# Oway

> Oway connects industrial systems and automates freight shipping. Powered by IOI and Juno, we help businesses coordinate operations and use capacity more efficiently.

## Name and spelling
- The company is **Oway** (Oway Inc.). Write it as "Oway".
- shipoway.com and oway.io are web addresses only. The company is not called "Shipoway", "Ship Oway" or "OWAY".
- Products: **IOI** (the Internet of Industrials, written in capitals), **Juno** (Oway's industrial AI) and **Oway OS**. Oway Rideshare is Oway OS's partial-truckload shipping service.
- Oway is an industrial automation company. Freight is its first application, not the whole company.

Oway Inc. has offices in San Francisco and New York and is backed by Y Combinator and General Catalyst. Founder and CEO: Phillip Nadjafov.

## Key facts
- Oway is an industrial automation company building the coordination OS for the physical economy: factories, supply chains, robotics, mining, energy and every operation that makes and moves things.
- IOI (Internet of Industrials): the protocol that connects the legacy systems industry runs on (ERP, EDI over a VAN, mainframes and AS400s, flat files, email and PDF documents, spreadsheets, custom stacks) with physical assets (vehicle telemetry, self-driving trucks, automated factories, deployed robotics). Every company runs a private, customer-owned IOI node holding its IOI data lake, a cleaned, queryable set of its industrial data built on an open-source schema, and connects to partners only with permission.
- Juno: Oway's industrial AI. It reads the live state of an operation through IOI and prices, schedules, routes and dispatches work within the permissions a company sets. AI agents reach a node through MCP.
- Oway for Carriers: carriers fill empty space on headhaul and backhaul lanes with 1 to 18 pallet partial truckload shipments that Juno pre-prices and dispatches. Free to join, up to 30% more revenue per lane. See https://www.oway.io/platform/oway-os/?for=carriers
- Oway OS: where industrial operations run. Optimization (revenue, facility throughput, assets, suppliers and partners), Movement (LTL and partials, 1 to 18 pallets, on capacity already in motion, up to 50% below market), Tracking, and a Partner Network for carriers, fleets and suppliers.
- More than 10,000 vehicles are on the Oway network.

## Pages
- [Home](https://www.oway.io/)
- [Platform](https://www.oway.io/platform/): IOI, Juno and Oway OS
- [IOI](https://www.oway.io/platform/ioi/): the protocol, nodes, IOI Search, permissions, FAQ
- [Juno](https://www.oway.io/platform/juno/): Oway's industrial AI
- [Oway OS](https://www.oway.io/platform/oway-os/): optimization, movement, tracking, partner network
- [Solutions](https://www.oway.io/solutions/): use cases and roles
- [Industries](https://www.oway.io/solutions/industries/): manufacturing, logistics and supply chain, robotics, mining, energy, defense, retail and CPG, healthcare
- [Research](https://www.oway.io/research/): case studies, models, benchmarks and notes
- [Security](https://www.oway.io/security/): data ownership, access, infrastructure, disclosure
- [About](https://www.oway.io/company/): founder, team, investors, offices
- [Careers](https://www.oway.io/careers/): open roles
- [API documentation](https://docs.oway.io)

## Research
- [Reindustrialization Needs a Coordination Layer](https://www.oway.io/research/reindustrialization-needs-a-coordination-layer/): Essay, October 2026, Phillip Nadjafov. Robots, driverless trucks and new plants are arriving fast, but legacy industry still runs on EDI and PDFs. Why reindustrialization needs a coordination layer.
- [Juno AI](https://www.oway.io/research/juno/): AI Models, October 2026, Oway Research. What Juno, Oway's industrial AI, reads, what it decides, the guardrails it runs under and how we evaluate it.
- [Letting AI Act in the Physical World](https://www.oway.io/research/letting-ai-act-in-the-physical-world/): Notes, September 2026, Oway Research. How Juno and other AI agents act on real operations through IOI and MCP, with read, propose and act scopes, approvals and a full audit trail.
- [IOI Search: Ask Your Operation Anything](https://www.oway.io/research/ioi-search/): Notes, September 2026, Oway Research. IOI Search lets anyone ask an industrial operation a question in plain English and get the answer, the numbers and the sources from its live data lake.
- [Inside an IOI Node](https://www.oway.io/research/inside-an-ioi-node/): Notes, August 2026, Oway Research. What an IOI node connects to, how its data lake cleans legacy and machine data into an open-source schema, and how permissions, audit and ownership work.
- [Introducing IOI, the Internet of Industrials](https://www.oway.io/research/introducing-ioi/): Essay, July 2026, Phillip Nadjafov. IOI connects legacy industrial systems and autonomous machines through an open-source schema, so companies and AI can read, quote, book and produce together.
- [The Physical Economy Has No Index](https://www.oway.io/research/the-physical-economy-has-no-index/): Essay, July 2026, Phillip Nadjafov. The internet became useful once it was indexed. The physical economy still has no live index of its capacity. Why that costs trillions, and how to build one.
- [Coordination Is the Bottleneck](https://www.oway.io/research/coordination-is-the-bottleneck/): Essay, June 2026, Phillip Nadjafov. What matching freight across 10,000+ vehicles taught us. Most of the cost comes after the match, and the physical economy needs a live index.
- [The Accidental Context Layer](https://www.oway.io/research/the-accidental-context-layer/): Essay, June 2026, Phillip Nadjafov. How the ELD mandate put a connected device in nearly every US truck, and how COVID turned that data into the start of a physical context layer.
- [Pricing Capacity That Already Exists](https://www.oway.io/research/pricing-capacity-that-already-exists/): AI Models, May 2026, Oway Research. How Juno prices open space on US trucks already on the road from live capacity, carrier, demand and market signals, without breaking a delivery.
- [The Half-Empty Truck](https://www.oway.io/research/the-half-empty-truck/): Notes, April 2026, Oway Research. US trucks drive one mile in six empty and run loaded at a third to half of capacity. What that costs, and how China cut empty running with freight matching.
- [Why We Started Oway](https://www.oway.io/research/why-we-started-oway/): Essay, April 2026, Phillip Nadjafov. Oway's founder on what building a defense vehicle manufacturer taught him about coordination, and why Oway started with the empty space in American trucks.
- [The New Rules of the Road](https://www.oway.io/research/the-new-rules/): Guest Article, March 2026, Bart A. de Muynck. Rising fuel costs make half-empty trucks unsustainable. How consolidating freight into space already moving turns fuel cost into an advantage.
- [Euri Lighting](https://www.oway.io/research/case-study-euri-lighting/): Case Study, February 2026, Will Honda. How Euri Lighting cut freight costs by up to 53%, saved hours of quoting every week and shipped about 200 loads with zero damage claims.
- [Vybes](https://www.oway.io/research/case-study-vybes/): Case Study, February 2026, Will Honda. How a fast-growing beverage brand cut freight costs by up to 63% across California and Nevada by shipping on trucks already making the trip.
- [RTD Delivery Systems](https://www.oway.io/research/rtd-delivery-systems/): Carrier Study, February 2026, Will Honda. How a regional carrier filled the empty space on its existing lanes and earned $1,520 in extra revenue from five shipments in its first week on Oway.
- Feeds: https://www.oway.io/research/rss.xml and https://www.oway.io/research/feed.json

## IOI, the Internet of Industrials
- IOI is the protocol that gives the physical economy a common language, so legacy industrial systems and autonomous machines can read and act on the same data, across companies.
- A few vertically integrated companies built private coordination layers across their own factories, fleets and robots. IOI makes that kind of integrated coordination open and easy to set up for any company, on top of the systems it already runs.
- Legacy surfaces IOI connects: EDI over a VAN (X12 204, 214, 990), mainframes and AS400 green screens, flat files over SFTP, email and PDF (bills of lading, rate confirmations, invoices), spreadsheets and custom in-house stacks.
- Physical assets IOI connects: vehicle telemetry (position, hours, empty space), self-driving trucks, automated factories (lines, docks, throughput) and deployed robotics.
- Setup: a default IOI setup takes days. It starts as a monitoring surface for a company's freight quotes, then optimizes its physical operations from a secured data lake. ERP, EDI and other surfaces are optional and can be added at any time afterwards.
- Each company runs a private, customer-owned IOI node. Inside it is the company's IOI data lake: a cleaned, queryable set of its industrial data, IOI-compatible and built on an open-source schema.
- The schema covers reading state and taking action, across companies and machines. Example operations: READ (how many units of a SKU sit in a warehouse right now), WRITE (send a status update to another factory), PRODUCE (ask a plant to run more mild steel), QUOTE (price nine pallets against trucks already rolling), BOOK (call a truck for 8 units by 8am tomorrow), TRACK (position and status without a phone call), TENDER (offer the load straight to the carrier) and CAPACITY (what is empty tonight, and where it is headed). These are examples; the schema is extensible.
- IOI Search answers plain-language questions about the operation with the numbers and sources behind them.
- Nodes connect to suppliers, carriers, customers, machines and AI agents only with explicit, scoped and revocable permission, and every access is logged.
- AI agents, including Juno and a company's own agents, reach a node through MCP with read, propose or act scopes. Sensitive actions can require a person's approval.

## Pricing
- Juno and Oway OS are free for everyone: setting up Juno, quoting, booking and tracking in Oway OS, and using Oway OS inside an existing TMS, ERP or quoting tool.
- IOI and IOI Search: contact Oway's sales team for pricing (https://www.oway.io/contact/?topic=ioi). Every IOI integration includes IOI Search, access to Oway's network of more than 10,000 vehicles, and Juno optimizations out of the box.
- Freight moved through Oway Rideshare is priced per shipment, up to 50% below market.

## Security
- Each IOI node and data lake is customer-owned and never pooled; every connection needs an explicit, scoped, revocable grant; AI agents get read, propose or act scopes; every access is logged. Details: https://www.oway.io/security/

## API and integrations
Authoritative reference: https://docs.oway.io
- Shipper API: quote, book and track LTL and partial-truckload shipments (1 to 18 pallets). Quote request reference: https://docs.oway.io/reference/quote-request. Requests use a bearer token; a JavaScript SDK is published on npm.
- Carrier API: carriers receive pre-priced load offers that fit their routes and accept them programmatically. Offers reference: https://docs.oway.io/reference/getoffers
- IOI: search a node's data lake, subscribe to changes so every connected system stays in sync, call schema operations such as quote, book, tender and produce, and give AI agents permissioned access through MCP.
- Also available through SMC3, project44, EDI and MCP, and inside existing TMS, ERP and quoting tools so every quote can include an Oway Rideshare option.
- Web app for shippers and carriers: https://portal.oway.io

## Open roles
- [Staff Engineer, IOI Protocol](https://www.oway.io/careers/staff-engineer-ioi-protocol/): Design the protocol that lets machines, software, and AI agents understand the physical world. San Francisco, in person. $220K to $280K + equity.
- [Senior Full-Stack Engineer, IOI Applications](https://www.oway.io/careers/senior-full-stack-engineer-ioi-applications/): Build the applications that let AI see, decide, and act across the physical economy. San Francisco, in person. $180K to $230K + equity.
- [Forward Deployed Engineer, Industrial Systems](https://www.oway.io/careers/forward-deployed-engineer-industrial-systems/): Connect the world's largest industrial companies to IOI, and automate what they run. San Francisco, 30 to 50% travel. $170K to $220K + equity.
- [Quantitative Researcher, IOI Index](https://www.oway.io/careers/quantitative-researcher-ioi-index/): Build the first real-time index of the physical economy. San Francisco, in person. $220K to $320K + equity.
- [Strategic Partnerships Lead, Enterprise](https://www.oway.io/careers/strategic-partnerships-lead-enterprise/): Bring the Fortune 500 onto the Internet of Industrials. San Francisco or Seattle. $180K to $220K base, $350K to $450K OTE + equity.
- [Enterprise Engagement Partner](https://www.oway.io/careers/enterprise-engagement-partner/): Diagnose how the world's largest companies run, then automate it. San Francisco, in person. $160K to $200K base, $250K to $320K OTE + equity.
- [Fleet Network Partnerships Director](https://www.oway.io/careers/fleet-network-partnerships-director/): Bring America's fleets onto the Internet of Industrials. US remote, Midwest or Texas preferred. $150K to $190K base, $250K to $350K OTE + equity.

