OnCorps AI Raises $55M to Scale Agentic Fund Operations
OnCorps AI has secured $55 million in growth capital from Long Ridge Equity Partners. The funding will accelerate product innovation, expand go-to-market, and strengthen infrastructure for a growing customer base across asset management and fund administration.
Founded in 2011, OnCorps AI focuses on intelligent exception resolution across fund operations. The platform doesn't just flag discrepancies in trade reconciliations and fund documentation-it investigates root causes, recommends fixes, and learns from patterns to prevent repeats.
"We've been running complex AI solutions in production for the world's largest asset managers for several years, giving us a meaningful advantage in delivering tangible returns from AI," said Bob Suh, Founder of OnCorps AI. "Every day, our systems close exceptions, reconcile trades, and oversee trillions in assets with high accuracy and low error rates. With Long Ridge's investment, we can expand these capabilities across the broader industry."
OnCorps' specialized AI agents are trained on millions of real exception cases across trade processing, fund accounting, and reporting workflows. With continuous learning in production, the system targets higher accuracy and faster resolution than generic automation tools.
Operating margins have tightened for asset managers due to higher volumes, tougher regulatory requirements, and fee pressure. Institutions like PIMCO and GMO-part of a group representing $13 trillion in assets-are using OnCorps AI to cut manual effort and streamline processes.
As part of the investment, Long Ridge Managing Partners Jim Brown and Kevin Bhatt will join the OnCorps AI Board of Directors. "OnCorps AI has built the market-leading AI platform for fund operations," said Jim Brown. "Their production-ready agents are trained on domain-specific data and workflows, delivering immediate operational value."
The company plans to expand into adjacent capital markets workflows where similar challenges exist, including cash management, regulatory reporting, and complex trade processing.
Why this matters for operations
- Fewer breaks and faster exception closure across trade, NAV, and reporting workflows.
- Proactive prevention through pattern learning, not just after-the-fact remediation.
- Stronger auditability with agent-driven lineage and explainability for each action.
- Improved SLA adherence and lower cost-to-serve without adding headcount.
- Better risk control via consistent application of playbooks across teams and regions.
What to watch
- Integration model: how agents connect to reconciliation tools, fund accounting systems, and data lakes.
- Data privacy and entitlements: field-level controls, PII handling, and segregation by client or region.
- Model governance: versioning, drift monitoring, approvals, and rollback plans.
- Change management: clear runbooks, exception taxonomies, and routing rules to avoid ticket ping-pong.
- Measurement: baseline KPIs (break rates, mean time to resolve, touches per exception, aged items) and target deltas.
- Time-to-value: pilot scope selection and data readiness often determine success more than model choice.
Practical next steps for ops leaders
- Map your top exception hotspots across reconciliation, corporate actions, and fund reporting.
- Define success metrics up front: reduction in aged breaks, touch reduction, and cycle-time targets.
- Start with a narrow pilot where you have labeled exceptions and clean reference data.
- Align early with Risk and Compliance on agent permissions, supervision, and audit trails.
- Build standard playbooks: data sources, decision rules, escalation paths, and closure criteria.
- Plan handoffs: who approves agent recommendations and who owns exceptions outside thresholds.
Need a quick overview of practical AI tools used in finance operations? See this curated list: AI tools for finance.
For teams preparing for expanded reporting requirements, review the SEC's guidance on Form N-PORT as a reference point for data structure and frequency.
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