine?
32:18–38:18
7
What changes are needed in the insurance industry to accelerate adoption of advanced data and AI?
38:18–38:55
8
What final advice do the guests give for the future of property intelligence and underwriting?
38:55–39:16
Speakers
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