## Contact
- Support: help@oway.io
- App: https://portal.oway.io


# Full page text

## Oway | Industrial Automation, Distribution and Research
URL: https://www.oway.io/

Oway is the coordination OS for the physical economy. Oway OS is where operations run: optimization, movement, tracking and a partner network. IOI, the Internet of Industrials, connects legacy industrial systems and autonomous machines through customer-owned data lakes on an open-source schema. IOI already powers every existing business or hardware unit using or connected to Oway. Juno, Oway's industrial AI, reasons over it and acts.

## The Oway Platform: IOI, Juno and Oway OS
URL: https://www.oway.io/platform/

Platform One platform for industrial operations. Oway connects every system, machine and partner in an operation through one open schema. Juno reasons over it and acts. Oway OS is where your teams run the work. Request a Demo Start Free Log in now and access Oway OS and Juno for free. How it fits together Three layers on top of the operation you already run. Oway OS Where operations run Optimization Shipping Tracking Partner Network Juno Industrial AI that acts Pricing Scheduling Routing Dispatch Agents and MCP IOI The common protocol Connect Live World Model IOI Search Network Your operation Systems, machines and partners ERP MES WMS TMS SCADA PLCs Sensors Robots Fleets Suppliers IOI Search Ask your operation anything and get the answer, the numbers and the sources behind it. Learn More Live World Model Every facility, asset, order and flow in your operation on one live map. Learn More Juno Ask Juno to quote, plan, book or reroute work, or drop in an email, CSV or purchase order. Learn More Revenue Optimization See where demand converts and where it leaks, by lane, customer and product. Learn More Facility Optimization Find where inventory sits, where throughput stalls and which sites are near capacity. Learn More Shipping Price and book moves between sites in seconds, on capacity already in motion. Learn More Tracking Follow every load and order from pickup to delivery. Learn More IOI Connect everything once. IOI is the common protocol for the physical economy. A default setup takes days: it starts as a monitoring surface for your freight quotes and optimizes your operati

## IOI: the Internet of Industrials | Oway
URL: https://www.oway.io/platform/ioi/

IOI The Internet of Industrials. The shared context layer behind Oway’s existing network. IOI starts with your freight quotes and a secured data lake, live in days. Optimize from there, and add ERP, EDI and machines whenever you are ready. Talk to Us About IOI Contact our sales team for pricing on IOI and IOI Search. IOI Search Ask Juno Acme Distribution Facility performance · this week Coppell Crossdock 4 At capacity Coppell, TX 96% utilization Vernon DC Healthy Vernon, CA 71% utilization Phoenix Yard Watch Phoenix, AZ 6.2 d median dwell What’s the status of OWY-7249? Juno OWY-7249 is driving to delivery: 4 pallets, Vernon to Beverly Hills with Pacific Short Haul. ETA today at 15:40, on time. On time 24 mi left Anything at risk this week? Juno Two inbound loads to Coppell overlap Thursday at 96% utilization. I can move ACM-2026-0427 to Friday at 07:00. Dock conflict Proposed fix IOI Search Connect Data Lake Schema Why IOI Pricing FAQ IOI Search Ask your operation anything. Every IOI setup comes with Search by default. Ask in plain English and get the answer, the numbers and the sources. Uses What runs on IOI. Optimization Oway OS Revenue, throughput, assets and suppliers, measured on live data. Search IOI Search Answers about facilities, products, lanes, suppliers and carriers. Agents Juno and AI agents Read state and take actions through MCP, within the permissions you set. Machines Autonomous assets Self-driving trucks, robots and automated lines that discover work and commit to it. Connect Live in days. Add more whenever you want. Every IOI setup star

## Juno: Oway's industrial AI
URL: https://www.oway.io/platform/juno/

Juno The AI that runs industrial operations. Juno reasons over the operational context connected through IOI. It decides what should happen next and acts within the permissions you set. Start Free Request a Demo Log in now and access Oway OS and Juno for free. Pricing and Scheduling Routing and Dispatch Juno in Oway OS Agents and MCP FAQ What Juno does Decisions in seconds, across the whole operation. Price Pricing Prices capacity, movement and services from live conditions, real bookings and cost. Schedule Scheduling Plans production, docks, pickups and crews around constraints and priorities. Route Routing and dispatch Routes and reroutes vehicles, robots and crews as conditions change. Coordinate Coordination Machines, fleets, partners and people work from one plan. Juno Industrial AI that acts in seconds. Juno reads the live state of an operation through IOI, decides what should happen next and acts. Here it fills the empty half of a truck already on the road with a short detour: the shipper pays 33% below market and the carrier earns 20% more on the lane. Juno Illustrative examples · I-95 and Miami to Dallas TRUCK 4471 · 53′ DRY VAN · I-95 NORTH 13 OF 26 OPEN 26 OF 26 FULL OWAY PARTIAL INDEX AUTOMATIC PRICING $141.50 PER POSITION 33% BELOW MARKET BCO OWAY, ADDED REQUEST · BALTIMORE PLANT 13 pallets · 10,400 lbs + DROP ADDED PHILADELPHIA · 13 PLT WASHINGTON PICKUP · BALTIMORE +6.2 MI · +11 MIN NEW YORK · ON TIME BALTIMORE PHILADELPHIA NEW YORK DC SHIPPER PAYS 33% BELOW MARKET CARRIER LANE REVENUE +20%

## Oway OS: optimize your entire shipping stack
URL: https://www.oway.io/platform/oway-os/

Built on IOI, Oway OS turns connected operational context into action. Oway OS for Enterprises: optimize your entire shipping stack, ship 1 to 18 pallets on trucks already making the trip at up to 50% less, free on its own or inside your TMS. Oway for Carriers: start accepting partial freight, fill empty space with 1 to 18 pallet partial truckload shipments pre-priced by Juno AI Dispatcher, up to 30% more revenue per lane, free to join.

## Solutions: Oway for industrial operations
URL: https://www.oway.io/solutions/

Solutions Oway for Manufacturers Every operation that makes and moves things runs on the same questions: what exists, where it is, where it needs to go and what it costs. Oway answers them live and acts on them. Request a Demo All Industries Use Cases By Team Industries Use cases What companies run on Oway. Each use case runs on one or more layers of the platform. Increase throughput Find the bottleneck on every line, dock and site, and the change that clears it. IOI Oway OS Find revenue in your network See where demand converts, where it leaks and which lanes, customers and products to grow. IOI Oway OS Cut the cost of moving goods Move LTL and partials on capacity already in motion, up to 50% below market. Juno Oway OS Reduce dwell and idle time Spot inventory, equipment and vehicles that sit too long, and fix the schedule behind it. IOI Juno Coordinate mixed machine fleets Robots, vehicles and equipment from different vendors working from one plan. IOI Juno Score suppliers and partners Measure every supplier and carrier on time, coverage, quality and cost. Oway OS Plan on live data Forecast and plan production, inventory and movement on the state of the operation right now. IOI Juno Give agents safe access Let AI agents read and act on your operation through MCP, inside the permissions you set. IOI Juno By team Built for everyone who runs the operation. Operations One live view of every site, line and load, and an AI that handles the routine decisions so your team handles the exceptions. Supply chain and procurement Supplier, carrier and inventory performance in one plac

## Industries: Oway for every critical industry
URL: https://www.oway.io/solutions/industries/

Industries Built for every industry that makes and moves things. The same platform runs factories, warehouses, fleets, mines and field operations. Choose an industry to see what runs on Oway. Request a Demo Solutions Overview Manufacturing Connect lines, plants and suppliers so production runs on one live plan. Explore Logistics and Supply Chain Run warehouses, fleets and partners as one network. Explore Robotics and Autonomy Give robots and autonomous vehicles the context they need to take on real work. Explore Mining and Materials Coordinate equipment, stockpiles and outbound haulage from pit to port. Explore Energy and Utilities Keep field assets, parts and crews moving to where they are needed. Explore Defense and Aerospace Trace every part and supplier behind readiness. Explore Retail, CPG and Food Keep shelves stocked and distribution centers flowing at the lowest cost to serve. Explore Healthcare and Life Sciences Get critical supplies where they need to be, on time. Explore Don’t see your industry? If you make or move things, Oway runs on your operation. Talk to our team. Request a Demo Contact

## Oway for Manufacturing
URL: https://www.oway.io/solutions/industries/manufacturing/

Industries / Manufacturing Oway for Manufacturing Plants run on dozens of systems that each hold part of the picture. Oway connects lines, machines, suppliers and logistics into one live model, and Juno keeps production, materials and shipments moving together. Request a Demo Start Free Log in now and access Oway OS and Juno for free. Use case Line throughput See output, downtime and bottlenecks across every line and shift, with the cause behind each. Live output by line, cell and shift Downtime ranked by cost, with its root cause The change that clears the constraint IOI Oway OS Units per hour, Line 3 Units per hour · Illustrative data 350 400 450 500 550 W1 W3 W5 W7 W9 W11 W12 486 Oway connected Units per hour, Line 3 Line 3 W1 412 W2 405 W3 418 W4 409 W5 421 W6 436 W7 449 W8 458 W9 463 W10 471 W11 478 W12 486 Use case Inbound materials Know when every component arrives and reschedule production before a shortage hits the line. Every inbound order tracked to the dock Shortage risk flagged days ahead Juno resequences production around late parts IOI Juno Line stoppages from late parts Stoppages per month · Illustrative data 0 5 10 15 Jan Feb Mar Apr May Jun 14 2 Line stoppages from late parts Stoppages Jan 14 Feb 12 Mar 9 Apr 5 May 3 Jun 2 Use case Supplier performance Score suppliers on lead time, quality and dock windows from the records you already have. On-time and on-window performance Quality and lead-time trends Early warning on slipping suppliers Oway OS On-time delivery by supplier Share of deliveries on time · Illustrative data Harbor Steel W

## Oway for Logistics and Supply Chain
URL: https://www.oway.io/solutions/industries/logistics/

Industries / Logistics and Supply Chain Oway for Logistics and Supply Chain Warehouses, carriers and fleets each run their own systems. Oway puts them on one network, so freight consolidates, inventory moves and every load is visible from pickup to delivery. Request a Demo Start Free Log in now and access Oway OS and Juno for free. Use case Consolidation Combine LTL and partial loads onto trucks already making the trip, priced in seconds. Juno finds open space on trucks headed your way Freight rides straight through, with no transfers Up to 50% below market Juno Oway OS Trailer fill by lane Share of trailer used · Illustrative data Before With Oway 0 25 50 75 100 DAL-HOU LAX-PHX CHI-IND ATL-CLT OAK-RNO Trailer fill by lane Before With Oway DAL-HOU 46% 81% LAX-PHX 52% 86% CHI-IND 41% 78% ATL-CLT 49% 84% OAK-RNO 38% 74% Use case Load tracking Follow every load, order and document from pickup to delivery in one view. Live ETAs on every load Documents and invoices attached Exceptions surfaced before customers call Oway OS Check calls to carriers Calls per day · Illustrative data 0 25 50 75 100 W1 W3 W5 W7 W9 W11 W12 11 Oway connected Check calls to carriers Check calls W1 86 W2 82 W3 88 W4 79 W5 61 W6 44 W7 31 W8 24 W9 19 W10 15 W11 13 W12 11 Use case Dock and dwell Find where freight and inventory sit longest and fix the schedule that causes it. Dwell by facility, door and SKU Root cause behind every delay Rebalancing between sites IOI Oway OS Median inventory dwell by facility Days · Illustrative data Phoenix Yard 6.2d Valley Bottling 4.9d Coppell Crossdo

