Baton

Supply chains rarely collapse in dramatic fashion. They decay through friction. A delayed truck outside Columbus. A warehouse crew running 2 hours behind schedule. A dispatcher staring at 11 browser tabs while a driver waits unpaid in a parking lot that smells like diesel fuel and exhaustion. Freight does not fail elegantly. It fails through accumulation, through thousands of tiny inefficiencies stacked on top of each other until entire networks start moving like shopping carts with busted wheels. Baton was built to attack that problem directly.

Founded in 2019 by Andrew Berberick and Nate Robert, Baton began as a San Francisco logistics technology startup focused on transportation network optimization. Ryder acquired Baton in 2022, transforming the company into “Baton, a Ryder Technology Lab,” a Silicon Valley-based innovation group focused on AI-powered logistics and supply chain infrastructure. Andrew Berberick and Nate Robert now serve as co-Chief Product and Technology Officers for Ryder’s supply chain and dedicated transportation businesses, with a mandate that sounds simple until you understand the operational weight behind it: modernize freight planning, execution, visibility, and optimization inside one of North America’s largest transportation ecosystems without slowing operational throughput in the process.

That mission matters because freight remains one of the largest operational blind spots in the modern economy. Software transformed finance, advertising, media, and cloud infrastructure decades ago, while logistics still runs on fragmented systems, delayed information, phone calls, spreadsheets, and operational muscle memory. Entire sections of the freight economy still resemble a group project held together by caffeine and panic.

About Baton

Baton operates as Ryder’s internal technology and product innovation lab focused on transportation and supply chain systems. The company develops customer-facing software and operational intelligence tools designed to help shippers and carriers plan, execute, monitor, and optimize freight movement more efficiently. Before Ryder acquired the company, Baton built a reputation inside freight technology circles for taking an unusually operational approach to logistics software.

The founders did not stay confined to conference rooms building abstract optimization models. Baton operated trucks, built drop-zone experiments, and spent time at truck stops observing where freight systems actually break under real-world pressure. That distinction matters because a large percentage of enterprise logistics software gets designed far away from actual freight movement. Baton approached the market differently by treating operational reality as product infrastructure. Drivers waiting at congested docks, dwell times eating profitability, and urban congestion creating cascading delays became direct inputs into product development rather than abstract data points inside executive dashboards.

The company’s “drop-zone” model became one of its defining operational experiments. Baton established freight transfer locations near urban centers where long-haul drivers could quickly swap trailers while localized operations handled final delivery routes. The result reduced detention time, improved asset utilization, and created cleaner scheduling patterns for drivers. Under Ryder, Baton’s role expanded significantly, with its software and data infrastructure now sitting much closer to enterprise-scale freight operations connected to Ryder’s broader transportation network.

Why Baton Matters Right Now

The timing behind Ryder’s Baton acquisition reflects a broader structural shift happening across logistics and enterprise infrastructure. Supply chains entered the post-2020 era exposed, brittle, and technologically uneven. Pandemic disruptions revealed how dependent global commerce remains on fragmented transportation systems with poor real-time visibility and limited automation, while AI infrastructure matured fast enough to move beyond theoretical demos into operational deployment. That combination changed the economics of freight technology.

Transportation companies can no longer compete solely through fleet size or procurement leverage. The competitive edge increasingly comes from data orchestration, predictive planning, operational automation, and decision-making speed. Logistics companies now need software capabilities that resemble modern cloud platforms more than legacy transportation management systems. Ryder clearly recognized that shift. Rather than treating technology as an outsourced support function, Ryder embedded Baton directly into its long-term operational strategy.

The company positioned Baton as a dedicated Silicon Valley innovation lab tasked with developing customer-facing technologies capable of digitizing and optimizing freight networks at scale. That strategy carries broader implications for enterprise infrastructure markets because traditional industries spent years assuming Silicon Valley would eventually “disrupt” them from the outside. Increasingly, large incumbents are importing startup DNA internally instead. Baton represents that model in its purest form: startup operators embedded directly into enterprise infrastructure with access to live operational data, large-scale customer networks, and long-term deployment pathways. The result is less theatrical than startup disruption narratives usually promise, but potentially far more durable.

