Pricing is one of the most significant operational levers in logistics. In August 2024, Sherpa Auto Transport deployed what is now RoxStart's AI-powered pricing engine. Within 12 months, EBITDA grew from approximately $300,000 to approximately $1.12 million. Net revenue per dispatch increased 18.1%. Rebate spend declined 92%. The results demonstrate that more precise pricing can strengthen financial performance without increasing operational complexity.
Pricing as a Profitability System
Most logistics companies think about growth in terms of volume. More quotes, more bookings, more shipments, more revenue. Those metrics matter, but they do not tell the full story. A transportation business can grow revenue while still leaving margin on the table. It can book more jobs while relying on incentives to close them. It can move more volume while increasing the cost required to serve that volume.
Pricing sits underneath all of those outcomes. A strong pricing system determines how much revenue the company keeps after carrier costs, customer incentives, transaction fees, and operational overhead. It shapes the economics of digital sales channels and influences whether higher-cost payment types are viable. It affects cancellations, customer commitment, and the ability to scale without adding more human intervention to every transaction.
For decades, many transportation companies have priced reactively. Teams look at current market conditions, draw on institutional knowledge, and make the best decision available in the moment. That approach becomes harder to optimize as volume grows and market conditions change faster.
"Some rely on overly complicated spreadsheets and outdated formulas that can take hours or days to provide a basic quote with questionable reliability."
No team can manually evaluate every historical job, every route pattern, every customer behavior signal, and every cost factor across thousands of transactions. The data exists, but it is difficult to turn into consistent pricing decisions at the pace of daily operations. Machine learning changes that process. Instead of treating pricing as a one-time judgment call, a model can learn from past performance, adjust to current conditions, and improve as more jobs move through the system.
From Reactive Pricing to Pricing Intelligence
Sherpa Auto Transport is a national vehicle brokerage company that connects consumers with carriers across the United States. As the company grew, pricing became increasingly central to profitability and scale. Before August 2024, Sherpa's pricing model was largely reactive. Each shipment was priced using current market data and operational judgment. While effective at smaller scales, this reactive approach made it increasingly difficult to optimize margins consistently as shipment volume expanded.
As a solution to this problem, Sherpa Auto Transport deployed what is now RoxStart's AI-powered pricing engine. This resulted in more accurate, reliable pricing delivered instantly, reducing reliance on manual intervention while improving operational efficiency. By combining historical data with real-time market conditions and operational variables, the pricing engine generated data-driven pricing recommendations that supported more consistent, scalable pricing across the business.
Beyond improving pricing accuracy, the platform identified opportunities to strengthen margins across routes and transaction types without increasing cancellation rates. As additional shipments were completed, the AI continuously learned from pricing outcomes and operational performance, further refining future recommendations.
Results
EBITDA increased 3.73x
Sherpa increased EBITDA from approximately $300,000 in 2024 to approximately $1.12 million in 2025. Although the company's improved performance was driven by multiple operational initiatives, the AI-powered pricing engine served as a key contributor to the overall results. The achievement came from multiple sources working together: higher net revenue per dispatch, lower rebate spend, more online volume, and stable cancellation rates.
Net revenue per dispatch increased 18.1%
Average net revenue per dispatch rose from $231 to $273 after implementation. That increase outpaced gross revenue growth, which rose 13.1%. Sherpa's net revenue improved faster than gross revenue, which indicates stronger margin quality. The business was keeping more of the revenue it generated.
Rebate spend declined 92%
Before implementation, Sherpa spent approximately $10.28 per dispatch on rebates. After implementation, that figure fell to $0.83. Prior to implementation, Sherpa often relied on rebates to adjust pricing after a quote had been presented. After implementation, sales agents had the tools and confidence to adjust pricing up front, reducing the need for post-sale rebates.
Fully online sales doubled
Fully online sales increased from 8.9% to 18.2% of total sales. In 2025, Sherpa completed 4,650 online bookings compared to 3,898 in 2023. By March 2026, online bookings had reached 26% of total sales. With greater confidence in pricing accuracy, Sherpa was able to present online quotes for approximately 80% to 85% of quote requests.
Cancellation rates remained stable
One of the most important findings is what did not happen. Sherpa increased pricing and improved profitability, but cancellation rates stayed within the same 10% to 13% range. Customers continued to book and follow through at the new price levels, indicating that the pricing model identified opportunities without a corresponding decline in customer demand.
What the Data Shows
Higher EBITDA shows bottom-line impact. Increased net revenue per dispatch shows better unit economics. Lower rebate spend shows greater accuracy at the point of sale. Online sales growth shows improved scalability. Stable cancellations show that Sherpa captured more value without weakening customer commitment.
Better pricing strengthened margins.
Stronger margins made higher-cost transaction types more viable.
More accurate quotes reduced rebates.
Greater pricing confidence supported online sales.
Online sales lowered cost-to-serve.
Stable cancellations preserved demand.
RoxStart is building a proprietary pricing asset that becomes more defensible the longer it operates.
From Reactive Pricing to Predictable Growth
The Sherpa deployment demonstrates how transportation businesses can operate differently. Pricing no longer has to be a reactive process driven by current market conditions and historical instinct alone. With the right intelligence layer, every completed shipment becomes an opportunity to improve the next decision.
Transportation companies generate enormous amounts of operational data, yet much of it remains underutilized. The businesses that turn that information into faster, more consistent, and more profitable decisions will be better positioned to compete in an increasingly data-driven industry.
Sherpa's results provide an early example of what that future can look like. RoxStart is building that same intelligence across the operational decisions that shape growth, profitability, and long-term competitive advantage throughout the logistics industry.
The financial and operational metrics presented in this case study are based on internal operating data from Sherpa Auto Transport, comparing key performance indicators before and after deployment of the AI-powered pricing engine. The AI-powered pricing engine featured in this case study is part of RoxStart's broader platform designed to help freight brokers and carriers improve operational performance through AI.
The same AI that drove 3.73x EBITDA for Sherpa is available for your operation.
RoxStart builds agentic AI for the 99% of trucking companies that enterprise software left behind. Start with RoxVault for compliance, or talk to us about pricing intelligence for your business.