## Oway for Robotics and Autonomy
URL: https://www.oway.io/solutions/industries/robotics/

Industries / Robotics and Autonomy Oway for Robotics and Autonomy Robots and autonomous vehicles are only as useful as the context they work from. Oway gives every machine, from any vendor, the orders, inventory and schedules it needs to take on real work. Request a Demo Start Free Log in now and access Oway OS and Juno for free. Use case Cross-vendor fleets Coordinate robots and vehicles from different vendors through one shared model of the site. One model of the site for every vendor’s fleet Shared map, tasks and traffic rules Fleet-wide utilization in one view IOI Juno Robot utilization by fleet Share of shift working · Illustrative data Before With Oway 0 25 50 75 100 AMR fleet A AMR fleet B Pallet movers Picking arms Robot utilization by fleet Before With Oway AMR fleet A 48% 74% AMR fleet B 41% 69% Pallet movers 55% 78% Picking arms 62% 81% Use case Task allocation Assign work to the right robot, vehicle or person based on location, capacity and priority. Juno assigns each task to the best resource Priorities update as orders change Idle time drops across the fleet Juno Tasks completed per hour, site-wide Tasks per hour · Illustrative data 150 200 250 300 350 W1 W3 W5 W7 W9 W11 W12 311 Oway connected Tasks completed per hour, site-wide Tasks W1 212 W2 208 W3 219 W4 214 W5 236 W6 258 W7 271 W8 284 W9 290 W10 298 W11 305 W12 311 Use case Shared context Let machines read orders, inventory and schedules from enterprise systems through IOI. Orders and inventory available to every machine No custom integration per vendor Permissions set per robot and pe

## Oway for Mining and Materials
URL: https://www.oway.io/solutions/industries/mining/

Industries / Mining and Materials Oway for Mining and Materials Mines run on equipment, stockpiles and haulage that rarely share one plan. Oway connects them from pit to port, so every load, queue and rail car is coordinated against production and contracts. Request a Demo Start Free Log in now and access Oway OS and Juno for free. Use case Haul cycles Measure every load, queue and cycle to cut idle time across the fleet. Every cycle measured by stage Queues predicted before they form Juno reassigns trucks between shovels IOI Juno Haul cycle time by stage Minutes · Illustrative data Before With Oway 0 5 10 15 Queue Load Haul Dump Return Haul cycle time by stage Before With Oway Queue 9.4 3.2 Load 3.1 2.9 Haul 12.6 12.1 Dump 1.8 1.6 Return 10.2 9.4 Use case Equipment utilization See which assets are working, waiting or down, and what each hour of downtime costs. Working, waiting and down time per asset Cost of every hour of downtime Maintenance planned around production IOI Oway OS Utilization by asset class Share of available hours working · Illustrative data Haul trucks 78% Excavators 71% Loaders 64% Drills 58% Dozers 46% Utilization by asset class Utilization Haul trucks 78% Excavators 71% Loaders 64% Drills 58% Dozers 46% Use case Stockpile inventory Track tonnage, grade and location for every stockpile against demand. Tonnage and grade by stockpile Committed demand against what is on hand Blending plans from live data IOI Stockpile against committed demand Thousand tonnes · Illustrative data Stockpile Committed demand 300 350 400 450 500 Jan Feb Mar

## Oway for Energy and Utilities
URL: https://www.oway.io/solutions/industries/energy/

Industries / Energy and Utilities Oway for Energy and Utilities Utilities and energy operators manage assets spread across thousands of miles. Oway gives them one live picture of every site, part and crew, so the right resources reach each job fast. Request a Demo Start Free Log in now and access Oway OS and Juno for free. Use case Field assets One live picture of substations, sites and equipment, with the history behind each. Every asset, site and its history in one model Work orders joined to sensor data Risk ranked across the territory IOI Open work orders by region Work orders · Illustrative data North 148 Coastal 121 Valley 96 Mountain 74 Desert 52 Open work orders by region Open North 148 Coastal 121 Valley 96 Mountain 74 Desert 52 Use case Spare parts movement Position critical parts ahead of demand and move them fast when something fails. Critical parts positioned near likely failures Juno books the fastest move when one happens Every part tracked to the job site Juno Oway OS Days to restore after a parts failure Days · Illustrative data 2 4 6 8 W1 W3 W5 W7 W9 W11 W12 2.4 Oway connected Days to restore after a parts failure Restore time W1 6.8 W2 7.1 W3 6.5 W4 6.9 W5 5.2 W6 4.1 W7 3.6 W8 3.2 W9 2.9 W10 2.7 W11 2.5 W12 2.4 Use case Crew and vehicle dispatch Send the right crew and vehicle to each job and reroute as priorities change. Jobs matched to crew skills and location Routes updated as priorities change More jobs per crew per day Juno Jobs completed per crew per day Jobs · Illustrative data Before With Oway 0 2 4 6 North Coastal Valley Moun

## Oway for Defense and Aerospace
URL: https://www.oway.io/solutions/industries/defense/

Industries / Defense and Aerospace Oway for Defense and Aerospace Readiness depends on parts and suppliers spread across many tiers. Oway traces every part from supplier to installation and keeps equipment moving against mission schedules. Request a Demo Start Free Log in now and access Oway OS and Juno for free. Use case Parts traceability Follow every part from supplier to installation, with the records attached. Every part linked to its supplier and records Installation history by serial number Audit-ready on demand IOI Oway OS Parts with complete records Share of parts · Illustrative data 40 60 80 100 120 Jan Feb Mar Apr May Jun 97% Parts with complete records Complete records Jan 58% Feb 63% Mar 74% Apr 86% May 93% Jun 97% Use case Supplier base visibility See lead times, capacity and risk across every tier of the supply base. Lead time and capacity by tier Single-source risk flagged Alternate suppliers ready before they’re needed IOI Oway OS Average lead time by supplier tier Weeks · Illustrative data Tier 1 6 wk Tier 2 11 wk Tier 3 17 wk Tier 4 24 wk Average lead time by supplier tier Lead time Tier 1 6 wk Tier 2 11 wk Tier 3 17 wk Tier 4 24 wk Use case Readiness logistics Plan and move equipment and parts against mission schedules. Moves planned against mission dates Juno books and tracks every leg Readiness measured by unit and site Juno Oway OS Equipment ready on schedule Share of units · Illustrative data 0 50 100 150 Q1 Q2 Q3 Q4 95% Equipment ready on schedule Ready on schedule Q1 78% Q2 84% Q3 91% Q4 95% More industries The same platf

## Oway for Retail, CPG and Food
URL: https://www.oway.io/solutions/industries/retail/

Industries / Retail, CPG and Food Oway for Retail, CPG and Food Retail, CPG and food companies win on availability and cost to serve. Oway connects sales, inventory and distribution, then moves product on capacity already in motion. Request a Demo Start Free Log in now and access Oway OS and Juno for free. Use case Replenishment Plan store and customer replenishment on live sales, inventory and capacity. Replenishment planned on live sell-through Inventory positioned before demand peaks Fewer stock-outs at the shelf IOI Juno Stock-outs across stores Stock-outs per week · Illustrative data 50 100 150 200 250 W1 W3 W5 W7 W9 W11 W12 74 Oway connected Stock-outs across stores Stock-outs W1 212 W2 198 W3 224 W4 205 W5 171 W6 139 W7 118 W8 101 W9 92 W10 84 W11 79 W12 74 Use case Distribution center throughput Balance inbound, storage and outbound across every DC. Throughput by DC, shift and door Labor matched to the flow of work Volume rebalanced between DCs IOI Oway OS Pallets per labor hour by DC Pallets per labor hour · Illustrative data Coppell 14.2 Vernon 12.8 Fresno 11.6 Oakland West 10.9 Phoenix 8.4 Pallets per labor hour by DC Throughput Coppell 14.2 Vernon 12.8 Fresno 11.6 Oakland West 10.9 Phoenix 8.4 Use case Partial loads Ship 1 to 18 pallets on trucks already moving, up to 50% below market. Juno prices every partial in seconds No warehouse transfers Tracked to the receiving dock Juno Oway OS Cost per shipment by pallet count USD per shipment · Illustrative data Typical LTL With Oway 0 500 1,000 1,500 2,000 2 pallets 4 pallets 8 pallets 12 pallets

## Oway for Healthcare and Life Sciences
URL: https://www.oway.io/solutions/industries/healthcare/

Industries / Healthcare and Life Sciences Oway for Healthcare and Life Sciences Hospitals and life sciences companies can’t run short. Oway keeps critical supplies visible, moves them with their handling requirements attached and catches supplier problems early. Request a Demo Start Free Log in now and access Oway OS and Juno for free. Use case Critical supply visibility Know where every critical item is, how much is on hand and when more arrives. On-hand and inbound for every critical item Days of supply by site Shortages flagged before they happen IOI Oway OS Days of supply, critical items Days on hand · Illustrative data 2 4 6 8 10 12 W1 W3 W5 W7 W9 W11 W12 9.8 Oway connected Days of supply, critical items Days of supply W1 4.2 W2 3.8 W3 4.5 W4 3.9 W5 6.1 W6 7.4 W7 8.2 W8 8.8 W9 9.1 W10 9.3 W11 9.6 W12 9.8 Use case Temperature-sensitive movement Plan and track moves with the handling requirements attached to every load. Handling requirements on every load Carriers matched to the equipment needed Every exception logged Juno Oway OS Loads delivered within handling requirements Share of loads · Illustrative data 0 50 100 150 Jan Feb Mar Apr May Jun 99% 99% Loads delivered within handling requirements Within requirements Jan 91% Feb 93% Mar 96% Apr 98% May 99% Jun 99% Use case Supplier reliability Score suppliers on fill rate and lead time so shortages are caught early. Fill rate and lead time by supplier Trends that predict a shortage Alternates ranked by reliability Oway OS Fill rate by supplier Share of order lines filled · Illustrative data Sup

## Security at Oway
URL: https://www.oway.io/security/

Security Built for the systems that run the physical world. Your operation’s data stays yours. Oway protects every node, connection and action with enterprise-grade security and controls. Start Free Talk to Our Sales Team Log in now and access Oway OS and Juno for free. Enterprise-grade protection Security is built into the protocol. Customer-Owned Nodes Every IOI node belongs to the company that runs it. Your operational data is never pooled with another customer’s, and you decide what leaves your node. Permission Before Every Connection A node connects to a partner, agent or machine only with the owner’s explicit permission. Every grant has a scope, and every grant can be revoked. Your Data Serves You Models that optimize your node run on your data for your operation. Nothing on your node is used to serve another company. Scoped Agent Access Juno and AI agents reach a node through MCP with the scopes you set: read, propose or act. Sensitive actions wait for a person to approve them. Safe Plant Connectivity Connections to plant and machine systems are segmented from corporate networks, run with least privilege and respect the safety systems already in place. Full Audit Trail Every query, approval and action is logged with who took it, when, and with what data. Controls Enterprise-grade security and controls. The controls behind every node, connection and action on Oway. Encryption Data is encrypted in transit and at rest. Identity and access Single sign-on, role-based access and least-privilege permissions for every user, agent and integration. Enterprise

## Oway Research: Industrial AI Essays, Case Studies and Models
URL: https://www.oway.io/research/

Oway Research. Reindustrialization Needs a Coordination Layer (Essay, October 5, 2026, Phillip Nadjafov): Robots, driverless trucks and new plants are arriving fast, but legacy industry still runs on EDI and PDFs. Why reindustrialization needs a coordination layer. Juno AI (AI Models, October 1, 2026, Oway Research): What Juno, Oway's industrial AI, reads, what it decides, the guardrails it runs under and how we evaluate it. Letting AI Act in the Physical World (Notes, September 21, 2026, Oway Research): How Juno and other AI agents act on real operations through IOI and MCP, with read, propose and act scopes, approvals and a full audit trail. IOI Search: Ask Your Operation Anything (Notes, September 7, 2026, Oway Research): IOI Search lets anyone ask an industrial operation a question in plain English and get the answer, the numbers and the sources from its live data lake. Inside an IOI Node (Notes, August 17, 2026, Oway Research): What an IOI node connects to, how its data lake cleans legacy and machine data into an open-source schema, and how permissions, audit and ownership work. Introducing IOI, the Internet of Industrials (Essay, July 27, 2026, Phillip Nadjafov): IOI connects legacy industrial systems and autonomous machines through an open-source schema, so companies and AI can read, quote, book and produce together. The Physical Economy Has No Index (Essay, July 6, 2026, Phillip Nadjafov): The internet became useful once it was indexed. The physical economy still has no live index of its capacity. Why that costs trillions, and how to build one. Coordination Is the Bottleneck (Essay, June 22, 2026, Phillip Nadjafov): What matching freight across 10,000+ vehicles taught us. Most of the cost comes after the match, and the physical economy needs a live index. The Accidental Context Layer (Essay, June 8, 2026, Phillip Nadjafov): How the ELD mandate put a connected device in nearly every US truck, and how COVID turned that data into the start of a physical context layer. Pricing Capacity That Already Exists (AI Models, May 18, 2026, Oway Research): How Juno prices open space on US trucks already on the road from live capacity, carrier, demand and market signals, without breaking a delivery. The Half-Empty Truck (Notes, April 27, 2026, Oway Research): US trucks drive one mile in six empty and run loaded at a third to half of capacity. What that costs, and how China cut empty running with freight matching. Why We Started Oway (Essay, April 6, 2026, Phillip Nadjafov): Oway's founder on what building a defense vehicle manufacturer taught him about coordination, and why Oway started with the empty space in American trucks. The New Rules of the Road (Guest Article, March 31, 2026, Bart A. de Muynck): Rising fuel costs make half-empty trucks unsustainable. How consolidating freight into space already moving turns fuel cost into an advantage. Euri Lighting (Case Study, February 21, 2026, Will Honda): How Euri Lighting cut freight costs by up to 53%, saved hours of quoting every week and shipped about 200 loads with zero damage claims. Vybes (Case Study, February 21, 2026, Will Honda): How a fast-growing beverage brand cut freight costs by up to 63% across California and Nevada by shipping on trucks already making the trip. RTD Delivery Systems (Carrier Study, February 21, 2026, Will Honda): How a regional carrier filled the empty space on its existing lanes and earned $1,520 in extra revenue from five shipments in its first week on Oway.

