Freight analytics are only as good as the data underneath them. Orca Analytics runs on invoice-level data that has already been normalized, matched to shipments, and audited, so every dashboard reconciles to what was actually paid.
The same foundation serves the CFO asking what freight cost as a percentage of revenue, the transportation director asking which carrier missed service on which lane, and the analyst asking why accessorials rose last quarter.
Executives get summary views and trends they can take to the board. Finance gets reconciliation-grade detail that ties to the GL. Transportation gets lane, carrier, and exception analysis they can act on this week. All three look at the same numbers.
An invoice-level exception is a symptom. Orca Analytics groups exceptions by cause, such as a carrier applying the wrong fuel table, a facility shipping in oversized cartons, or a contract that never captured a surcharge cap, so the fix happens once instead of every invoice.
Orca's AI capabilities, including anomaly detection and the ORI analyst agent, run on the same audited data model. Clean, connected freight data is the prerequisite for any useful AI in transportation, and Analytics is where that data becomes visible.
Book a strategic demo and Orca will walk through executive, finance, and operations views built from your own carrier data.
