Optimal Dynamics has launched Scale, a new AI-based platform designed to help trucking carriers identify and secure freight that best fits their networks.

The company describes Scale as a “decision-native” system that combines network optimization with autonomous software agents that search for and secure freight in real time. The system was built to continuously understand the carriers’ entire operational network.

Optimal Dynamics says it targets a challenge carriers face earlier in the freight lifecycle — when customer service representatives decide whether to accept or reject loads.

“There was a key issue that we constantly saw within our customer base that was persistent, and our products didn’t solve as well as we wanted to. And so that was really the catalyst of Scale, which is revenue pressure. Just really, active acceptance imbalances didn’t begin at the dispatching. They actually began further upstream,” said Daniel Powell, CEO of Optimal Dynamics, during a press conference at the Trucload Carriers Association’s annual convention in Orlando.

According to the company, those early freight acceptance decisions are often made without full visibility into network conditions, leaving dispatch and optimization tools to correct problems later.

Scale is designed to address the issue, Powell said, claiming Scale could help fleets reduce deadhead miles, protect service commitments, and grow revenue while maintaining network balance. The system continuously analyzes a carrier’s network to identify where capacity shortages or imbalances may occur.

Scale runs continuously in the background, modeling a carrier’s entire network so it can identify where trucks, drivers, and freight may be short or long in the days ahead. When a customer service representative receives a load offer, the system evaluates that freight in context — considering downstream impacts such as deadhead miles, driver home time, and overall tour profitability — before deploying software agents to act on the carrier’s behalf. Those agents can negotiate rates, search load boards and existing customer channels for complementary freight, and adjust appointments, turning what would otherwise be yes-or-no decisions into network-aware booking and execution.

During the press conference demonstration, Optimal Dynamics showed Scale handling an email tender for a New York–Chicago load that looked profitable on its own.

Instead of simply saying yes, Scale evaluated the offer against the carrier’s network and the driver’s need to get home. It revealed that taking the load as-is would force a long empty reposition and cut end‑to‑end profit roughly in half. This is when the agents tried to renegotiate the rate and searched for a backhaul, assembling a round trip that protected service, avoided extra deadhead, and delivered higher total profit than it would, had the offer been just accepted from the get-go.