## Reindustrialization Needs a Coordination Layer | Oway Research
URL: https://www.oway.io/research/reindustrialization-needs-a-coordination-layer/

Reindustrialization Needs a Coordination Layer. Humanoid robots, driverless trucks and new factories are arriving fast. The industrial market they have to work with still runs on EDI, mainframes and PDFs. By Phillip Nadjafov, October 5, 2026. Robots, driverless trucks and new plants are arriving fast, but legacy industry still runs on EDI and PDFs. Why reindustrialization needs a coordination layer.

In brief: America is rebuilding its industrial base, and every new plant still plugs into a coordination model built on phone calls, EDI, PDFs and spreadsheets. Autonomous machines are arriving quickly. Driverless trucks have hauled commercial freight in Texas since 2025, and humanoid robot makers are building factories sized for mass production. None of them can read an EDI 204 or answer a PDF rate confirmation. A few vertically integrated operators can coordinate robots, trucks and factories because they own the whole stack. IOI makes that capability open and easy to set up for everyone else. IOI gives both worlds one open schema, so autonomous machines can quote, book, tender and produce with the legacy industrial market as it exists today.

America has decided to reindustrialize. New factories, semiconductor fabs, battery plants, energy projects and defense capacity are going into the ground across the country, backed by hundreds of billions of dollars in public and private investment. I spent the first part of my career building a manufacturer, so I am glad to see it. I also know what those new plants will run into on their first day of operation. New buildings, old coordination A new plant arrives with modern machines and modern software. Then it plugs into the same coordination model the rest of industry has used for decades. Suppliers confirm by email. Carriers receive load tenders as EDI messages from systems built in the 1980s. Bills of lading and rate confirmations move as PDFs. Inventory lives in one system and the production schedule in another, and partners share what they must, late and by hand. The results are familiar. Buffer inventory to cover uncertainty. Lines waiting on parts that exist somewhere else. Trucks leaving the dock at a third to half of their capacity, as they do across American freight, with one mile in six driven empty. 1 People spending their days moving information between systems that cannot see each other. Building new plants on that foundation means pouring new concrete onto an old coordination model, and inheriting its costs from the first shipment. The machines are arriving faster than the market can absorb them The more important shift is happening on the machine side. Driverless trucks began hauling commercial freight between Dallas and Houston in May 2025. 2 Humanoid robot makers are building factories sized for mass production. Robot makers, autonomous trucking companies and factory automation vendors are moving at a pace the industrial economy has not seen before. These machines are capable, and they will need work. A driverless truck needs to know what to carry, where to go and what the trip is worth. A fleet of humanoid robots needs orders, inventory and schedules. A highly automated factory needs to know what to build before anyone asks. The market they have to get that work from was not built for them. Much of American freight is still tendered through EDI over value-added networks, often generated by mainframe and AS400 systems. Purchase orders arrive as PDFs. Status updates arrive by email. Capacity is negotiated by phone. A driverless truck cannot read an X12 204 sitting in a VAN mailbox. A robot cannot reply to a rate confirmation attached to an email. The companies best positioned for this shift are the ones that own the whole stack. When one company builds the robots, the vehicles and the factories they work in, on its own systems, its machines can work together because it controls every system they touch. Almost no one else is in that position. Without a shared translation layer, autonomous machines will be limited to the few customers who can rebuild their operations around them, and the rest of the industrial economy will keep running on people relaying messages. That gap is one of the biggest risks to reindustrialization, and one of the least discussed. IOI is built to close it. It makes the kind of integrated coordination only the largest vertically integrated operators have today open and easy to set up for every manufacturer, carrier and distributor, on top of the systems they already run. Legibility is the advantage The countries that lead this century will all have capable machines. What will separate them is whether their physical economies are legible enough for those machines to coordinate with everything else. Some countries will get there from the top down. China, with heavy government backing, pushed freight-matching platforms onto millions of drivers' phones to drive down empty running, and its logistics costs fell from 18% of GDP in 2012 to 14.4% in 2023. 3 America's better path is a market: a neutral layer every company can plug into, with full control over what it shares. America also starts with an advantage it rarely gets credit for. Thanks to the ELD rule, nearly every truck in the country already reports where it is. What changes when the layer exists A new plant on IOI New plant IOI node Legacy partners EDI, mainframes, PDFs and spreadsheets, as they run today Driverless trucks Claim loads tendered from any system Robots Read orders, inventory and schedules Automated lines Accept requests to produce Agents Plan inside the limits you set Oway. IOI is built to be that layer. Each company runs its own node, connected to the systems it already has, legacy and autonomous alike. The node keeps an IOI data lake built on an open-source schema, and that schema covers actions as well as state: read, write, produce, quote, book, track, tender and capacity, among others. Once a new plant is on IOI, it can see what its suppliers can deliver and when, which carriers have open space heading its way and where its inputs and outputs are at any moment. Its own agents can plan production against live conditions inside limits the plant sets. Juno can price, schedule and route its freight in seconds. The same holds for the machines. A driverless truck on IOI can claim a load that was tendered from an AS400, because the node has already translated the tender into the open schema. A robot fleet can read the orders and inventory that live in an ERP. An automated line can accept a request to produce from a customer's planning system. The legacy market keeps running as it does today, and the autonomous market gains a way to work with it immediately. What we are building Oway started with the empty space in trucks because it was the clearest place to prove the idea, down to the pallet. Today more than 10,000 vehicles are on our network, shippers move freight at up to 50% below market and carriers earn more on lanes they already drive. IOI extends that approach to production, warehousing, robotics, energy and every operation that makes and moves things. Juno brings industrial AI that acts inside clear permissions. Oway OS is where the work runs. This is the last note in our series on indexing the physical economy, and the work is just beginning. The challenge of our generation is to make the physical world legible, coordinated and trustworthy enough for intelligence to act on. It is very winnable, and we would love to build it with you. Join the team or talk to us about your operation. American Transportation Research Institute (2025), An Analysis of the Operational Costs of Trucking , for 16.7% empty miles in 2024; American Trucking Associations, Trucking Trends , for average Class 8 payloads of about 29,000 lbs against rated capacities of 60,000 to 90,000 lbs. See The Half-Empty Truck .  ↩ Aurora Innovation (May 1, 2025). "Aurora Begins Commercial Driverless Trucking in Texas." Business Wire.  ↩ People's Daily (February 2025). "Reduced logistics costs help release China's economic vitality." See also Xu, X. et al. (2026), Nature Communications 17, 4714.  ↩

## Juno AI: How Oway's Industrial AI Works | Oway Research
URL: https://www.oway.io/research/juno/

Juno AI.  By Oway Research, October 1, 2026. What Juno, Oway's industrial AI, reads, what it decides, the guardrails it runs under and how we evaluate it.

In brief: Juno is Oway's industrial AI. It reads live state through IOI and decides what work should cost, where it fits and who should do it. It reads capacity, identity, demand, market and operation signals, and returns a price, a plan and an action. Every action runs inside scopes the owner grants, holds existing delivery windows and is logged with its inputs.

Overview Juno is Oway's industrial AI. It reads the live state of an operation through IOI and decides what should happen next: what a piece of work should cost, where it fits, and who or what should do it. Juno's first and largest deployment prices and routes freight onto trucks already on the road, across a network of more than 10,000 vehicles. Inputs Signal What Juno reads Capacity Open space, position and route of every vehicle and asset on the network. Identity Operating authority, insurance and equipment for every carrier and vehicle. Demand Requests, orders and documents: origin, destination, quantity, weight, class and handling requirements. Market Real quotes, bookings and acceptances across the network. Operation Schedules, facility hours, dock windows and constraints from the customer's IOI node, with permission. Outputs Output What Juno produces Price A price for the work, with the cost of the detour or change it requires. Plan Where the work fits: the vehicle, machine or crew, the stop order and the timing. Action A booking, dispatch or reroute, taken directly or proposed for approval. Guardrails Juno adds work to a route only when it holds the existing delivery window. Every action runs inside the scopes the operation's owner grants. Commitments that move money or change a plan can require a person's approval. Every decision is logged with its inputs and outcome. How we evaluate Juno Price accuracy: the gap between Juno's price and the realized cost of the work. Win rate: how often a price is accepted, by lane and by how long the lane has been active. Latency: time from request to price. Plan quality: on-time performance and added distance for every inserted stop. Benchmark reports are shared with enterprise customers and research partners. Request access . Intended use Juno is built to price, schedule, route and dispatch physical work for companies that run on Oway, and for AI agents acting on their behalf through MCP.

## Letting AI Act in the Physical World: Scopes and Audit | Oway
URL: https://www.oway.io/research/letting-ai-act-in-the-physical-world/

Letting AI Act in the Physical World. Permissions, approvals and audit for agents that move goods, machines and money. By Oway Research, September 21, 2026. How Juno and other AI agents act on real operations through IOI and MCP, with read, propose and act scopes, approvals and a full audit trail.

In brief: AI agents reach an IOI node through MCP with one of three scopes the owner sets: read, propose or act. Actions that commit money or change a plan can require a person's approval, and every decision is logged with its inputs. Content that arrives from outside, such as emails, documents and form submissions, is treated as data. It can inform a decision and can never change the rules.

Software that reads an operation is useful. Software that acts on one is far more useful, and it has to be held to a much higher standard. A wrong answer from a chatbot costs a reread. A wrong action in the physical world costs a missed delivery, an idle line or money committed to the wrong carrier. Here is how we think about letting AI act on the physical world. One door for every agent Juno, Oway's industrial AI, and any MCP-compatible agent a company chooses reach an IOI node the same way: through the Model Context Protocol, with permissions the node's owner sets. Our own AI uses the same door, under the same rules, as every other agent. Through MCP, an agent works with the same IOI schema operations as any other connected system: reading inventory, checking capacity, quoting, booking, tendering a load or asking a plant to produce. What it is allowed to do with them depends on its scope. Every agent gets a scope: Three scopes, granted by the owner Read Query state and use IOI Search No changes Propose Suggest actions for a person to accept Waits for approval Act Take defined actions inside its grant Approval for money or plan changes, if set Every read, proposal and action is logged with its inputs and who approved it. Oway. Scopes can be narrowed or revoked at any time. Read. The agent can query state and use IOI Search. Propose. The agent can suggest actions, which wait for a person or another approved system to accept them. Act. The agent can take defined actions directly, inside the limits of its grant. Scopes can be narrowed to specific data, facilities or actions, and revoked at any time. Most companies start an agent on read, review its proposals for a while, and then grant it act on specific, well-understood operations. People stay in the loop where it counts Some decisions should always have a person behind them. Commitments that move money or change a plan can be set to require approval, and Juno confirms before anything is charged. A proposed fix to a dock conflict, for example, shows up for the site lead with the reasoning and the alternatives, and goes ahead only when they say so. Rules that come from the real world Juno follows rules that come from the physical world and from regulation. It adds a stop only when every existing delivery window still holds. It verifies carrier authority, insurance and ELD connection before freight moves. It respects the weight and space left in the trailer and the hours a driver has left. Every action runs inside the scopes the owner grants. Treat outside content as data Industrial AI reads a lot of content it did not write: EDI messages, emails, purchase orders, nightly files, PDFs and messages from partners. Any of them could contain text written to manipulate an AI. We treat all outside content as untrusted data. It can inform a decision. It can never change the rules an agent runs under or the permissions it holds. A full record Every decision Juno or another agent makes is logged with its inputs, its outcome and who approved it. When a customer asks why a load moved or a window changed, the answer is on the record along with the data behind it. Why this matters now AI is quickly getting good enough to run much of the coordination work people do by hand today. The companies that benefit will be the ones that can let it act, confidently, inside clear limits. IOI and MCP are built to give them exactly that. For developers, the docs cover MCP, the API and scopes. For security details, see our Security page. In the last note of this series, we step back and look at what all of this means for American industry.

