With us today is Dominic Jainy, an IT professional with deep expertise in AI, machine learning, and blockchain. We’re diving into the rapidly evolving world of insurtech, specifically focusing on a challenge that has become a major liability in our digital economy: cloud downtime. We’ll explore how new models of parametric insurance are transforming this technical issue into a measurable financial risk, discuss the real-world triggers behind these innovative policies, and look at how data-driven risk intelligence is empowering businesses to build more resilient infrastructures.
With your new $1.77 million in funding, you’re initially focusing on the MENA region and North America. What specific market needs are you seeing in these regions, and how will you use the capital for product development and risk modeling to address them effectively?
It’s a very deliberate choice. Both the MENA region and North America are experiencing an explosion of digital-first businesses, but we’ve noticed that the financial safety nets haven’t kept pace with their technological reliance. There’s a palpable anxiety in these markets because while everyone has migrated to the cloud for its scale and speed, they’ve also inherited a massive, unpriced liability. That $1.77 million is foundational; it’s being channeled directly into sharpening our product and, most critically, our risk modeling. We’re building sophisticated algorithms that can accurately quantify the financial impact of an outage for a specific business, turning a vague threat into a concrete, insurable event. This capital allows us to move from theory to early deployments, proving to our first customers that they can finally have financial certainty in a very uncertain environment.
Parametric insurance uses predefined triggers for payouts, which differs from traditional claims processes. Could you walk us through how this model works for a cloud outage and why it offers a better solution for businesses than relying solely on service level agreements?
Absolutely. Think of the traditional insurance claims process—it’s slow, adversarial, and requires you to painstakingly prove your losses after the fact. It can take months. Parametric insurance completely upends that. For a cloud outage, we agree on a specific, verifiable trigger upfront—for example, a particular service in a specific cloud region being unavailable for a set duration. Our systems monitor for this trigger using verified data. The moment that predefined condition is met, the payout is automatically initiated. There’s no lengthy claims adjustment or debate. It’s a clean, transparent transaction. This is a world away from service level agreements, which are often just a token gesture from cloud providers. An SLA might offer you a small service credit, which is frankly insulting when an outage has just cost you millions in lost revenue and customer trust. Our model provides immediate, meaningful capital precisely when a business is in crisis.
You target digital-first companies like fintechs and airlines. How does your real-time risk monitoring provide these specific clients with actionable intelligence, and can you share an example of how a customer might use this data to inform their infrastructure decisions before a failure occurs?
For businesses like fintechs and airlines, uptime isn’t a feature; it’s the entire foundation of their operation. Every second of downtime translates into staggering financial and reputational damage. That’s why our offering is more than just an insurance policy; it’s a continuous risk intelligence partnership. We provide a real-time dashboard that shows clients their precise exposure. For instance, our system might identify that a critical payment processing service a fintech relies on is hosted in a cloud region that has shown historical instability during peak traffic. Armed with that data, the client can proactively re-architect their system, perhaps building in better redundancy or shifting some workloads to a more stable region. They’re no longer just reacting to failures; they’re making informed infrastructure decisions to prevent them, all based on the same data that underpins their insurance coverage.
The problem of downtime is increasingly seen as a financial liability, not just a technical issue. How did the founder’s personal experience with a service outage highlight this gap, and what specific steps does your model take to turn a cloud outage into a measurable, insurable risk?
It’s a powerful origin story because it’s so relatable. The founder, Mimi, simply tried to order food during a major cloud outage and saw the entire system collapse. What seemed like a minor technical glitch was, in reality, a catastrophic event for that business, leading to huge financial losses and a public relations nightmare. That experience was the spark. It crystalized the realization that while engineers treat downtime as a technical problem to be solved with more code, the CFO is left with a massive, unmitigated financial risk. Our model directly addresses this by first deconstructing the outage. We use data to define what an “outage” actually is—not as a vague concept, but as a set of measurable parameters. By tying coverage to these specific, verifiable data points, we transform a chaotic technical event into a predictable, insurable risk, giving the financial layer of a business the certainty it has been lacking.
Investors noted your approach ties insurance to how infrastructure behaves in the real world. How do you gather and verify the outage data that triggers a payout, and what makes this a more reliable method than what’s described on paper in contracts?
This is the core of our credibility. Contracts and service level agreements are just words on paper; they often describe an idealized version of reality. We, on the other hand, are focused on ground truth. We gather data from a multitude of independent, verifiable sources that monitor the real-time performance of cloud services globally. This creates a composite, objective view of an infrastructure’s health. When these trusted data sources collectively confirm that a predefined outage condition has been met, the trigger is pulled. It’s not based on a single report or a subjective claim. This data-driven consensus is far more reliable because it reflects what is actually happening in the digital world, not what a legal document promises should happen. It’s this direct link to real-world behavior that gives our clients and underwriters confidence in the model.
Looking ahead, you plan to expand as cloud and AI architectures become more interconnected. How do you foresee risks like cascading failures evolving, and what steps is Mantas taking to extend its coverage and risk intelligence to meet these future challenges?
The future is all about interconnectedness, which is both powerful and perilous. As we weave AI services into the fabric of our cloud infrastructure, we are creating new, complex dependencies. A single failure in a foundational AI model or a core cloud service could trigger a domino effect, a cascading failure that brings down entire ecosystems of applications. We are already seeing the early signs of this systemic risk. Our roadmap is built around this evolution. We are actively expanding our monitoring capabilities to map these intricate dependencies and model the potential blast radius of a failure. The goal is to extend our parametric coverage beyond simple uptime to include these more complex, multi-service digital risks, ensuring our risk intelligence and financial protection keep pace with the ever-advancing complexity of the digital world.
What is your forecast for the cloud insurance market?
The cloud insurance market is on the cusp of a major transformation. For years, it’s been a niche segment, often bundled confusingly within cyber policies. But that’s changing rapidly. I foresee it becoming a standard, non-negotiable line item in every digital company’s risk management portfolio, just like property or liability insurance. As businesses come to grips with the fact that their entire revenue stream depends on third-party infrastructure, the demand for dedicated, transparent financial protection will skyrocket. The future of this market belongs to providers who can move beyond vague policy language and offer data-driven, parametric solutions that deliver real-time intelligence and immediate liquidity. It will no longer be about arguing over losses after the fact; it will be about proactively managing and transferring a clearly defined financial risk.