GSmart: Treasury-Native AI
Your treasury day, already run. Inside the policy you set.
Settlement went from three days to five minutes, so treasury now runs on the moment. GSmart agents read statements, reconcile accounts and fund shortfalls before your team logs in, and you decide how much runs on its own, workflow by workflow. Built by people who have lived inside funding cascades and concentration limits, GSmart is available in every region you operate, auditable end to end and priced so using it more never costs more. This is Treasury-Native AI for always-on corporate treasury.
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 for its own sake isn't the goal. Closing the distance between deciding and moving is, with controls that run as continuously as your cash does.
No hidden costs
Predictable scale
AI adoption expands as fast as your treasury needs, without budget penalties.
No token tax
No hidden consumption costs or token limits.
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, at every autonomy setting, cites its controls and is traced end to end.
Policy-cited control, at the autonomy you choose
Clause-cited ACTIONS
Agents analyze, propose or act at the setting you choose, and cite the exact policy clause behind every move.
Earned Autonomy
Your approvals and overrides tighten your controls each cycle, not the model. When a workflow's approval record shows it's ready, GSmart offers to let it run on its own.
Always-On Corporate Treasury
AI is collapsing the time it takes to decide.
Adaptable solutions gave treasury its foundation. GSmart collapsed the time it takes to decide, inside your policy. Digital rails let money move when the decision is made. The orchestration layer means the decision gets made at any hour.
Tied together by the orchestration layer
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 four decades of experience simplifying complex treasury data, GSmart runs on a data foundation purpose-built for treasury.
GSmart is built by treasury experts who know which exceptions are routine, which need a person and which should never run on their own.
You govern the AI.
GSmart works within the policy limits you set in Knowledge Studio. You choose how much each workflow runs on its own: Manual, Semi-autonomous or Fully autonomous. Raising autonomy changes who clicks, never what is permitted.
The Governance Layer
One foundation. Every agent checks itself against it.
Outline your policy and rules directly, and Knowledge Studio turns static policy and tribal knowledge into the controls every agent works within. Whenever an agent proposes or acts, it cites the exact clause behind the move, not a paraphrase or a summary. Policy checks run on every action at every setting, agents step back to a person when something falls outside what they've seen, and every action is logged for audit.
The Agents
Forecasting, liquidity, risk and reconciliation, working as one operation.
GSmart agents span forecasting, risk, liquidity, reconciliation, reporting and more. The orchestration layer runs them across every solution against your policy, so the forecast feeds the liquidity decision without anyone joining the steps by hand. Each agent works at the autonomy you set and cites the exact policy clause behind every move.
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 with no SQL and no IT ticket, or ask GSmart for it in plain language.
Related resources
See what Treasury-Native AI actually looks like
One policy foundation. You decide how much runs on its own.
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 works inside your policy, cites the exact clause behind each action and runs at the autonomy you set.
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 and cites the exact clause behind each action. The orchestration layer runs those agents across every Ripple Treasury solution, so decisions get carried out, not just recommended, with one audit trail.
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, at every autonomy setting, cites the exact policy clause behind it and logs a complete, auditable trail. Nothing an agent proposes or executes is a black box.
You decide, workflow by workflow. At Manual, agents prepare every move and nothing happens until a person approves it. At Semi-autonomous, agents act inside your guardrails and report in real time. At Fully autonomous, agents run the workflow end to end and your team checks in when it wants. Raising autonomy changes who clicks, never what is permitted.
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