I. Introducing Atalanta
Atalanta is a legendary figure from Greek mythology. Celebrated as a world-class athlete, she was known for her speed, agility, and precision as a runner, a warrior, and a huntress. She was also a member of the Argonauts, an elite group of champions who successfully pursued seemingly impossible missions together, unlocking individual strengths through the alchemy of their combined talents.
Our company is named after our heroine. We are a mathematical AI company with a vision to bring provably correct decision-making to the world’s most important missions.
II. Back to the Building Blocks
We are confronted with a hard truth: the human experience increasingly relies on software-defined systems, and those systems are complex, interdependent, and progressively targeted by adversaries. Yet the way we manage that risk has not kept pace.
The dominant paradigm has been probabilistic. We hope that systems are likely to work, likely to be secure, likely to behave as intended. In some cases, that is sufficient. In the domains that matter most, it is not. We need a different thesis: probabilistic confidence cannot be the foundation for systems that cannot afford to fail. Instead, we must move toward provable guarantees.
If we want resilient cyber-physical systems, we must construct them with building blocks whose behavior can be mathematically verified using techniques called formal methods. Systems should not be assembled from parts we hope will work, but from parts we know will.
This vision builds on decades of foundational work. As early as 2012, DARPA’s High-Assurance Cyber Military Systems (HACMS) program demonstrated that it is possible to mathematically prove the security of complex, real-world systems. First, researchers applied formal methods to Boeing’s Unmanned Little Bird. Then, DARPA attempted to hack the helicopter while in operation, bringing in expert teams whose best efforts could not compromise its defense. The lesson was clear: formal methods work in the real world. And more importantly, they can give our warfighters trust in the systems they rely on.
We now see the emergence of verified building blocks across the stack: memory-safe programming languages, cloud infrastructure that utilizes automated reasoning, formally verified microkernels, and secure chip architectures. We are even seeing a growing number of AI companies building models for mathematical research, autoformalization, and code verification.
Taken together, these developments point in the same direction: a world where the building blocks of our systems are no longer opaque, but verifiable. It reflects a broader recognition that security must be built in from the start. These advances are meaningful progress, and continue to lay the foundation for the next seemingly impossible mission.
III. The Age of Software Understanding
If technology is constructed from building blocks, who holds the blueprint?
The systems that matter most are no longer defined by individual components, but by how those components interact. They span software and hardware, networks and physical environments, evolving over time and interacting in ways that no single team fully owns.
We have already seen what happens when that coordination breaks down. The CrowdStrike outage of 2024 was triggered by a software update that propagated across systems globally, disrupting operations at banks, airlines, and hospitals. The cascading impact of one broken component had devastating effects on the broader ecosystem.
The consequences extend beyond outages; adversaries are already exploiting the same failure mode. In cyber campaigns like Volt Typhoon, foreign actors were able to pre-position themselves inside U.S. critical infrastructure. What made this especially concerning was not just their presence, but the uncertainty it created. We did not know where else they were, or what was at risk if they chose to act.
That uncertainty introduces a deeper epistemic risk. When you cannot reason about the systems you depend on, you cannot act with confidence. And when you cannot act with confidence, you begin to cede advantage to your adversary. In this way, the inability to understand our own systems becomes a strategic vulnerability, one that puts the United States on the precipice of being deterred.
As the world becomes increasingly defined by AI, this challenge becomes even more acute. We are moving toward a future where critical infrastructure will be autonomous, run by agents that will no longer rely on humans to intervene, let alone understand. The surface area of what must be reasoned about continues to expand. In this reality, local correctness does not translate to system reliability. Each component may behave as expected, while the system as a whole remains unpredictable. Formal methods, in theory, can play a key role in navigating this AI future. However, the challenge is bringing that theory into practice.
Today, mathematical proof functions as a kind of technical scripture. It establishes what is true with precision and rigor. But scripture alone does not create a religion. If only a small set of specialists can produce and interpret this proof, they cannot guide how systems are designed, built, and operated in practice. For rigor to shape reality, it must become an organizational discipline. It must be shared, interpreted, and applied across teams responsible for different parts of the system.