The Problem Baton Is Solving

Freight inefficiency compounds quietly but expensively. A driver sitting idle at a warehouse dock creates downstream scheduling delays. Missed delivery windows create warehouse congestion. Inaccurate forecasting creates underutilized routes. Fragmented planning systems reduce visibility across transportation networks. Multiply those problems across thousands of shipments, facilities, and operational handoffs, and logistics networks start leaking both money and time continuously.

Baton’s broader thesis centers on reducing operational waste through integrated planning, execution, and optimization systems. The company describes its vision as enabling “supply chain on autopilot,” though beneath the branding language sits a very practical objective: reduce manual coordination, improve freight visibility, optimize routing decisions, and automate operational workflows that still rely heavily on human intervention. That is where AI becomes operationally relevant rather than performative.

Most enterprise AI conversations still orbit productivity theater. Freight is different because optimization improvements produce measurable operational outcomes quickly. Reduced dwell time improves asset utilization. Better routing reduces fuel consumption. Improved forecasting lowers scheduling volatility. Small efficiency gains inside logistics systems create meaningful economic leverage at scale. The freight industry does not care whether software sounds impressive in a keynote presentation. It cares whether trucks move faster.

Leadership and Team

Andrew Berberick and Nate Robert remain central to Baton’s strategic identity inside Ryder. According to Ryder’s public announcements, both founders continue leading Baton while serving as co-Chief Product and Technology Officers for Ryder’s supply chain and dedicated transportation businesses. That continuity matters because logistics technology often fails when operational understanding gets separated from product development.

Baton’s culture still reflects its startup origins despite operating inside a major enterprise environment. The company describes itself as a team of technologists and supply chain experts focused on eliminating waste across freight networks. Employees work across software engineering, machine learning, operational systems, and transportation infrastructure problems simultaneously. That interdisciplinary structure is increasingly important across industrial AI markets because sophisticated logistics systems require engineers who understand both distributed software architecture and physical operational constraints.

Elegant code means very little when freight delays start rippling through warehouse schedules and transportation networks. Baton appears to prioritize builders comfortable operating close to operational reality rather than purely abstract technical environments, which increasingly separates operational AI companies from firms building software detached from deployment environments.

Why Hiring Momentum Matters

Baton’s hiring activity signals something larger than simple headcount expansion. The company continues hiring across engineering, machine learning, backend infrastructure, and product roles tied directly to freight optimization systems and real-time operational software. That demand reflects how transportation companies increasingly view software infrastructure as a competitive requirement rather than a support function.

Logistics historically struggled to attract elite engineering talent compared to consumer internet or enterprise SaaS companies. That dynamic is shifting because modern freight systems now generate massive operational datasets, require low-latency infrastructure, and create opportunities for applied AI deployment at meaningful scale. Engineers working on transportation systems increasingly influence physical economic activity directly rather than optimizing digital engagement metrics inside advertising platforms.

Infrastructure suddenly looks interesting again, especially as enterprise AI moves beyond chatbot interfaces and into operational systems that directly affect physical commerce.

What Baton Signals for Logistics and Enterprise AI

Baton represents a larger transition happening across industrial technology markets. The next major AI infrastructure layer may not emerge primarily from consumer applications or chatbot ecosystems. It may emerge inside industries where operational inefficiency still dominates large portions of economic activity. Logistics, manufacturing, energy, transportation, and supply chain infrastructure remain filled with fragmented workflows waiting for software modernization.

That creates a different kind of technology race. The winners may not be companies with the loudest AI branding. They may be organizations capable of integrating machine intelligence into messy operational environments without disrupting core business continuity. Freight does not reward hype cycles. Freight rewards reliability.

Baton’s positioning inside Ryder gives the company unusual advantages in that environment, including operational scale, live network access, enterprise deployment pathways, and direct exposure to real transportation constraints. Silicon Valley spent years trying to “move fast and break things.” Logistics companies do not have that luxury because breaking things in freight usually means delayed inventory, missed deliveries, and operational chaos spreading across supply chains. Baton appears to understand that distinction clearly.