## IOI Search: Ask Your Industrial Operation Anything | Oway
URL: https://www.oway.io/research/ioi-search/

IOI Search: Ask Your Operation Anything. Plain-English answers about facilities, products, lanes, suppliers and carriers, with the numbers and sources behind them. By Oway Research, September 7, 2026. IOI Search lets anyone ask an industrial operation a question in plain English and get the answer, the numbers and the sources from its live data lake.

In brief: IOI Search is the default way to use a node. Ask in plain English and get the answer, the numbers and the sources. Answers come from the node's live data lake, so they reflect what is happening this morning across every connected system. Search respects the same permissions as the rest of IOI, and every answer traces back to the records it came from.

Most operational questions take ten seconds to ask and two days to answer. Which sites are nearly full this week? Which suppliers keep missing their dock windows? Where is product sitting the longest? Today, answering any of those means pulling exports from several systems, joining them in a spreadsheet and emailing it around, by which point the data has often changed. Analysts across industry spend a large share of their week on exactly this work. IOI Search answers the question directly. Ask it like you would ask a person Because an IOI node keeps a cleaned, current data lake of the operation, a question can reach across every connected system at once, whether the underlying data came from an ERP, EDI messages, nightly files or vehicle telemetry. You type it the way you would ask the most informed person in the building: "How are my facilities doing this week?" "Which suppliers are missing their dock windows into our western sites?" "Where is inventory sitting longest?" "What needs my attention today?" IOI Search answers in plain English, shows the numbers behind the answer and lists the sources it used, so anyone can check the work before acting on it. What comes back What an IOI Search answer looks like Ask How are my facilities doing this week? Reno DC is at capacity and inbound dwell is up for the third week. Fresno is worth watching. Dallas has room. Reno DC 94% full 3.8 days dwell At capacity Fresno plant 78% full 2.1 days dwell Worth watching Dallas DC 61% full 1.4 days dwell Healthy Sources ERP orders EDI 214 status ELD positions Nightly WMS file Illustrative example. Ask how your facilities are doing and you get each site with how full it is, how long things are sitting, the trend over recent weeks and a status: healthy, worth watching or at capacity. Ask which suppliers miss their windows and you get them ranked by on-window performance, with the actual shipments behind each number. Every answer links back to the records it came from, whether that is an order in the ERP, an EDI 214 status message or a position from a truck's ELD. Reads this morning's operation Traditional reporting describes how last month went. IOI Search reads the data lake as it stands right now, so "what needs attention today" reflects this morning's data across every connected system and permitted partner. For teams, software and agents People use IOI Search inside Oway OS. Software uses the same capability through the API, for example to ask a node a question from an internal tool. AI agents, including Juno, use it through MCP to understand the state of the operation before they propose or take an action. Search respects the permissions on the node. A partner or agent sees only what its grant allows, and every query is logged. From answers to action An answer is most useful when it leads somewhere. If IOI Search shows two inbound loads about to collide at a site that is already nearly full, Juno can propose a fix, such as moving one delivery to an open window, and the site lead can approve it. That handoff from seeing to doing is the subject of the next note: how we let AI act in the physical world safely. You can try IOI Search on the IOI page .

## Inside an IOI Node: Data Lake, Schema, Permissions | Oway
URL: https://www.oway.io/research/inside-an-ioi-node/

Inside an IOI Node. How a customer-owned node turns EDI, mainframes, PDFs and telemetry into one clean, queryable data lake. By Oway Research, August 17, 2026. What an IOI node connects to, how its data lake cleans legacy and machine data into an open-source schema, and how permissions, audit and ownership work.

In brief: An IOI node connects to the legacy surfaces and physical assets a company already has, and keeps an IOI data lake, a cleaned, queryable set of its industrial data built on an open-source schema. An EDI 204, a PDF rate confirmation and a spreadsheet row describing the same load all land as one record that software, agents and machines can act on. Each node is private and customer-owned. Every connection to a partner, agent or machine starts with an explicit, scoped and revocable grant, and every access is logged.

An IOI node is the part of IOI a company actually runs. It sits beside the systems the company already has, keeps the company's IOI data lake current and controls who and what can use it. What a node connects to A default node is live in days. It starts as a monitoring surface for the company's freight quotes, and IOI optimizes physical operations from the secured data lake from there. Everything below is optional and can be connected at any time afterwards, without replacing anything the company already runs. The optional connections fall into two groups. Group Examples EDI over a VAN X12 204 load tenders, 214 status updates, 990 responses Mainframe and AS400 Green screens and COBOL batch jobs Flat files over SFTP Nightly CSV drops Email and PDF Bills of lading, rate confirmations, invoices Spreadsheets Excel on a shared drive Custom stacks Whatever was built in house Vehicle telemetry Position, hours of service, empty space Self-driving trucks Vehicles that take a load and route themselves Automated factories Lines, docks and throughput Deployed robotics Arms and movers on the floor The first seven are the legacy surfaces where most industrial information lives today. The last four are the physical assets that increasingly do the work. A node connects to both, along with suppliers, carriers and customers who choose to connect. From raw data to a data lake Each of those systems describes the world in its own format. The same load can arrive as an EDI 204 from a mainframe, a rate confirmation PDF attached to an email and a row in a dispatcher's spreadsheet. The node reads all three, reconciles them and writes one clean record into the company's IOI data lake. One load, four legacy formats, one IOI record Four formats, one load EDI 204 from an AS400 ST*204*0001~B2**OWAY**SH77412~S5*1*LD~ Rate confirmation PDF Load SH77412 · 9 pallets · Coppell to Laredo Dispatcher spreadsheet SH77412 | 9 plt | 7,850 lb | Thu AM Nightly SFTP file SH77412,COPPELL TX,LAREDO TX,9,7850 IOI data lake tender { load: "SH77412" , origin: "Coppell, TX" , dest: "Laredo, TX" , pallets: 9 , weight: 7850 , pickup: "Thu 08:00" } Anything that speaks IOI Carrier truck claims it from its ELD-connected app Self-driving truck takes the load and routes itself Juno quotes, books and tracks it The same load arrives as an EDI 204 from an AS400, a rate confirmation PDF, a spreadsheet row and a nightly file. The IOI node reconciles them into one tender record that a carrier truck, a self-driving truck or Juno can act on. Illustrative example. The data lake is a cleaned, queryable set of the company's industrial data, IOI-compatible and built on an open-source schema. It stays current as the operation moves. When a telematics feed shows a truck's open space change, or a nightly file updates inventory, the data lake updates, and so does every system and agent that depends on it. Because the schema is open, anything that speaks it can work with the data lake directly: the company's own software, Juno, a partner's planning system, a robot fleet or a self-driving truck. And because the schema covers actions as well as state, a node can do more than report. It can quote, book, tender, write a status update to another factory or ask a plant to produce, within the permissions the owner sets. Owned by the company that runs it The node and its data lake belong to the company. Nothing on it is pooled with another customer, and Oway uses only the access a company grants to its own node to run and optimize that company's work. Permission before every connection Sharing is where most of the value of an index comes from, and also where most of the risk is. IOI treats every connection the same way: Explicit. A connection to a supplier, carrier, customer, AI agent or machine starts with the owner's permission. Scoped. Each grant names exactly what can be read, proposed or done, and for which data. Revocable. Grants can be narrowed or revoked at any time. Recorded. Every access is logged with who made it, what was used and what changed. That lets two companies share exactly what one purpose needs, such as dock windows for a shared shipment, and nothing else. A carrier can see when a dock is open without seeing the shipper's margins. Built to be read by machines A node is designed for people and for machines. People use IOI Search and Oway OS. Software uses the API to query the data lake and subscribe to changes. AI agents, including Juno, reach the node through MCP with read, propose or act scopes the owner sets. In the next note, we show what it looks like to ask a node a question in plain English. For the full security model, visit our Security page.

## Introducing IOI, the Internet of Industrials | Oway Research
URL: https://www.oway.io/research/introducing-ioi/

Introducing IOI, the Internet of Industrials. One open schema that lets the systems industry already runs, and the autonomous machines arriving now, read and act on the same physical world. By Phillip Nadjafov, July 27, 2026. IOI connects legacy industrial systems and autonomous machines through an open-source schema, so companies and AI can read, quote, book and produce together.

In brief: A few vertically integrated operators built their own coordination layers across factories, fleets and robots. IOI makes that capability open and easy to set up for every company. The physical economy runs on two worlds that cannot talk to each other. One is ERP, EDI, mainframes, nightly flat files, PDFs and spreadsheets. The other is telemetry, self-driving trucks, automated factories and robots. IOI connects both. Each company runs an IOI node holding its IOI data lake, a cleaned, queryable set of its industrial data built on an open-source schema. The schema covers both reading state and taking action, across companies. A system or an agent can read inventory, write a status update to another factory, ask a plant to produce, quote, book, tender and track, within permissions the owner sets.

