AI-native reconciliation built for what happens next.
Vajra goes beyond identifying differences. It investigates the underlying transactions, surfaces what caused the exception, and helps drive resolution across every asset class.
The Industry Optimized Detection.
We Built Resolution.
*Vajra’s AI can achieve these rates through training on firm-specific data, workflows, and historical resolution patterns. These rates have already been demonstrated in testing.
"Reconciliation is not a matching problem; it is a trust and exception problem. Cost and risk live in the gaps where automation ends and manual intervention begins."
Vajra resolves the mismatches that others flag as 'too complex.' By processing the context of each transaction, we turn manual operations into high-integrity oversight.
Coverage
Six asset classes today, each with its own document formats, counterparties and failure modes.
Equities
Trade, position, and cash breaks matched against custodian and prime broker records, resolved through multi-point comparison logic.
Commodities
Physical trade documentation, off-take notices, and grade/quality certificates parsed at the document layer and verified against arbiter sources.
Corporate Actions
Lifecycle events parsed from notices and filings, with manual elections captured and reconciled against entitlement records.
OTC Derivatives
Counterparty valuations compared line by line, with deltas explained through underlying trade and market context.
Private Assets
Unstructured GP reports for PE and credit parsed at the document layer and reconciled against internal capital account records.
Bank Debt
Facility positions, paydowns, and interest accruals reconciled from lender notices and agent bank statements, verified against internal ledger history.
Resolution Logic
Ingest raw data fragments from any source, including unstructured emails and complex PDFs.
Identify break types using multi-point comparison logic that mirrors buy-side operational workflows.
Resolve mismatches autonomously by cross-referencing against internal ledger history.
Exception Priority
Ready to automate the exceptions desk?
Notes from the team
A $105,900 break and the limits of general AI in enterprise finance
A single $105,900 break highlighted why general AI falls short in enterprise finance.
September 9, 2026Designing an Exception Queue Operations Teams Can Actually Trust
Operations teams are accountable for every sign-off. Exception handling must be inspectable, risk-weighted, and continuously improving.
September 2, 2026What Document-Level Context Actually Means in Financial Reconciliation
In complex financial operations, the authoritative record is a document. Extracting numbers without reconciling them to their operational context creates clean data that matches nothing.
Get in touch
hello@vajraops.aiToronto, Canada