Vajra reconciles positions, transactions, and balances across equities, bank debt, cash, derivatives, corporate actions, and private assets - classifying breaks and proposing resolutions - with 95% handled autonomously at onboarding, scaling toward 99% at full maturity.
Reconciliation has always been treated as a matching problem. But in practice, matching is not where teams spend their time. The real work begins when things do not match.
Exceptions are where operational cost accumulates, where risk hides, and where human effort is concentrated.
Yet most systems are designed to optimize matching rates, not to understand or resolve the underlying causes of exceptions.
Vajra is built differently.
Instead of focusing on matches, we focus on exceptions. We identify them, understand their root causes, resolve them through intelligent workflows, and ultimately reduce their occurrence over time.
The goal is not better reconciliation. The goal is better risk adjusted decisioning.
Vajra reconciles across any asset class - as long as we can get the data. The examples below reflect common starting points, but coverage expands with each new data source connected.
Vajra's decision layer replicates how a skilled analyst approaches reconciliation - matching positions and transactions, classifying breaks by type and cause, and proposing ranked resolutions. At onboarding, ~95% of breaks are handled deterministically and autonomously. As Vajra learns the client's patterns, that number moves toward 99% - leaving only true edge cases for human review.
Every break is scored by a composite model across three signals. The score determines whether a notification fires instantly, joins the next batch, or waits for EOD review.
Vajra reflects 15+ years of experience leading and transforming investment operations at scale.
Most reconciliation breaks are not mysteries. They are patterns.
After more than 15 years leading operations, transformation, and risk initiatives at Goldman Sachs, Citco, and Citadel, Gayathri Kalyansundar saw those patterns emerge across every part of the industry - from corporate actions and asset servicing to prime brokerage, fixed income, and middle office operations.
Throughout her career, she worked at the intersection of operations, technology, and risk, leading teams responsible for critical processes that support the daily movement of capital, securities, and information across global markets. Regardless of the product, platform, or institution, the challenge was often the same: highly skilled teams spending countless hours investigating issues that followed predictable paths.
The industry became exceptionally good at reporting problems after they occurred. It remained far less effective at identifying and resolving them before they escalated.
That observation became the foundation for Vajra.
Gayathri founded Vajra on a simple belief: operational teams should spend less time chasing exceptions and more time solving meaningful problems. After years leading operations across global financial institutions, she recognized that many breaks follow consistent patterns. Vajra applies that insight to help firms detect issues earlier, streamline investigations, and improve operational efficiency at scale.
Today, Vajra is building technology that helps operations teams move beyond identifying breaks to understanding why they occurred and how to resolve them faster. By combining deep operational expertise with intelligent automation, Vajra helps firms prioritize what matters, reduce investigation time, and strengthen operational resilience.
LinkedInDevin is an AI researcher and technology leader working at the intersection of data science, software engineering, and data architecture. Across more than two decades in both the public and private sectors, he has led teams building systems where the cost of getting it wrong is high. He is a vocal advocate for the centaur model - humans and AI working in partnership, each doing what the other cannot.
He served as acting CTO for the Special Operations Joint Task Force in Afghanistan, where engineering work had to hold up under live operational pressure. In the private sector, he led data center infrastructure management at Emerson and spent four years at Goldman Sachs in financial technology, running a distributed team that rebuilt asset servicing systems - lifting productivity and reducing operational risk across large-scale financial operations through process automation, cross-functional partnership, and platform-level data architecture.
Devin is a platform evangelist who invests heavily in mentoring and developing technologists, with a particular focus on connecting engineering decisions to the business outcomes they serve. He holds a Master of Science in Information Technology and has delivered solutions for multiple Fortune 500 companies. A Veteran of both the Marine Corps and Army, he brings firsthand understanding of the constraints and culture of high-stakes technology environments.
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