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.
Gayathri has spent 15+ years inside investment operations at Goldman Sachs, Citco, CIBC, and Citadel, not observing it but running it. She has built and led corporate actions, FX settlement, and middle office teams through periods of rapid scale, regulatory change, and post-crisis recovery.
The pattern across her career is consistent. She takes broken or underperforming processes, understands them deeply, and fixes them at the root. At Goldman Sachs, this meant reducing exceptions by 98% in six months and delivering over $2B in capital savings through a large-scale client account integration. At Citco, this included heading the Toronto Middle Office and successfully managing some of the firm's most complex clients, driving strong outcomes in risk and operational efficiency. At CIBC, she led operational risk programs, including board reporting and enterprise-wide risk initiatives, strengthening risk visibility and decision-making. At Citadel, she led data-driven workflow redesigns that reduced reliance on manual exception handling.
Having worked across a wide span of Operations teams, she developed a firsthand understanding of the challenges that drive operational risk, inefficiency, and manual effort across financial institutions. She repeatedly saw talented teams spending countless hours investigating exceptions, chasing breaks, and piecing together information across fragmented systems.
Those experiences shaped a simple belief: AI should help people make better decisions. The future of Operations is not replacing human expertise, it is augmenting it. Humans bring judgment, context, and experience. AI brings speed, scale, and pattern recognition.
Gayathri founded Vajra to combine the strengths of both, helping Operations teams identify issues faster, understand root causes sooner, and focus their time where human expertise creates the most value.
LinkedInWe're onboarding select investment operations teams now. Tell us about your environment.