Spend Guardian AI
A tightly scoped AI pilot built for Rippling Spend. Our goal: help Rippling catch more out-of-policy spend in real time, cut manual reviews, and shorten reimbursement cycles—in a 6-week experiment, with clear metrics and acceptance criteria.
Policy Drift is Expensive
Rogue spend and slow reviews quietly erode the value of Rippling Spend.
Rippling already promises to stop waste at the source, unifying cards, expenses, bill pay, and payroll in one place. Yet even the best programs see "policy drift"—a Rippling Spend post highlights that 54% of employees admit to going rogue with unauthorized purchases.
The broader picture is just as stark. The ACFE's 2024 report estimates organizations lose about 5% of revenue each year to fraud, much of it through everyday spend. At the same time, SAP Concur data shows it costs roughly $58 to process a single expense report and 19% of reports contain errors. That means policy violations and mistakes are both expensive and slow to catch.
Solution
An AI reflex layer for Rippling Spend
Real-time Detection
Dynamic Limits
Risk Scoring
Auto-Flagging
Thin AI reflex layer—no new UI, stronger guardrails, clearer signals.
Real-Time Detection Flow
Card Authorization
$2,500 catering charge
Rippling Policy Engine
Policy check: $2,000 limit
Spend Guardian AI
Policy breach + vendor anomaly
Auto-Flag
Routed to Finance Ops
Resolution
Approved with audit trail
Thin AI reflex layer • No new UI • Real-time • Full audit trail
Pilot Proposal
6 weeks to measurable proof
Acceptance Criteria
Mirroring Rippling's AI pilot approach. If the numbers aren't clearly better, we stop quickly. If they are, we scale.
Threshold: ≥ 70%
Threshold: ≥ 25%
Threshold: ≤ 1–2 weeks
Adjusted as finalized
Integration & Security
Minimal surface area, maximum control
How It Fits
Real-time decision log of key Spend events
AI risk scorer alongside existing policy checks
Feedback loop so models learn from approvals/declines
No new UI. Runs behind existing workflows. 5-line SDK integration.
Compliance & Standards
Security & Deployment
In-Tenant
Minimal Data
Audit Trail
Example & Projected Impact
Real scenario, measurable targets
The Scenario
$2,500 catering charge exceeds $2,000 policy limit. Today, caught only at expense review. With Spend Guardian AI: flagged in real time, auto-routed to correct approver, resolved same day with full audit trail.
Pilot Targets
6-week outcomes
$2,500 charge → flagged in real-time vs. days later
Time to resolution
Roadmap After the Pilot
From internal reflex to customer feature
Phase 2: Expand to Rippling's full card fleet
If the pilot clears its thresholds, the same reflex layer can monitor all internal Rippling card spend, deepening the value of Rippling Spend as Rippling's own finance team uses it.
Phase 3: Customer-facing capability
From there, Rippling can choose to surface this as a premium Spend add-on, a built-in anomaly detection setting, or a partner integration—the same AI reflexes that protected Rippling's spend, now offered to customers.