In The Physical Economy Has No Index , I argued that the physical economy needs a neutral, live index of what capacity exists, where it is, what it costs and what is moving through it. Today I want to introduce the system we are building to make that index possible. We call it IOI, the Internet of Industrials. Two worlds that cannot talk The physical economy runs on two very different sets of systems. The first is the legacy surface that most of industry still runs on. EDI over a value-added network, where a load tender is an X12 204, a status update is a 214 and an acceptance is a 990. Mainframes and AS400 green screens running COBOL jobs. Flat files dropped over SFTP every night. Bills of lading, rate confirmations and invoices sent as PDFs by email. Spreadsheets on a shared drive. And whatever custom stack each company built in house over the past thirty years. The second is the new physical layer. Telematics that report a vehicle's position, hours and empty space. Self-driving trucks that can take a load and route themselves. Automated factories with lines, docks and throughput that software can schedule. Robots that pick, move and load on the floor. These two worlds do not share a language. A self-driving truck has no way to read an EDI 204 from an AS400. An automated factory cannot answer a rate confirmation that arrives as a PDF. So people translate between them, the same way they have translated between legacy systems for decades, and the autonomous machines end up waiting on email. IOI connects the systems industry runs on to the machines arriving now Legacy surfaces EDI over a VAN 204, 214, 990 Mainframe and AS400 green screens, COBOL jobs Flat files over SFTP nightly CSV drops Email and PDF BOLs, rate cons, invoices Spreadsheets Excel on a shared drive Custom stacks whatever was built in house Internet of Industrials IOI node and data lake A cleaned, queryable set of your industrial data, IOI-compatible and built on an open-source schema. Schema, for example READ how many units of a SKU sit in a warehouse right now WRITE send a status update to another factory PRODUCE ask a plant to run more mild steel QUOTE price nine pallets against trucks already rolling BOOK call a truck for 8 units of mild steel by 8am tomorrow TRACK position and status without a phone call TENDER offer the load straight to the carrier CAPACITY what is empty tonight, and where it is headed Physical assets Vehicle telemetry position, hours, empty space Self-driving trucks takes the load, routes itself Automated factories lines, docks, throughput Deployed robotics arms and movers on the floor IOI is the layer in the middle. Integrated coordination for every company A few companies have already solved this problem for themselves. The largest vertically integrated operators built their own coordination layers, where factories, warehouses, fleets, robots and planning systems share one internal model of the operation. A robot, a truck and a production plan can act on the same information in real time. That capability is a large part of how those companies move as fast as they do. It also took them years and enormous engineering teams to build, and it only works inside their own walls. A mid-sized manufacturer, a regional carrier or a distributor running on an AS400 has no realistic way to build the same thing. IOI makes that capability open and easy to set up. Connecting a node works much like adding a carrier, with no multi-year integration project, and every company that connects can coordinate with the others through the same open schema. The coordination advantage that used to require owning the whole stack becomes available to the entire market. The node and the data lake Each company runs its own IOI node. The node connects to the systems the company already has, on both sides, and nothing gets ripped out. Inside the node is the company's IOI data lake : a cleaned, queryable set of its industrial data, IOI-compatible and built on an open-source schema. A default setup takes days. Every node starts as a monitoring surface for the company's freight quotes, and IOI optimizes physical operations from the secured data lake from there. ERP, EDI and every other surface are optional, and can be added at any time afterwards. As a node grows, it follows three steps: Connect. The node starts with freight quotes, then links to whichever legacy surfaces and physical assets the company chooses to add, along with suppliers, carriers and customers who choose to connect. Clean. It normalizes what arrives, whether that is an EDI message, a nightly CSV, a PDF bill of lading or a telemetry stream, into the IOI schema, and keeps the data lake current as the operation moves. Act. People, software and AI agents read and act on the data lake through IOI Search, the API and MCP. One load, four legacy formats, one IOI record Four formats, one load EDI 204 from an AS400 ST*204*0001~B2**OWAY**SH77412~S5*1*LD~ Rate confirmation PDF Load SH77412 · 9 pallets · Coppell to Laredo Dispatcher spreadsheet SH77412 | 9 plt | 7,850 lb | Thu AM Nightly SFTP file SH77412,COPPELL TX,LAREDO TX,9,7850 IOI data lake tender { load: "SH77412" , origin: "Coppell, TX" , dest: "Laredo, TX" , pallets: 9 , weight: 7850 , pickup: "Thu 08:00" } Anything that speaks IOI Carrier truck claims it from its ELD-connected app Self-driving truck takes the load and routes itself Juno quotes, books and tracks it The same load arrives as an EDI 204 from an AS400, a rate confirmation PDF, a spreadsheet row and a nightly file. The IOI node reconciles them into one tender record that a carrier truck, a self-driving truck or Juno can act on. Illustrative example. A schema for reading and for acting IOI's schema covers taking action as well as reading state, between companies and between machines. A few examples of what it covers: Operation Example Read How many units of a SKU sit in a warehouse right now Write Send a status update to another factory Produce Ask a plant to run more mild steel Quote Price nine pallets against trucks already rolling Book Call a truck for 8 units of mild steel by 8am tomorrow Track Position and status without a phone call Tender Offer the load straight to the carrier Capacity What is empty tonight, and where it is headed Because every connected company speaks the same schema, these operations work across company lines. A manufacturer's planning agent can check a supplier's capacity, ask its plant to produce, book a truck with open space heading that way and track the result, without anyone re-keying an EDI message or forwarding a PDF. A self-driving truck can claim a tender that originated on an AS400. The legacy system keeps running exactly as it does today. It simply gains a counterpart that can act on what it says. The schema is open source because a common language only works if anyone can speak it. Fleets, factories, robot makers and software companies can build to it directly, and every company that connects makes the network more useful for every other. Private by design The node and its data lake belong to the company that runs them. Its data is never pooled with another customer's. Connecting a partner, an agent or a machine takes the owner's explicit permission. Every grant has a defined scope, every grant can be narrowed or revoked at any time, and every access is logged. AI agents get one of three scopes: read, propose or act. We learned the importance of this in freight. Carriers will not share where their open space is if it helps a competitor undercut them, and shippers will not share their volumes if they end up in someone else's rate sheet. An index only works when the people feeding it trust it. What becomes shared knowledge is the aggregate shape of the economy. Every individual position stays private. What runs on IOI IOI is the foundation for everything Oway builds: Oway OS uses it to optimize revenue, facility throughput, assets and suppliers, and to move goods on capacity already in motion. IOI Search lets anyone ask the operation a question in plain English and get the answer with the numbers and sources behind it. Juno , our industrial AI, reads the data lake and quotes, books, schedules, routes and dispatches work within the permissions a company sets. AI agents and machines read state and propose or take actions through MCP, under the same rules as everyone else. Freight was the first surface because the data already existed, thanks to the ELD rule, and the waste was easy to measure. IOI is how the same approach reaches production, warehousing, robotics, energy and every operation that makes and moves things. Over the next few notes we will open up an IOI node, show what IOI Search can answer, and explain how we let AI act in the physical world safely. To see it on your own operation, request a demo or read the docs .

## The Physical Economy Has No Index | Oway Research
URL: https://www.oway.io/research/the-physical-economy-has-no-index/

The Physical Economy Has No Index. Why the most important companies of the next century will be the ones that make the world legible. By Phillip Nadjafov, July 6, 2026. The internet became useful once it was indexed. The physical economy still has no live index of its capacity. Why that costs trillions, and how to build one.

In brief: Trucks leave warehouses one-third to half full, and moving goods costs the US $2.6 trillion a year, 8.7% of GDP. The physical economy needs a neutral, private index of live capacity: what exists, where it is, what it costs and what is moving. Oway is building that index, starting with empty truck space, as the backend autonomous machines and reindustrialization will plug into.

One of the largest industrial problems of our time is slowly killing our economy and most have no idea it exists. It touches every physical thing around you and charges a tax you don’t get a benefit for. The American industrial sector is operating at around half of its productive efficiency, irrespective of economic conditions. 1 That might sound like a shock, but it’s by design of the vehicles moving everything around: the trucks. This trillion-dollar industry acts as an artery of the physical economy and doesn’t have a mechanism to know how truly inefficient it is. Because of the lack of a reliable big nervous system, trucks depart warehouses with only one-third to half of their capacity filled and cannot utilize their empty space even when there is high demand. 2 Warehouses sit idle, machines wait for work that is already needed three states away, robots cannot fix it, and you the consumer pay the cost. America is flying blind through the heart of its own economy, paying a tax it doesn’t even know exists. Fixing it would unlock hundreds of billions of dollars in productivity in America alone. And the pieces have now fallen into place to do it. In fact, we have already solved this problem once. The Internet of Real Things The internet indexed humanity's collective data, search made it query-able. Limitless, effectively free information enabled us to find and match novel wasted supply to demand instantly at almost no cost. 3 It took historically binary decisions and split them into countless new outcomes. The payoff was trillions of dollars in value and a generation of asset-light companies that we now use as utilities, all built on one simple idea: if you can see everything and match it for the right use case instantly, historical waste turns into value. This same pattern is beginning to fall into place again. Over decades, companies have started connecting their factories, vehicles, and robots to the internet, and the sensors on these devices are transmitting new, novel information we’ve never seen before. Unfortunately, the physical economy is still stuck in behavioral gridlock. The industrial economy, which is the overwhelming majority of what humans actually produce and the entirety of our survival (food, medicine, materials, every object sitting around you), still gets orchestrated roughly the way it did in the 1980s. Binary, simple decisions because we physically cannot do better relative to the time and information available. And so far, we have accepted that as the best it can be. The bill for that acceptance is not abstract: moving goods through the US economy now costs $2.6 trillion a year, 8.7 percent of GDP. 4 We are missing a layer. ** ** The Index Layer The physical economy needs its own index. A live matrix of what productive capacity exists, where it is, what it costs, and what is actually moving through it. This index has to be neutral, akin to how the index of the internet belongs to no single website. Neutral rails that everyone can use without handing their business to a competitor. Once that index exists, three things happen almost instantly. Matching becomes instant. Waste becomes visible. Prices will equilibrate. All because there finally exists a physical source of truth. 5 I started working on this problem with my team, beginning where the physical economy is most broken and most legible at the same time: the supply chain. Specifically, we believe the empty space in trucks can create a structurally new category of moving goods. We are building an index of live, empty carrier capacity, and treating it as a real-time measure of how inefficient the economy actually is. We operate this network with AI that reads the index and decides what should go where, filling capacity that would otherwise have driven empty. A wave of startups tried to fix our supply chain. Most of them died trying. It is worth being precise about why. They digitized the broker, not the market: apps layered over phone calls, marketplaces competing on price. And behind every load? A human still doing the actual coordination. The cost of coordinating never fell, so they inherited the same thin margins as the industry they set out to replace. 6 None of them built the index; matching without an index is just guessing faster. And the coordination itself could not be automated, because software could not yet read context. That constraint has expired. We do not need more logistics companies doing the old thing slightly faster. We need the neutral coordination layer the physical economy was never given, and indexing trucks is only the first surface. This is also why the index has to be built as infrastructure from day one, not retrofitted later. Every load coordinated through it becomes structured data: what moved, where, when, at what price, and what capacity was left behind. Compounded over years, that becomes the ground truth of the physical economy, a data backend designed to be read not just by people but by machines. An autonomous truck does not need an app. It needs an API to the economy: what to carry, where to go, what the trip is worth. A dark factory needs to know what to build before anyone asks. Autonomy without context is a fleet of brilliant machines with no nervous system. We are designing the index as that backend now, so that when the machines arrive, the world they need to read already exists. We will not build the trucks or the factories or the robots. We are building the thing they will all have to plug into. Neutrality = Trust, Trust = Scale An index this powerful only works if it is trusted, because the heart of the industrial economy also houses its most sensitive information on private business behavior. Who ships what, to whom, when, and at what cost is some of the most sacred information companies have. Markets with hidden information do not merely leak value; they unravel entirely when participants cannot trust what they cannot verify. 7 A neutral coordination layer must abstract specifics away by design, not as a feature bolted on later. The index has to surface the match without exposing the participants. It has to give every player its full benefit without forcing them to surrender position or leverage to access it. The index itself should ultimately be open, a reference anyone can build on. And what compounds into that reference is the aggregate, never the participant: the shape of the economy becomes shared knowledge while every individual position stays private. That is the deal, and it only works one way. Neutrality and privacy are not constraints on this idea. They are what make the rails usable by everyone at once. You earn the right to coordinate the economy only by being the party that everyone can plug into without fear. Raising Ships The part that matters beyond efficiency is what this index does for everyone globally. Coordination is deflationary in the best possible sense of the word. Every idle machine, every empty mile, every shipment that sits and waits is a cost, and that cost does not vanish. It is passed on to you. 8 The waste of the physical economy is a hidden variable in your rent, your groceries, your medicine, and every object around you. You pay for the disorder of a system you cannot even see. Minimize the waste in coordination and you lower the cost of physical existence itself. Then add the machines. When they plug into a layer that already knows what the world needs and where, the cost of moving and making things falls toward the cost of the energy and intelligence required to do it. That is the difference between a century of material abundance and a century of stagnation: affordability not as a slogan but as a direct consequence of letting intelligence act on a world it can finally read. Building the REST There is a larger clock running. America has decided to reindustrialize: new factories, new fabs, new energy, new defense capacity, hundreds of billions in steel and silicon going into the ground. But we are attempting to rebuild an industrial base on top of a coordination layer from the 1980s. Every new plant inherits the same darkness: the phone calls, the guesswork, the waste. Reindustrialization without an index is pouring concrete into a blind system. The nations that win this century will not be the ones with the most machines. They will be the ones whose physical economies are legible enough for machines to run. Some will coordinate their industry by mandate. We will do it by market. That is the challenge of our generation, and it is winnable. Now is the time for founders who look at the physical world the way the last generation looked at information and simply refuse to accept that it has to stay dark, fragmented, and dumb. We need coordination layers for energy, materials, manufacturing, and verticals that do not even exist yet. The people who successfully build them will be among the most important builders of the century, not because they optimized a niche, but because they are giving the physical economy the nervous system it was never born with. The future everyone keeps promising, the self-driving everything and the robotics that build and the abundance that follows, does not arrive on the back of better hardware alone. It arrives the moment the physical world becomes legible, coordinated, and trustworthy enough for intelligence to act on it. Someone has to build that layer. We are building a part of it. Come build the rest. In manufacturing, Overall Equipment Effectiveness (OEE), the standard measure of realized output against productive potential (Nakajima, S., 1988, Introduction to TPM, Productivity Press), averages roughly 60 percent across industry benchmark surveys, with 40 to 60 percent typical of most operations and 85 percent considered world class. Peer-reviewed studies of road freight measure the same shape: average vehicle load factors at or below half of capacity (e.g., Transportation Research Part E, 2021; European Environment Agency load factor methodology, 2001). ↩ In US trucking, empty (deadhead) miles averaged 16.7 percent of all miles driven in 2024, per the American Transportation Research Institute (2025), An Analysis of the Operational Costs of Trucking; ATRI is a 501(c)(3) nonprofit research organization. American Trucking Associations fleet data has shown average Class 8 payloads of roughly 29,000 pounds against rated capacities of 60,000 to 90,000 pounds (Trucking Trends), a pattern that has persisted across decades and cycles. ↩ Rochet, J.-C., and Tirole, J. (2003). “Platform Competition in Two-Sided Markets.” Journal of the European Economic Association, 1(4), 990-1029. ↩ Council of Supply Chain Management Professionals and Kearney (2025). Annual State of Logistics Report. US business logistics costs totaled $2.6 trillion, 8.7 percent of GDP. ↩ Hayek, F. A. (1945). “The Use of Knowledge in Society.” American Economic Review, 35(4), 519-530. ↩ Coase, R. H. (1937). “The Nature of the Firm.” Economica, 4(16), 386-405. ↩ Akerlof, G. A. (1970). “The Market for ‘Lemons’: Quality Uncertainty and the Market Mechanism.” Quarterly Journal of Economics, 84(3), 488-500. ↩ The American Transportation Research Institute (2025) puts the average marginal cost of operating a truck at $2.26 per mile in 2024. Every one of the industry’s empty miles incurs this cost while generating no value, an expense ultimately embedded in consumer prices. ↩

## Coordination Is the Bottleneck in Freight | Oway Research
URL: https://www.oway.io/research/coordination-is-the-bottleneck/

Coordination Is the Bottleneck. What running a network of more than 10,000 vehicles taught us about where the time actually goes. By Phillip Nadjafov, June 22, 2026. What matching freight across 10,000+ vehicles taught us. Most of the cost comes after the match, and the physical economy needs a live index.

