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Optimal Dynamics

Optimal Dynamics provides an AI-driven platform that automates load planning and dispatch decisions for trucking operations, enhancing asset utilization and operational efficiency. By leveraging advanced machine learning algorithms, the platform enables users to make data-driven decisions, resulting in measurable increases in loaded miles, fleet loads moved, and revenue per truck.

East New York, United StatesFounded 20177210K+ followers
Updated 20 months ago

Funding

$57.9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

TW
Funding rounds are not available yet.

Founders

Product

Problem

Trucking companies face complex, rapidly changing conditions when planning and executing load assignments, leading to inefficiencies, missed revenue opportunities, and reliance on guesswork. Existing load matching systems often lack the sophistication to account for downstream impacts and uncertainty, resulting in suboptimal decisions.

Solution

Optimal Dynamics offers an AI-powered platform that automates and optimizes load planning and dispatch decisions for trucking operations. The platform uses CORE.ai, a proprietary technology that leverages advanced machine learning algorithms and complex value analysis to simulate the impact of changes to operations and performance. By breaking down the barriers between planning and execution, the system enables users to make data-driven decisions, manage uncertainty, and improve asset utilization across their network. The platform provides dynamic automation for real-time load and dispatch decisions, eliminating reliance on gut feelings and adapting to new conditions.

Target Audience

The primary target audience includes trucking executives, analysts, load planners, and dispatchers seeking to improve asset utilization, increase revenue, and optimize their freight network.

Features

  • CORE.ai technology uses complex value analysis and over 20 iterations of scenario machine learning.
  • Digital twin environment to simulate the impact of operational changes.
  • Dynamic load planning and dispatch automation.
  • Ability to evaluate bidding decisions and understand downstream impacts.
  • Real-time matching of drivers with loads.
  • Integrates with existing tech stacks, including McLeod Software, Samsara, and Trimble.
  • Planning visibility up to 3x further into the future.
This profile is AI-generated and may contain inaccuracies.