Scout InsurTech Interview with ISeeChange

ISeeChange is an adaptation technology company turning real-time observations into actionable flood intelligence for cities, utilities, risk modelers and insurers. By transforming photos, videos and field reports into structured flood data, the company gives stakeholders the ground truth they need to calibrate models, improve infrastructure decisions and assess risk with greater accuracy. Chris Luiz sat down with CEO Julia Drapkin to learn how ISeeChange is reshaping the future of flood data and its role in the insurance ecosystem.

Who are your clients?

Our current customers are cities, utilities, and engineering firms. They use our platform to understand what is happening on the ground during flood events and improve operational response, planning, and infrastructure investments. At the same time, the data we are gathering and processing with our AI is increasingly valuable to risk modeling companies. Both the raw data and the insights help validate, soft calibrate and augment models with observations they do not currently have. We expect a much bigger focus on that B2B customer segment in 2026.

What does your product do? 

Our specialty is taking unstructured flooding data and turning it into quantified intelligence in real time. That includes photos, videos, complaints and stories from residents and from our customers who are responding to live flood events.

We take an image or a report and convert it into flood height, flood volume and flood extent, and we do this automatically as events unfold. In effect, we are taking an architecture and engineering model calibration process that usually takes about two weeks and compressing it into real-time output.

Put simply, we are taking photos and turning them into flood maps. That helps fill critical gaps in knowledge, especially for frequent, lower severity flooding that traditional satellite or sensor approaches often miss.

In markets where we have a lot of data, a carrier could give us the addresses of all its policies in a city and ask which ones have experienced street flooding and how often. We can come back and say that a specific group of policies had a foot or more of water on the street in particular years. It is a direct way to understand how total insured value is exposed to flooding that is already happening and becoming more frequent, rather than only thinking about rare catastrophic events.

How much capital have you raised? 

In terms of dilutive capital, we have raised very little, only about $600,000, and we are already in market with our product.

Was the company born from within or outside the industry? 

Outside of the insurance industry. That said, insurance is clearly a critical part of the puzzle. Flood risk touches cities, utilities, infrastructure, and then ultimately shows up in insurance portfolios, so we have always known that our data would be highly relevant to insurers.

What growth metrics have you accomplished over the last 12 months? 

Over the last year, we have signed our first water utility customer. We have enabled our AI to be deployed anywhere in the United States on demand, which is new for us and unlocks a level of scalability we did not have before. We are heading into 2026 with close to $900,000 in the pipeline. We launched our Flood Extent AI this spring, which is very new and very exciting for us. We are in contract conversations with a major synthetic aperture radar satellite provider that operates one of the largest commercial fleets in the world.

We also benchmarked our model validation process against a traditional architecture and engineering firm and found that we are about forty times faster. Together, these milestones give us both technical validation and strong commercial momentum.

Within your domain, what is the current challenge that the industry is facing? 

In flooding, the core challenge is ground truth.

Risk models are often global or regional in scale, and they need to be downscaled to a single latitude and longitude, or to a specific block, in order to support accurate and actionable decisions. Without local ground truth, the models can be directionally right and operationally wrong, which leads to very costly mistakes.

For insurance, that can mean not writing business in areas that look unattractive in the model, even though the real-world data suggests the risk is manageable. For infrastructure projects, it can mean underestimating flood risk, mis-sizing a project, or placing it in the wrong location. For day-to-day operations, cities and utilities are under-resourced, they manage large networks of assets, and they rarely have good intelligence about which assets matter most at any given time. The hidden risk in those systems eventually transfers to insurers, whether in auto or property.

Without continuous local ground truth, both infrastructure decisions and insurance decisions are exposed.

How does ISeeChange take a unique approach to providing value? 

Our core innovation is to turn lived experience and operational observations into structured, model-ready data in real time.

We collect photos, videos, and reports from residents, asset management systems,  and municipal field teams during flood events. Our AI transforms those unstructured inputs into metrics like depth, volume, and extent, and we do that at the speed of the event. That means we are not just validating models after the fact. We are continuously correcting them and feeding them evidence from the ground.

That ground truth supports multiple decision layers. It helps risk modelers calibrate their products. It helps cities and utilities prioritize which drains, culverts, and streets matter most today. It helps insurers understand not only catastrophic risk, but also the frequent, lower severity events that drive recurring claims and erode profitability.

In short, we make local flood impacts visible, measurable, and usable so that the entire system can move from guessing to knowing.

What inspired you to start this company? 

ISeeChange actually started as a radio show, which sounds a little unusual for a data company.

At the time, global climate models were struggling to downscale in a way that meant something to everyday Americans. No climate scientist could point to an individual house and say with confidence that it was flooding because of climate change. Attribution science has advanced a lot since then, but the basic structural issue is still present. We still struggle to connect high level climate information to local, lived experience.

Insurance does not have the local insight that a resident or a utility operator has. Without that visibility, the system fails communities and it also fails markets. I started ISeeChange to bridge that gap. What began as storytelling and listening to communities has evolved into a data and analytics platform, but the core mission is the same. We capture local experience, translate it into evidence, and use it to fix the structural disconnect between models and reality.

Can you share any goals for the next 12 months? 

We have several concrete goals for the coming year. We are raising a seed round so that we can grow our development team and keep up with customer demand. We want to increase our B2B representation, both in our pipeline and in our customer base, particularly with risk modelers and insurers. We aim to be active in at least twenty flood prone metropolitan areas and regions in the United States, where the flood market is most significant.

We would also like to have at least one insurance pilot under our belt by the end of the year. All of these goals point in the same direction. We want to get our product into as many high-impact places as possible, prove its value across the flood ecosystem, and deepen our role as the ground truth layer for flood risk.

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