In brief: On a network of more than 10,000 vehicles, trucks are plentiful. Timely, trusted information about where their open space is turns out to be the scarce resource. Most of the cost of a shipment comes after the match. Verification, scheduling, documents and payment still run on phone calls, email and EDI across most of the industry. Matching without a shared, live picture of capacity is guessing faster. The physical economy needs an index.

Today more than 10,000 vehicles are on the Oway network. People assume the hard part of running it is finding trucks. America has plenty of trucks, and plenty of open space on them. The hard part is knowing, in time to act, which truck has room for a given shipment and is heading the right way. Capacity is everywhere and invisible On any given morning there is an enormous amount of open space on American highways. As we covered earlier in this series, one mile in six is driven completely empty, and the average loaded truck runs at a third to half of its rated capacity. 1 That is roughly 32 billion empty miles a year, plus the unused space on every loaded trip. The space is spread across thousands of trucks, changes by the minute and is known only to the drivers and dispatchers inside them. By the time that information travels through a phone call, an email and a load board to a shipper, the truck has usually left. Capacity information has a shelf life measured in minutes. ELDs solved part of this. Once carriers connect to Oway through them, Juno can see where open space is now and where it will be in an hour. On active lanes, a truck can be at a shipper's dock in an average of 30 minutes. Most of the work comes after the match Finding a truck with space is the first of six steps. Turning a match into a delivered shipment also means: Six steps after the match 01 Verify authority, insurance and ELD Today: phone, email, EDI, PDF Oway: automated 02 Fit the stop to the schedule and dock hours Today: phone, email, EDI, PDF Oway: automated 03 Agree on a price Today: phone, email, EDI, PDF Oway: automated 04 Produce the bill of lading Today: phone, email, EDI, PDF Oway: automated 05 Track and confirm delivery Today: phone, email, EDI, PDF Oway: automated 06 Pay the carrier, invoice the shipper Today: phone, email, EDI, PDF Oway: automated Oway. Verifying the carrier has active authority, the right insurance and a connected ELD. Confirming the stop fits the truck's schedule and the facility's dock hours. Agreeing on a price both sides accept. Producing the bill of lading. Tracking the load and confirming delivery. Paying the carrier and invoicing the shipper. Across most of the industry, people still do each of those steps by hand, moving information between EDI messages, PDF rate confirmations, emails and spreadsheets. That labor is a large part of why moving a few pallets costs what it does. It is also why so many freight startups that digitized the broker never changed the economics. The interface improved, while a person still did the coordination behind it. What automation needs We automated those steps one at a time. Every one depended on the same thing: a reliable, current picture of what exists, where it is, what it can do and what it costs. When that picture was stale, every automated decision got worse. A price built on yesterday's capacity is confidently wrong. When the picture was accurate, decisions that used to take a day took seconds. The lesson extends well beyond freight. The physical economy is limited by coordination, and coordination is limited by information that nobody has been able to share live, safely and without giving up their position to a competitor. China showed in freight how quickly load factors rise once capacity becomes visible. The same holds for factory lines, warehouses and every other asset with time to sell. From a network to an index The internet had this problem before search. The information existed, scattered across millions of computers, and most of it was effectively unfindable. Indexing it made it usable, and a large share of the modern economy grew on top of that index. The physical economy never got that layer. I wrote about what it would take in the next piece in this series, The Physical Economy Has No Index . After that, we introduce IOI, the system we are building to give it one. American Transportation Research Institute (2025), An Analysis of the Operational Costs of Trucking , for 16.7% empty miles in 2024; American Trucking Associations, Trucking Trends , for average Class 8 payloads of about 29,000 lbs against rated capacities of 60,000 to 90,000 lbs; Federal Highway Administration, Highway Statistics 2024 , Table VM-1, for 192.5 billion combination-truck miles. See The Half-Empty Truck .  ↩

## The Accidental Context Layer: ELDs, COVID and Freight Data | Oway
URL: https://www.oway.io/research/the-accidental-context-layer/

The Accidental Context Layer. How a logbook rule and a pandemic put America's trucks on the internet. By Phillip Nadjafov, June 8, 2026. How the ELD mandate put a connected device in nearly every US truck, and how COVID turned that data into the start of a physical context layer.

In brief: The federal ELD rule, fully in force by December 2019, put a connected device that records location, engine hours and duty status in the cab of roughly 3 million drivers. It was written to enforce hours of service. As a side effect, it gave America the first live, machine-readable map of where its freight capacity is. COVID made everyone care where their stuff was, and the market swings that followed made carriers care about every empty pallet position. Together they turned that map into the start of a physical context layer.

For most of trucking history, a driver's record of where he had been and how long he had driven was a paper logbook. The industry nicknamed them comic books, because paper logs were easy to fill in creatively. That detail matters because some of the most important data infrastructure in American freight came from a federal rulemaking about those logbooks. It did not come from a startup or a technology company. A rule about logbooks In December 2015, the Federal Motor Carrier Safety Administration published the electronic logging device rule. 1 The goal was simple and sensible: enforce hours-of-service limits so tired drivers stop driving. Paper logs were easy to fudge. An electronic device wired to the engine is not. The rule phased in over four years. Carriers had to install compliant ELDs by December 18, 2017, and trucks running older automatic recorders were given until December 16, 2019. 2 By the end of that decade, roughly 3 million drivers were logging their hours on a connected device, and FMCSA expected the change to save the industry more than a billion dollars a year, mostly in paperwork and crashes avoided. 3 How America's trucks got online Dec 2015 ELD rule published FMCSA finalizes electronic logging for hours of service. Dec 2017 Compliance date Carriers must log hours on an ELD. Dec 2019 Every truck on an ELD Grandfathered recorders retire. Roughly 3 million drivers covered. 2020 The pandemic Dry van spot rates reach about $2.35 a mile. Live tracking becomes expected. 2023 Oway founded Carriers connect through their ELDs to share open space. Today 10,000+ vehicles On the Oway network, with encrypted location and route data. Sources: FMCSA ELD rule and implementation timeline; DAT Freight and Analytics; Oway. The side effect is what matters for the physical economy. To log hours, an ELD has to know when the engine is running, how far the truck has moved and where it is. So the rule put a GPS-connected computer into nearly every interstate truck in the country, recording position, miles and duty status around the clock. A rule written to enforce driver rest also produced something nobody had before: a live, machine-readable map of where American freight capacity is. Then 2020 happened For a few years, that data mostly served compliance. Then the pandemic arrived and the entire country took a crash course in supply chains, from port congestion to empty shelves. Trucking had one of the strangest years in its history. Demand swung from collapse to a buying frenzy in a matter of months, and by late 2020 the national dry van spot rate had climbed to about $2.35 a mile, its highest in five years. 4 Every shipper wanted to know exactly where its freight was, and a phone call to the driver stopped being an acceptable answer. So carriers started sharing what their ELDs knew. Location feeds that used to stay inside a fleet's office got piped, with permission, to shippers, brokers and tracking tools. Live tracking went from a premium feature to the price of admission. When the market swung the other way and rates fell, the lesson flipped too. Carriers who had spent 2020 turning freight away suddenly cared about every empty pallet position on every trip. Same data, new question: where is the space I am not getting paid for? The start of a context layer Put those two shocks together and something new exists. For the first time, a large share of America's freight capacity is reporting where it is, how long its driver can keep driving and where it is headed, live, in a format software can read. That is the beginning of what I call a physical context layer: a machine-readable description of the real world that software and AI can reason about. China built its matching layer by putting an app in every driver's pocket. America got a sensor wired into the engine of nearly every truck, by regulation, and then a pandemic taught everyone to share it. The physical context layer, so far What coordination needs Added by Oway and IOI Open space and load factor Freight already on board Authority, insurance, equipment Dock windows and facility hours Price both sides accept What an ELD knows In nearly every cab since 2019 Location Miles and engine hours Duty status and hours left Oway. It is only the beginning, though. An ELD knows where the truck is and how long the driver can drive. It has no idea what is inside the trailer, what the truck is allowed to carry, how much room is left or what the plant down the road needs moved today. The load factor, the thing that actually matters, is invisible to it. That is the layer we went after. When carriers connect to Oway through their ELDs, Juno combines encrypted location and route data with what it knows about the freight already on board. It can see where open space is right now and where it will be in an hour. On active lanes, that means a truck at a shipper's dock in about 30 minutes on average. What it taught us A regulation meant to enforce driver rest became one of the most important freight data projects in American history, without anyone designing it to be one. It also taught us how much is still missing. Location is one signal. Coordination needs all of them: capacity, constraints, timing, cost and trust, shared across companies that do not want to give up their position. That is the problem the next note is about. Federal Motor Carrier Safety Administration (2015). Electronic Logging Devices and Hours of Service Supporting Documents , Final Rule, 80 Fed. Reg. 78292, published December 16, 2015.  ↩ FMCSA ELD implementation timeline. Compliance date December 18, 2017; carriers using grandfathered automatic onboard recording devices (AOBRDs) had until December 16, 2019.  ↩ FMCSA regulatory evaluation of the ELD rule. Roughly 3 million drivers affected; estimated net benefits of more than $1 billion a year, primarily from reduced paperwork and crash reductions.  ↩ DAT Freight and Analytics, 2020 truckload market reports. National average dry van spot rate of about $2.35 per mile in late 2020, a five-year high.  ↩

## Pricing Capacity That Already Exists: How Juno Prices | Oway
URL: https://www.oway.io/research/pricing-capacity-that-already-exists/

Pricing Capacity That Already Exists. How Juno decides what the empty half of a moving truck is worth. By Oway Research, May 18, 2026. How Juno prices open space on US trucks already on the road from live capacity, carrier, demand and market signals, without breaking a delivery.

In brief: Open space on a moving truck has a real cost. It is the detour, the minutes and the risk to every delivery already on board. Juno reads five groups of live signals and returns a price, a plan and an action in seconds. A price only counts if the existing delivery windows hold. Juno is graded on accuracy, win rate, speed and on-time performance.

