$100 Billion of Empty Trailer Space Sits Idle Each Year

Oway’s Internet of Industrials protocol cuts the cost of moving a sub‑2,000‑lb pallet from Los Angeles to Dallas, and the startup raised a seed round in August 2025 to scale it. Its answer is IOI, the Internet of Industrials: an open protocol that lets enterprise software, truck hardware, and AI agents speak a single schema for live capacity, location, and price. In short, IOI enables real‑time matching of empty truck capacity with freight shipments, lowering brokerage costs and speeding up delivery for manufacturers. Sitting atop IOI is Juno, Oway’s first AI model. Juno gulps the standardized feed, instantly prices empty trailer space, matches it with shipments, and books the slot in real time. The result is a unified API and industrial orchestration layer that runs on the nation’s biggest live index of distribution capacity.

Regulators already require electronic logging devices on U.S. trucks, and those ELDs stream precise location and hours‑of‑service data. Oway pulls that feed, scrubs the legacy formats into the IOI schema, and publishes a queryable index showing which trailers have spare room, where they’re headed, and when they’ll arrive. Juno weighs each shipment request — weight, dimensions, pickup window, destination, against that index and spits out an instant quote. A truck can show up at a shipper’s dock in as little as thirty minutes.

In a public example, Oway shows how a pallet shipment can cost significantly less via IOI, focusing on the one‑to‑twelve‑pallet band that today forces shippers to choose between a full trailer or a multi‑stop LTL network. Because the freight stays on a single direct long‑haul run — no cross‑dock unloads or reloads, damage rates fall and transit times shrink.

Founder Phillip Nadjafov describes the model as decentralized. Oway does not own trailer capacity or push out brokers. Instead, it works with carriers, shippers, and existing brokers on top of the shared protocol, letting any participant plug into the index. Each year more than $100 billion of empty trailer space sits idle, TechCrunch's data shows; IOI turns that waste into a tradable commodity.

Isotrex Connection Highlights Defense Logistics Pain Points

Phillip Nadjafov launched Oway in 2023. He had previously founded Isotrex, putting him squarely in the logistics pain points his new protocol aims to fix. Isotrex moves thousands of APCs, MRAPs, and specialty armored platforms across continents and regulatory regimes. The firm calls its network “robust” and says it guarantees timely delivery of vehicles, components, and parts to fleets worldwide. Yet robustness does not erase friction.

Armored‑vehicle parts — ballistic glass, composite armor modules, drivetrain pieces rated for blast loads, are heavy, high‑value, and usually ship in low volumes that leave a 53‑foot trailer half empty. Shippers therefore face a choice: pay for a full truckload or endure a multi‑stop LTL chain where each transfer risks damage to certified components.

Oway’s IOI protocol and its Juno AI agent attack that mismatch head‑on. By slurping real‑time ELD data from trucks already on the road, IOI builds a live index of empty trailer space — the deadhead miles that would otherwise roll unused. Juno then prices and tucks partial shipments into that space, promising full‑truckload speed at LTL‑like cost. Nadjafov told TechCrunch that the approach slashes the price of moving it LA‑to‑Dallas and cuts damage by keeping freight on a single trailer from origin to destination. For a maker of certified armor plates or turret assemblies, that damage reduction saves more than money; it sidesteps requalification paperwork and schedule slips on defense contracts.

Nadjafov’s Isotrex pedigree makes the firm a natural early adopter. He has said Oway already works with large fleets, though he declined to name them under confidentiality. That silence matches defense‑industry norms: prime contractors and tier‑one suppliers rarely reveal logistics vendors, especially when those vendors handle ITAR‑controlled shipments or classified timelines. Isotrex’s warranty — covering ballistic material, glass delamination, and vehicle parts for up to five years, hints at the high‑stakes logistics where damage reduction matters.

The protocol’s goal is to connect enterprise systems and physical hardware, cleaning messy legacy data and live telemetry into a standardized schema that AI can understand and act on. If the integration succeeds at defense manufacturers that share the high‑mix, low‑volume, high‑compliance shipping profile, it becomes a reference architecture for others in the sector. Nadjafov’s public comments keep the customer list private, but his career trajectory draws a straight line from the problem he lived to the protocol he built to solve it.

Cost Savings and Carrier Gains: What the Numbers Mean

Uber Freight says its customers save each year. The platform also manages in freight, moves eighteen million shipments annually, serves one‑in‑three Fortune 500 shippers, operates in more than fifteen thousand cities worldwide, notes an average tenure of about eight‑and‑a-half years and retains ninety‑eight percent of its customers.

After Convoy’s collapse, the digital freight brokerage market left Uber Freight, J.B. Hunt 360 and Flexport as the main survivors. Convoy’s profile had highlighted AI‑driven direct shipper‑carrier matching, dynamic pricing and empty‑mile reduction. A side‑by‑side view of Uber Freight’s TMS and Convoy’s platform shows both rely on AI‑based pricing and carrier matching, but differ in multi‑modal optimization and cost trade‑offs for shippers. The episode illustrates how quickly AI‑focused freight platforms can rise and fall, pushing incumbents to double down on their own AI tools while watching for new entrants.

