The situation
A mid-size trucking company operating a fleet of more than 2,000 tractors and trailers across regional and long-haul lanes had invested in AI for dispatch optimization, spot pricing, and driver productivity management. Asset data was fragmented: the same tractor appeared under a different identifier in the maintenance platform, the ELD system, the DOT compliance database, and the TMS. Maintenance models were producing outputs the team wanted to act on, but maintenance history in one system did not reconcile with utilization records in another.
The compliance team had asked a specific question about the dispatch AI running for eight months: were its decisions consistent with the company’s routing policies and hours-of-service constraints? The organization needed to answer that question with documented evidence.
The solution
The Knowledge Fabric™ connected the maintenance platform, ELD, DOT compliance database, and TMS. Entity resolution matched every tractor and trailer across four system identifiers into one canonical Trust-Scored record. Every reading, record, and compliance status carries a PolyPhaze Trust Score™ and full lineage.
The Asset Twin capability built the live digital model of the fleet. Predictive maintenance runs against Trust-Scored condition data and maintenance history. DOT inspection compliance is tracked continuously. Component histories follow parts across asset assignments.
The Optimize capability surfaced route profitability by lane and load type, driver productivity, fuel cost, detention recovery opportunities, and equipment utilization ranked by P&L impact daily.
AI Decision Trace established a governed baseline for dispatch, pricing, and driver assignment. The Coverage Discovery capability detected that the dispatch AI was making routing decisions without human review 94% of the time — above the 60% pre-AI baseline — and had been operating this way for eight months without triggering any internal review flag.