Trucking & Logistics
Driver payments were calculated using information scattered across multiple operational systems. Billers had no simple way to cross-check every payment, allowing approximately $2M in potential annual overpayments to go undetected. We built an agentic AI product that reviewed the underlying data, identified suspicious payments, and explained each discrepancy before the biller released the payment.
Users
Billers & operations teams
Before
fragmented records, limited review
After
flagged before payment
The information needed to verify a driver's payment lived across different operational data sources. Reviewing every record by hand was impractical, so discrepancies could pass through the payment process without being caught, sometimes not until after the driver had already been paid.
The problem wasn't missing data. It was connecting the data early enough to act on it.
Before payments were released, the product analyzed the relevant records across the company's data sources and compared them for inconsistencies. Instead of asking billers to investigate every payment, it generated a focused report: which payments looked suspicious, the estimated overpayment amount, what created the discrepancy, which records supported the finding, and what the biller should check before approving payment. The biller stayed responsible for confirming the issue and deciding whether to adjust it.
01
Collect the operational and payment data associated with each driver payment.
02
Specialized agents compare the records and evaluate whether the payment matches the underlying activity.
03
Potential duplicates, mismatches, or unsupported amounts are separated from normal payments.
04
Each flagged payment includes the suspected issue, estimated amount, and supporting evidence.
05
The biller confirms, dismisses, or corrects the exception before releasing the payment.
01
The product prioritized potentially incorrect payments so billers could focus their attention where it mattered most.
02
A dollar amount alone wasn't actionable. Each exception showed why it was flagged and which records supported the finding.
03
The workflow surfaced discrepancies before money went out, while billers could still investigate and correct them.