Engineers, mission operators, system architects, and decision-makers must be able to reason about the same system in their own context, with a common understanding of what is true and what is at stake. This requires a way to connect proof across design, implementation, and deployment, so that guarantees hold as systems evolve.
This shift is now underway. We call this Software Understanding: a new discipline focused on making complex systems legible, verifiable, and aligned across the teams responsible for them.
IV. Argo
We define Software Understanding as the emerging science of formally verifying that software-controlled systems perform correctly across normal, abnormal, and hostile conditions. However, the missions that depend on these systems cannot afford for understanding to slow them down. The systems we rely on are evolving faster than our ability to reason about them, and the gap between what we build and what we know continues to widen. Closing that gap requires bringing mathematical proof into the pace and complexity of real-world systems.
Our flagship product, Argo, does exactly that. Argo combines AI, formal methods, and digital engineering to deliver systems that are faster, cheaper, and safer. It enables teams to move quickly by updating proofs as requirements evolve, rather than restarting from scratch. It reduces cost by identifying errors during system design where they are significantly cheaper to fix than in production. Traditional testing can show how a system performs in known scenarios, but it cannot exhaustively explore all possible behaviors. Argo goes further by systematically checking every possible state a system can reach, and reasoning about entire classes of inputs rather than individual test cases. This allows teams to prove that critical properties — such as safety, correctness, or security constraints — hold across all modeled behaviors, including adversarial or unexpected conditions.
Argo is already being used by organizations building and operating some of the most complex and consequential missions today. These include proving safety guarantees for military systems, verifying that secure architectures prevent sensitive data leakage, demonstrating the resilience of communication networks under adversarial conditions, reasoning about systems that underpin national security, and ensuring that human oversight is preserved in increasingly autonomous systems.
V. A Team of Modern-Day Argonauts
Atalanta was not alone. She chose to join the Argonauts, a group defined not by uniformity, but by the strength of their differences. Each brought a distinct superpower, and it was their combination that made the impossible possible.
Solving the problem of Software Understanding requires the same approach. This is not a problem that can be answered by software engineering alone, or formal methods in isolation. It requires a new kind of team, one that brings together talented specialists to deliver outsized results.
At Atalanta, the engineering team always starts with the mission. Forward deployed engineers work directly with customers to understand their hardest questions and most important challenges. Formal methods engineers translate those into logic and proof. Software engineers turn those proofs into decision-making workflows that can be productized at scale.
The strategy team shapes the market and policy conditions under which this work matters. Policy strategists ensure that Software Understanding becomes a sustained national priority. Growth strategists expand adoption across the ecosystem. Business operations strategists ensure that Atalanta can deliver this capability everywhere it is needed.
The design team ensures that what is proven can be understood. It is not enough to establish proofs of correctness; humans need to be able to use them. This means turning abstract guarantees into something intuitive, so more people can reason about the systems they are responsible for.
The research team ensures that Atalanta not only leads the Software Understanding category today, but continues to define what it becomes tomorrow. The discipline is evolving, and staying ahead requires advancing both how the problem is defined and how the solution is built, grounded in what is learned in the field.
These functions do not operate in isolation. They are tightly coupled through the mission. Progress comes from their coordination, from the ability to move between theory and practice, between proof and policy, between mission intent and real-world consequence.
We’re building a team of modern-day Argonauts: exceptional engineers, mathematicians, designers, and strategists committed to solving the hardest, highest-stakes problems in the world. We move fast, but we do so with discipline and logic. We think from first principles and execute with autonomy and accountability.
You may be a good fit for Atalanta if you are insatiably curious in the face of discomfort, are a coalition builder who sees progress as a positive-sum game, and believe that the alchemy of interdisciplinary expertise can solve seemingly impossible problems.
VI. Conclusion
A software-defined world does not have to be one we fear. We can hold an affirmative vision for the future, one where we harness the full potential of these systems, and where we no longer have to choose between prosperity and safety.
The missions that matter will always require reasoning about complex systems. Industrial systems are only the beginning. The same approach can be applied to biological systems to accelerate the discovery of new treatments and cures; to financial systems to build more resilient and thriving markets; and to legal systems to better encode and defend democratic values.
Software Understanding is a movement. Come join us.