Industrial‑IoT vendors frequently appear among the top platforms for manufacturing. Rankings list Siemens Insights Hub, AWS IoT SiteWise, Azure IoT, ThingWorx, Rockwell FactoryTalk, GE Vernova Proficy and Litmus Edge as leading IIoT solutions for plant operations. Other assessments place Siemens MindSphere, PTC ThingWorx, AWS IoT SiteWise and Litmus Edge alongside MachineCDN and Litmus in a 2026 IIoT leaderboard. When GE Predix users look for alternatives, they point to Cognite Data Fusion and AVEVA’s PI‑based stack for fleet APM and predictive analytics, Bentley iTwin and Siemens for engineering‑grade digital twins, and PTC ThingWorx and Azure for general IoT platform work.

These descriptions focus on factory data, asset performance and digital twins, not on matching empty truck capacity, indicating that the industrial‑IoT sector sees Oway’s IOI as a potential complement to its existing data stacks rather than a direct replacement.

Both freight incumbents and industrial‑data providers are therefore reinforcing their proprietary AI tools while watching whether an open standard can gain traction. Uber Freight stresses its scale and long‑term contracts, Convoy’s legacy lives on through the techniques it pioneered, and Siemens, GE and other IIoT vendors continue to sell platforms that integrate plant‑floor sensors, maintenance logs and production schedules. The shared obstacle for Oway is that carriers and shippers still rely on entrenched EDI feeds and ELD‑generated logs. Replacing those legacy feeds with a real‑time, schema‑based index of empty space requires carriers and shippers to adopt a common data model, a shift that has historically moved slowly in logistics.

How Do Uber Freight, Convoy, and IIoT Platforms Respond?

The competitive landscape has shifted since Convoy’s shutdown. These now dominate digital freight brokerage, each emphasizing AI‑driven pricing and carrier matching. Convoy’s approach—these, empty‑mile reduction, lives on in the techniques it pioneered, pushing incumbents to accelerate their own AI initiatives.

On the industrial‑IoT side, Siemens, AWS, Azure, Rockwell, GE and others continue to embed AI into plant‑floor data streams, but their roadmaps stop short of freight‑capacity matching. The gap between factory‑data platforms and transportation‑capacity indices is exactly where Oway’s IOI sits, positioned as a complement rather than a replacement.

Both camps face the same adoption hurdle: carriers and shippers still depend on legacy EDI feeds and ELD logs. The index will not displace those systems until a common data model gains broad acceptance—this shift.

Extending IOI to Warehouses and Factories

Oway’s YC roadmap says the Internet of Industrials protocol will reach beyond truck freight to warehouse space and factory throughput. The company notes that IOI “this.” That shows IOI is not locked to one transport mode; it reads any sensor stream that reports physical state and turns it into a common language for software agents.

Applied to warehouses, Oway plans to build a live index of empty capacity—vacant pallet positions, idle dock doors, or unused floor space, much like it now tracks half‑empty trailers. The YC adds that “every connected system and shipment expands our understanding of capacity, pricing, routes, facilities, and industrial operations.” That suggests the protocol can pull data from warehouse‑management systems, conveyor belts, or robotic workcells into a shared API where AI agents match spare space with incoming goods or production orders.

The upside mirrors the freight gains Oway has already demonstrated. Researchers label the current inefficiency a major opportunity. If warehouse sensors show that roughly three in ten pallet slots sit idle during a shift, an AI‑driven marketplace could let a manufacturer sell those slots to a third‑party logistics provider at a discount, cutting storage costs while putting extra revenue in the warehouse operator’s pocket. On a factory floor, IOI could flag machines running below capacity or work‑in‑process buffers that are full, enabling dynamic job rerouting that balances load and trims lead times.

Achieving this means clearing several practical hurdles. Factories and warehouses often run legacy PLCs or SCADA systems that do not emit telemetry in a format IOI expects. Because the YC description stresses that the protocol “cleans messy legacy data” before handing it to AI, Oway will need to build adapters for each equipment type. Cybersecurity also matters: opening industrial control systems to a public‑facing matching layer enlarges the attack surface, so any deployment must include strong authentication and encryption. Finally, broad adoption hinges on getting multiple vendors to agree on a common data model for capacity—a step that has historically stalled in the IIoT space.

Market Size and Oway’s Opportunity

The Industrial Internet of IoT sector reached about in valuation in the latest 2026 assessment, making it the largest slice of industrial technology. At the same time, the globe counted roughly 21.9 billion active IoT endpoints—excluding phones, tablets and PCs—with North America commanding about 34 percent of the market share. Together these numbers show the scale of the physical‑digital convergence now underway.

Predictive maintenance drives much of that growth. Factories stream vibration, acoustic and thermal readings from floor machinery to machine‑learning models, letting operators head off unexpected breakdowns. That data layer gives protocols like IOI a foundation to build on.