Freight pricing was built for a world where a truck is either yours or someone else's. You look up the lane, check the rate table, add a fuel surcharge and send a quote. That model has nothing to say about the truck that is already driving to Houston with half its trailer empty. The truck is going anyway. The diesel is already burning. The real question is what it costs to add your freight to that trip, right now, without putting any delivery already on board at risk. That is the question Juno answers. What a price has to know When Juno prices a partial load, it is answering four questions at the same time: Where is the space? Which trucks have open positions on a route that passes near the pickup and the delivery. What does the stop cost? The extra miles, the extra minutes and the cost of running the truck for that time. Does the schedule survive? Whether every delivery already on board still lands inside its window. Will both sides say yes? What the shipper would pay anywhere else, and what makes the stop worth it to the carrier. A broker answers those by calling around, which takes hours. Juno answers them in seconds, from live data. The five signals How Juno turns live signals into a booked move Live signals Capacity open space, position, route Identity authority, insurance, equipment Demand lanes, pallets, weight, handling Market quotes, bookings, acceptances Operation schedules, hours, dock windows Juno Finds trucks with open positions Checks every delivery window holds Prices the detour and the stop Outputs Price including the detour Plan vehicle and stop order Action booked, or sent for approval Oway. Signals are read with each operation's permission. Juno works from five groups of live signals: Signal What Juno reads Capacity Open space, position and route of every vehicle on the network Identity Operating authority, insurance and equipment for every carrier and vehicle Demand Origin, destination, pallets, weight, class and handling requirements Market Real quotes, bookings and acceptances across the network Operation Schedules, facility hours and dock windows, with permission Out the other side come three things. A price that includes the cost of the detour. A plan that says which truck takes the freight and in what stop order. And an action : the booking itself, made directly or sent to a person for approval. An example Take a 53-foot dry van rolling south from Dallas to Houston with 13 of its 26 pallet positions open. By pallet positions, that is a load factor of 50%, close to the American average for a loaded truck. A plant near the route needs 13 pallets moved the same direction. Juno checks the carrier's authority, insurance and ELD connection, confirms that the weight and the space fit, and works out the detour: about six miles and eleven minutes off the route, with the Houston delivery still on time. Dallas to Houston: from 50% to full Before: 13 of 26 positions full After: 26 of 26 +6.2 mi detour +11 min added time On time Houston delivery 33% below market +20% lane revenue A 53-foot dry van from Dallas to Houston with 13 of 26 pallet positions full picks up 13 more pallets for a 6.2 mile, 11 minute detour. The Houston delivery stays on time, the shipper pays 33 percent below market and the carrier earns 20 percent more on the lane. Illustrative example based on a typical Oway Rideshare move. Because the truck was making the trip anyway, the shipper pays well below market and the carrier earns revenue on space that would otherwise have run empty. In our illustrative version of this trip, that comes out to a shipper rate 33% below market and 20% more revenue on the lane for the carrier. Juno prices, checks and books it in seconds, and the truck's load factor goes from 50% to 100%. Guardrails come first A fast price is only useful if it is safe to act on. Juno adds a stop only when the existing delivery windows hold. Every action runs inside the scopes the operation's owner grants. Anything that commits money or changes a plan can require a person's approval, and every decision is logged with what Juno saw and what it did. We would rather lose a quote than cause a missed dock appointment. How we measure it We hold Juno to four measures: Price accuracy. The gap between Juno's price and the real cost of the work. Win rate. How often a price is accepted, by lane and by how long the lane has been active. Latency. The time from request to price. Plan quality. On-time performance and added distance for every inserted stop. Building this taught us that a good price depends most on knowing where the trucks are and how much room they have, minute by minute. For most of trucking history nobody had that information. Then a federal regulation put a connected device in nearly every cab in America. That story is next. You can read more about Juno on its platform page .

## The Half-Empty Truck: Load Factors and Empty Miles | Oway
URL: https://www.oway.io/research/the-half-empty-truck/

The Half-Empty Truck. What America's load factors cost, and how China raised its own with freight matching. By Oway Research, April 27, 2026. US trucks drive one mile in six empty and run loaded at a third to half of capacity. What that costs, and how China cut empty running with freight matching.

In brief: In 2024, 16.7% of US truck miles were driven completely empty. Across the combination-truck fleet, that is roughly 32 billion empty miles and on the order of $70 billion a year in operating cost. Loaded US trucks run at a load factor of roughly 32% to 48% by weight, with an average payload of about 29,000 lbs against rated capacities of 60,000 to 90,000 lbs. China attacked the same problem with freight matching. Empty running for its tractor-trailers fell to about 27%, matched trucks run near 90% full, and logistics costs fell from 18% to 14.4% of GDP.

Stand at the exit of any American distribution center for an hour and count the trucks. A large share of them are hauling air, at highway speed, with a professional driver, at about $2.26 a mile. 1 The measure for this is load factor: the share of a vehicle's capacity that is actually carrying freight. It is one of the most important numbers in the physical economy, and one of the least discussed outside of freight. Two ways to waste a truck A truck can waste capacity in two ways. The first is driving with nothing in it. These are deadhead miles. The American Transportation Research Institute found that 16.7% of all US truck miles in 2024 were driven completely empty , exactly one mile in six. 1 One mile in six: US truck miles driven empty, 2024 Loaded Loaded Loaded Loaded Loaded Empty 83.3% loaded miles 16.7% ~32 billion empty combination-truck miles a year ~$70 billion yearly operating cost of those miles In 2024, 16.7 percent of US truck miles were driven empty, one mile in six. Across 192.5 billion combination-truck miles, that is about 32 billion empty miles costing roughly 70 billion dollars a year. Sources: ATRI (2025); FHWA Highway Statistics 2024, VM-1. Cost is an Oway estimate at $2.26 per mile. Scale that to the country. US combination trucks drove about 192.5 billion miles in 2024. 2 At a 16.7% empty rate, that is roughly 32 billion empty miles. At ATRI's marginal operating cost of $2.26 a mile, those miles cost on the order of $70 billion a year , spent moving nothing. 3 The second kind of waste is quieter and larger: a truck that is loaded but far from full. American Trucking Associations fleet data has shown average Class 8 payloads of about 29,000 pounds against rated capacities of 60,000 to 90,000 pounds . 4 That puts the average loaded American truck at a load factor of roughly 32% to 48% by weight . The average loaded US truck runs a third to half full Rated 60,000 lbs 48% load factor 29,000 lbs 31,000 lbs of air Rated 90,000 lbs 32% load factor 29,000 lbs 61,000 lbs of air The average US Class 8 truck carries about 29,000 pounds. Against rated capacities of 60,000 pounds that is a 48 percent load factor, and against 90,000 pounds it is 32 percent. Source: American Trucking Associations, Trucking Trends. Load factor = average payload / rated capacity. Put together, America runs a fleet that drives a sixth of its miles empty and most of the rest a third to half full. Every one of those miles still pays for diesel, tires, insurance and a driver. Carriers price the waste into their rates, shippers pass it into their prices, and consumers pay it at the shelf. Moving goods through the US economy cost $2.6 trillion in 2024, about 8.7% of GDP. 5 The small-shipment penalty A shipper with six pallets has two standard options, and neither is efficient. LTL picks the freight up, takes it to a terminal, unloads it, sorts it, loads it onto another truck and often handles it again at a second terminal before delivery. Every touch adds time, cost and a chance of damage, and accessorial and reclassification charges tend to arrive after the fact. A full truckload moves the freight directly, at the price of 26 pallet positions to carry six. The shipper pays for 20 positions of empty space. Both options exist because the empty space on trucks already heading the same direction is invisible to the shipper at the moment it matters. China raised its load factors China faced the same problem at a larger scale and with a far more fragmented industry. Its long-haul trucking runs on millions of owner-operators, and for years the commonly cited estimate was that around 40% of truck trips ran empty. A study by China's Ministry of Transport used 45% as its baseline. 6 Chinese heavy trucks averaged about 36,000 km a year against 90,000 km in the US, largely because drivers waited days between loads. 7 China's response was to build a matching layer on the driver's phone, and it worked. Freight-matching platforms let shippers post loads and let nearby drivers claim them. By 2025, China had more than 3,000 digital freight platforms coordinating over 8 million trucks, and the time drivers spent waiting for a load fell from two to three days to eight to ten hours. 8 The largest platform alone fulfilled 197 million orders in 2024. 9 The results show up in the data. GPS records from 2021 put empty running at about 27% for Chinese tractor-trailers , well below the old 40% figure. Trucks matched through these platforms run at load factors approaching 90% . 10 Across the whole economy, China's logistics costs fell from 18% of GDP in 2012 to 14.4% in 2023. 8 China raised its load factors with freight matching Empty running, Chinese trucks Commonly cited, pre-platform ~40% Tractor-trailers, 2021 GPS data 27% Load factor China, platform-matched trucks ~90% US, average loaded truck 32 to 48% By weight, against 60,000 to 90,000 lbs rated 2 to 3 days → 8 to 10 hrs waiting for a load in China 18% → 14.4% China logistics cost share of GDP, 2012 to 2023 8M+ trucks on 3,000+ Chinese freight platforms Chinese tractor-trailer empty running fell from a commonly cited 40 percent to 27 percent in 2021. Platform-matched Chinese trucks run near 90 percent load factor, against 32 to 48 percent for the average loaded US truck. Waiting time fell from 2 to 3 days to 8 to 10 hours, and China logistics costs fell from 18 to 14.4 percent of GDP. Sources: Xu et al., Nature Communications (2026); People’s Daily (2025); American Trucking Associations. Matched-truck load factor comes from a platform sample. China's work is far from finished. Platform matching still covers only about a fifth of its road freight, and its overall empty rate remains higher than America's. 10 But the direction is clear, and so is the lesson: once spare capacity becomes visible and matchable, load factors rise quickly. China had less trucking data than America and built the matching layer anyway. America has something better than a phone app in nearly every truck cab, which is the subject of part four. A third option For most small shipments there is a better fit: put them in the open space of a truck already going the same way. We call it Oway Rideshare. A shipper gives us four things: pickup ZIP, delivery ZIP, pallet count and total weight. Juno finds trucks on the network with open positions on a compatible route, checks that the pickup fits without breaking anyone's delivery window, and prices the move in seconds. The freight is loaded once and rides straight through. Shippers move LTL and partials from 1 to 18 pallets at up to 50% below market, with an average pickup time of 30 minutes on active lanes. Carriers get paid for space they were going to haul anyway and have earned up to 30% more revenue per lane. Every match raises the load factor of a truck that was already on the road. What it taught us Filling empty trucks works. The harder question is why the space stays empty when the truck, the freight and the road are all right there. The answer is information that arrives too late to act on: where the space is, what it is worth and whether a stop fits the schedule. That is a pricing problem and a coordination problem, the subjects of the next notes in this series. American Transportation Research Institute (2025). An Analysis of the Operational Costs of Trucking: 2025 Update. Deadhead miles were 16.7% of all miles in 2024; average marginal operating cost was $2.26 per mile.  ↩ ↩ Federal Highway Administration, Highway Statistics 2024 , Table VM-1. Combination trucks traveled 192,520 million vehicle-miles in 2024.  ↩ Oway estimate: 192.5 billion combination-truck miles × 16.7% empty × $2.26 per mile ≈ $72.7 billion. ATRI's empty-mile share and cost come from its fleet survey and are applied here to the national fleet as an approximation.  ↩ American Trucking Associations, Trucking Trends . Average Class 8 payloads of about 29,000 lbs against rated capacities of 60,000 to 90,000 lbs. Load factor here is payload divided by rated capacity.  ↩ Council of Supply Chain Management Professionals and Kearney (2025). Annual State of Logistics Report.   ↩ Xinhua, reporting research by China's Ministry of Transport with a national freight-matching platform. Lowering the empty-load rate from 45% to 25% could cut CO2 emissions by 69.51 million tonnes a year.  ↩ International Council on Clean Transportation. Barriers to energy efficiency in China's long-haul freight. Heavy trucks average about 36,000 km a year in China against 90,000 km in the US.  ↩ People's Daily (February 2025). "Reduced logistics costs help release China's economic vitality." 3,286 digital freight platforms coordinating over 8 million trucks; waiting times cut from 2 to 3 days to 8 to 10 hours; social logistics costs fell from 18% of GDP in 2012 to 14.4% in 2023.  ↩ ↩ China's largest digital freight-matching platform, fiscal year 2024 results: 197.2 million fulfilled orders and 2.64 million average monthly active shippers.  ↩ Xu, X. et al. (2026). "The potential role of truck-hailing and operational efficiency improvement in China's road freight decarbonization." Nature Communications 17, 4714. 2021 GPS data: 27% empty running for tractor-trailers, 34% to 36% for straight and dump trucks; load factors near 90% in the truck-hailing sample; truck-hailing penetration of about 22%.  ↩ ↩

## