Anyone can point AI at a problem.
AI in Treasury demands Clarity and Confidence.
Many AI vendors point a general model at treasury and call it done. GSmart was built from the inside out, by people who have lived inside funding cascades, concentration limits and policy controls. It's compliant everywhere you operate, auditable end to end, acts only within the limits you set, at a price that doesn’t grow just because you use it more. This is Treasury-Native AI.
Explore GSmart:
The Foundation
The Treasury-Native AI difference.
You wouldn’t tolerate a margin of error in your treasury operations. Your AI should be held to the same standard. Automation isn’t the goal. Confidence is.
No hidden costs
Predictable scale
AI adoption expands as fast as your treasury needs, without budget penalties.
No token tax
No hidden consumption costs and token constraints.
Enterprise grade
Local and compliant
In-region data residency for supported geographies.
Strict privacy
Your data stays tenant-isolated and is never used to train shared AI models.
Governance
Live policy ingestion
Outline policies and rules in Knowledge Studio to convert static policy and tribal knowledge into AI controls.
100% auditable trail
Every action cites controls and generates a full audit log.
Policy-cited agentic control
Clause-cited proposals
Agents analyze, propose actions and cite the exact policy clause behind every move.
Adaptive learning
Approvals and overrides tighten your controls each cycle, not the model.
Built in, not bolted on
Treasury is the foundation. The AI was built on it.
AI’s only as effective as the data powering it.
With decades of experience in simplifying complex data structures, our AI is architected with a data foundation purpose-built for treasury.
GSmart is built by treasury experts who know which exceptions are routine, which ones need a human, and which ones should never be automated at all.
You govern the AI.
GSmart AI operates within the policy limits you set in Knowledge Studio. They propose, cite the exact clause, and wait for human approval before execution.
The Governance Layer
One foundation. Every agent checks itself against it.
Upload your policy documents, or outline the rules directly, and Knowledge Studio turns static policy and tribal knowledge into the governance and protocols that AI works within. When an agent proposes a move, it cites the exact clause behind it, not a paraphrase, not a summary. Nothing executes without approval, and every action is logged for audit.
The Agents
A growing stable of orchestrated agents.
GSmart agents span forecasting, risk, liquidity, reconciliation, reporting and more. All of them work within your operational parameters: it proposes a move, cites the exact policy clause behind it and waits for your approval.
Analytics Studio
Ask any question. Build any report.
Ask three people in your organization to define “cash position” and you’ll often get three slightly different answers, each one technically defensible, each calculated a little differently. Analytics Studio runs on one governed treasury data warehouse, so every report, dashboard, and formula pulls the same number, the same way, every time. Build a new report yourself, no SQL, no IT ticket, and with Ask GSmart, just ask for it in plain language. When it answers a question nobody built a report for, it’s grounded in that same governed data, not guessing at what “cash” means on the fly.
Related resources
See what Treasury-Native AI actually looks like
One policy foundation. Every action is one you approve.
FAQ
Common questions about GSmart and treasury-native AI
Treasury-native AI is AI built from inside treasury operations, by people who have worked funding cascades, concentration limits, and covenant triggers directly, rather than a general-purpose AI model pointed at treasury data after the fact. GSmart is Ripple Treasury's treasury-native AI: every agent proposes an action, cites the exact policy clause behind it, and waits for your approval before executing.
GSmart is built by treasury practitioners, not a general-purpose AI model adapted for finance. Every agent operates inside the policy limits you set in Knowledge Studio, proposes an action, cites the exact policy clause behind it, and waits for human approval before it executes.
GSmart is available in every region of the platform, so storage and AI processing follow your business operating needs. Client data is encrypted in transit and at rest, stays in your selected region, and is never used to train models.
No. Your data stays tenant-isolated and is never used to train shared AI models.
GSmart evaluates and matches the best-suited model to each specific task, continuously, rather than routing every request through a single model. That reduces the single-point-of-failure hallucination risk that comes with relying on one model for every kind of financial decision.
Yes. Every agent action cites the exact policy clause behind it and logs a complete, auditable trail, so nothing an agent proposes or executes is a black box.
Every agent proposes an action and cites the policy clause behind it, then waits for human approval before executing. You set the autonomy limits in Knowledge Studio.
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