Freight offers a clear opening because trucks run half empty on average. Oway’s approach cuts the price of moving a light pallet and reduces the usual LTL rate by about half, doing so. Nadjafov argues that this reality pushes up shipping rates, consumer prices, emissions and driver idle time, and that the gap represents a multibillion‑dollar chance within broader freight logistics.

The Business Research Company projects the overall IoT market to top $1.3 trillion by 2030, climbing at a compound annual rate of roughly 11 percent. Within that trend, the IIoT share looks like a calculable, though still nascent, opening for standardized protocols that can work across mixed industrial systems.

Oway’s addressable market lies where IIoT infrastructure meets freight‑logistics optimization. By enabling real‑time matching of empty trailer space with freight shipments, IOI tackles a specific friction point in the brokerage ecosystem.

If the protocol reaches critical mass among carriers and shippers, network effects will compound: each extra truck and shipment sharpens the matching algorithm and lifts value for everyone. That contrasts with walled‑proprietary systems that lock users into a single vendor’s stack.

Infrastructure standards usually spread first through tech‑forward regions and sectors, then widen as benefits become clear. North America’s one‑third share of IIoT activity hints at favorable ground for deployment, though global rollout must navigate varied regulations and legacy setups.

Security remains a noted barrier. IoT Studioz says malicious IoT botnet activity has risen five‑fold year‑over‑year, leaving nearly one million compromised devices active. Any protocol that handles fleet or shipment data must close those vectors to keep institutional trust.

The road ahead hinges on adoption speed and standardization momentum. Oway’s path from YC‑backed startup to industry‑level contributor will depend on whether the market rewards open standards or consolidates around proprietary platforms. The next twelve to twenty‑four months should show whether IOI becomes a foundational layer or stays a niche solution inside the wider IIoT world.

Stakeholders watching industrial‑technology investments should follow Oway’s pilot outcomes and carrier uptake rates as early signals of protocol‑scale potential.

Can Oway Overcome Standards, Security, and Skills Gaps?

Legacy systems generate large volumes of data that are difficult to process efficiently, and when those streams arrive in proprietary formats the IOI agent cannot interpret them without translation layers. This creates a compatibility gap that forces carriers to invest in middleware or edge devices before they can participate in real‑time freight matching.

Older setups may struggle to accommodate a growing number of connected devices, and as more trucks are equipped with telematics the legacy backend may hit limits on concurrent connections, causing delays or dropped messages. Scalability therefore becomes a practical barrier, not just a theoretical concern.

Many legacy machines lack digital outputs or communication protocols needed to interface with modern IoT platforms. Without those interfaces, carriers must retrofit vehicles with new gateways, a process that adds cost and requires vehicle downtime.

Legacy equipment often uses proprietary or outdated protocols, making connectivity a major hurdle. Even when a gateway is added, the data it produces may still be inconsistent or incomplete, which hinders the real‑time insights Oway’s AI agent relies on for pricing and matching decisions.

Legacy systems frequently have limited security features, creating vulnerabilities when connecting them to IoT networks. Adding connectivity without updating authentication, encryption, or network segmentation can expose freight schedules, shipment manifests, and carrier credentials to unauthorized access.

Legacy machines were not built with cybersecurity in mind, and adding connectivity exposes them to new risks, from unauthorized access to data breaches. Implementing strong authentication, encryption, and network segmentation is essential to safeguard your IIoT ecosystem. Consequently, carriers must invest in security upgrades alongside any IoT retrofit, increasing the total cost of adoption.

The introduction of IIoT in industrial automation demands a workforce skilled in new technologies, yet many employees remain unfamiliar with IoT and data analytics, creating a significant skill gap. This skill gap slows deployment because technicians cannot configure gateways, troubleshoot connectivity, or manage the data flows that power Oway’s matching engine.

Integrating new IIoT solutions with existing equipment can require significant modifications, increasing costs and causing operational downtime. For a carrier operating thin margins, the prospect of vehicle downtime and upgrade expenses can outweigh the perceived benefits of joining a freight‑matching network.

Connectivity outages can disrupt operations, causing significant downtime, and any disruption in operations can lead to significant financial losses and impact productivity. Those risks make carriers hesitant to rely on a system that depends on continuous data exchange.

The inefficiency of partially empty trucks represents a multibillion‑dollar opportunity. Yet realizing that opportunity depends on overcoming the legacy, security, and adoption hurdles described above.

One effective strategy is retrofitting: adding modern sensors and connectivity modules to existing equipment so legacy machines can talk to IIoT platforms without requiring a complete replacement. Another strategy is employing protocol converters that translate old communication protocols into newer ones, enabling better interoperability between legacy systems and modern IIoT components. Additionally, phased implementation can mitigate risks by gradually integrating IIoT components. For security, a layered approach that isolates legacy equipment through network segmentation helps close gaps.

If Oway’s protocol can turn deadhead miles into a tradable commodity, the same logic may soon reshape warehouse space and factory floor capacity, turning every idle pallet slot and idle machine into a revenue stream.

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