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Insurance companies used to spend months pricing disasters. Earthian AI wants to do it in real time.
The Amsterdam-based startup, founded by former Harvard researcher Shayan Shokri, is building what it calls a “financial inference layer,” an AI system designed to tell banks, insurers, and asset managers not just what’s happening in the world, but also what those events are actually worth in dollars and their risk exposure.
And investors are paying attention.
On April 30, Earthian announced a pre-seed raise at a $112 million valuation — structured as equity plus debt — with backing from Rabobank and NVIDIA’s Inception program. The angel round also drew former executives from Goldman Sachs, Moody’s, and Accenture.
“Five Fortune 500 firms are using our risk models today, and we expect that to be 30 by 2027,” Shokri said in a statement. “That kind of traction doesn’t come from a good pitch deck. It comes from solving a problem these institutions have been trying to solve internally for years.”
The problem isn’t hypothetical. Natural catastrophes caused $107 billion in insured losses globally in 2025, the sixth straight year that figure has exceeded $100 billion. The Los Angeles wildfires alone accounted for $40 billion. Add in geopolitical fragmentation, climate volatility, and AI-driven cyber threats, and the math is moving faster than the analysts trying to do it.
Credit: Earthian AI
That’s where Earthian’s positioning starts to land. While large language models dominate most public AI conversations, Earthian is focused on SLMs, or small language models, trained specifically for financial reasoning.
“A large language model can write you a sonnet, but it shouldn’t be pricing your catastrophe exposure,” Shokri said. “Our small language models run faster, use less energy, and hold up better in regulated environments. They don’t hallucinate on the kind of financial nuance that general AI tends to choke on because they were never trained to be generalists.”
The models may be small, but the intake is massive. Earthian’s AI engine processes up to 2 billion data points per day, pulling from satellite imagery, SEC filings, regulatory updates, and corporate disclosures — a level of capacity that exceeds what even the largest insurers typically have in-house.
And risk modeling is just the entry point. A separate research initiative, Project Alpha-Index, explores what the company calls “AI-native portfolios,” models that continuously rebalance holdings across all 11 GICS sectors based on forward-looking risk reasoning, with the goal of outperforming traditional benchmark strategies like the S&P 500.
It’s a direct shot at how Bloomberg, Moody’s, and S&P Global have built their empires for decades: aggregate the data, sell it, repeat. Earthian is betting the next category belongs to whoever can interpret risk fastest.
Shokri isn’t thinking small either. “We want to be worth $1 trillion within ten years,” he told FD, pointing to Google and Nvidia as proof that markets nobody saw coming can reshape entire industries almost overnight.
That may sound enormous. But then again, pricing disasters in real time used to sound impossible too.
Insurance companies used to spend months pricing disasters. Earthian AI wants to do it in real time.
The Amsterdam-based startup, founded by former Harvard researcher Shayan Shokri, is building what it calls a “financial inference layer,” an AI system designed to tell banks, insurers, and asset managers not just what’s happening in the world, but also what those events are actually worth in dollars and their risk exposure.
And investors are paying attention.