Meet the Founders: Omar and Francisco of ResiQuant

Founder’s Journey & Vision
Omar shares his background and what led him to launch ResiQuant

Omar: I am a second-generation structural engineer from the Bay Area. My dad conducted damage evaluations after the 1989 Loma Prieta earthquake, so I grew up aware of how fragile the built environment is.

During my undergraduate studies at UCLA, I was deployed after the 2019 Ridgecrest earthquakes and saw firsthand how seemingly identical buildings could perform very differently under the same event. That experience motivated me to pursue a PhD at Stanford focused on applying AI to disaster risk.

At Stanford’s Blume Center, I met Francisco. During the pandemic, with labs nearly empty, we spent long nights simulating building-level resilience and recovery for San Francisco properties. Our conversations often returned to a single idea: how to build a world where natural hazards do not have to become disasters.

Francisco reflects on his journey and what drew him to the mission

Francisco: For me, it is deeply personal. I am a third-generation engineer; my father and grandfather were civil engineers. I grew up about 50 kilometers from the epicenter of the 1999 Armenia earthquake in Colombia, which devastated the region. My wife, who I met years later, was living in Armenia and experienced that destruction firsthand. I will never forget being at home when everything started shaking violently.

That experience led me to structural engineering. I spent six years designing buildings and bridges to withstand hazards, but I kept seeing a gap. Building codes are designed to prevent collapse, not minimize damage or speed recovery. While researching how to quantify disaster impacts at Stanford, I met Omar. We both noticed that buildings labeled as similar could behave very differently in disasters. That insight became our foundation.

The Inspiration Behind ResiQuant

Omar: Natural hazards are inevitable, but human disasters are not. Catastrophe is driven by vulnerability, not by the hazard itself.

During my PhD, we first explored the concept of resilience by design, helping developers use design intelligence to create more resilient properties. While this approach seemed effective, hundreds of interviews through Stanford Lean LaunchPad and later PearX revealed a key challenge outside the development phase. Insurance carriers were unable to price or incentivize resilience because they lacked credible building-level data. Transitioning into insurance was a natural move since that is where much of the risk science is ultimately applied.

We discovered that even advanced catastrophe models rely on dozens of inputs, yet underwriting teams often have only a handful available. This gap has real consequences in the 2025 market. Capacity is returning, but risk clarity has not kept pace. E&S premiums rose around 13 percent to approximately 46 billion dollars in the first half of 2025, with property accounting for about a third of that. The demand exists, but underwriting still depends on thin property data.

ResiQuant was created to close this gap. Using AI, we embed decades of structural engineering expertise directly into underwriting. The vision is for underwriters to begin their day with AI agents that have already identified construction details, structural vulnerabilities, and key insights, allowing them to prioritize and price risks more effectively. That is the future we are building.

About ResiQuant

Omar: ResiQuant is an AI-powered underwriting platform for property insurers and MGAs with significant natural catastrophe exposure. It automates the tedious process of submission intake and enhances it with structural engineering intelligence, integrating decades of engineering practice, disaster forensics, and academic research into AI agents that emulate expert judgment.

We work with several U.S. specialty carriers and MGAs that focus on catastrophe risk. Our platform improves data quality, reduces expense ratios, and increases confidence in risk selection. Adoption has accelerated as carriers recognize the value of AI-driven risk assessment.

This is not about automation alone but about the intelligence that automation enables. In the current market environment, abundant capital has driven catastrophe rate-on-line reductions of about 5 to 15 percent. The carriers that are succeeding are those proving property-level resilience. Growth in catastrophe-exposed markets requires not just capital but engineering intelligence that enables profitable underwriting at granular levels. This is the gap we solve while also lowering expense ratios, empowering carriers to succeed in markets others avoid.

Delivering Real-World Impact

Omar: Our impact is already measurable. In a year when first-half insured catastrophe losses reached around 80 billion dollars, among the highest on record, accurate assessment of a building’s vulnerability is the difference between profitable growth and retrenchment. Enhanced building data leads to material improvements in expected loss metrics, which directly influence reinsurance and rating outcomes.

Operationally, clients report significant reductions in manual data entry, freeing underwriters to focus on higher-value work such as broker engagement and negotiations.

Francisco: Our platform identifies costly misclassifications that have major financial consequences. For example, a recent submission initially classified as unreinforced masonry was reclassified by ResiQuant as a wood-frame structure with brick cladding. This distinction is critical for pricing seismic and wind risk. Another frequent issue is misalignment between construction year and actual code adoption at the county level.

These nuances matter. The difference between unreinforced masonry and wood-frame construction can determine whether a building experiences minor roof damage or partial collapse. When underwriters can see these distinctions clearly, they price more accurately and make better decisions about risk selection.

Challenges and Lessons Learned

Francisco: Team composition has been critical. NatCat-focused AI requires a unique blend of structural engineers, AI researchers, and product builders who understand the realities of underwriting. Finding the right balance has been essential.

Another challenge has been the pace of AI advancement. The difficulty lies not only in building effective models but in creating an architecture flexible enough to incorporate innovation while preserving domain specialization. Our modular approach allows continuous evolution while remaining anchored in engineering accuracy.

Omar: Building from the ground up is an expedition. The path is rarely clear, and progress comes one step at a time. Being transparent about that reality attracts people who embrace challenge and are motivated to create lasting impact.

Technology and Market Evolution

Francisco: Our approach focuses on two horizons. The first is automation, aimed at eliminating repetitive work. The second, and more transformative, is embedding deep domain expertise so that AI can surface insights even experienced professionals might overlook. We teach AI to reason like a structural engineer, grounded in evidence and explainability.

We rigorously validate our models against historical events and ensure full auditability, which is essential for enterprise adoption. The platform’s architecture separates the core engineering intelligence from the AI layer, allowing us to integrate new techniques without compromising decades of domain expertise. This design keeps us aligned with rapid technological evolution while remaining faithful to engineering principles.

Omar: Over the next few years, we see three key shifts reshaping competition among carriers.

First, the rise of real-time portfolio intelligence will allow underwriters to monitor concentration across perils and understand how each new risk affects aggregate exposure and reinsurance utilization.

Second, speed is becoming a competitive advantage. The carriers capable of underwriting in minutes rather than days will consistently capture the best submissions before competitors respond.

Third, reinsurance negotiations are becoming more data-driven. Carriers with granular, property-level data can demonstrate portfolio quality, secure better terms, and strengthen their competitive position.

This transition from defensive to proactive underwriting is already visible in daily operations. AI enables carriers to enter markets they once avoided because they could not differentiate good risks from bad ones at scale. The winners will be those who deploy capital intelligently, not merely abundantly.

Looking Ahead

Francisco: ResiQuant is accelerating multi-peril expansion and deeper workflow integrations. The platform is evolving from individual risk assessment to portfolio-aware intelligence, allowing underwriters to evaluate how each decision impacts their overall exposure. These advancements are being rolled out with current customers.

Omar: Our vision is for ResiQuant to become the category-defining platform for natural catastrophe property underwriting. We are building toward a future where most catastrophe-related decisions are informed by AI-driven structural insight, whether for carriers underwriting individual buildings, reinsurers evaluating portfolios, or brokers qualifying submissions.

Our goal is market leadership through network effects. As more carriers use our intelligence, we generate better insights; as our models improve, we attract even more users and expand into adjacent markets, including specialty lines, international markets, and deeper reinsurance integration.

By 2030, we aim to support a significant share of U.S. commercial property catastrophe premiums. The combination of AI and structural-engineering expertise forms a durable foundation for an intelligence platform that transforms how the industry evaluates and prices catastrophe risk.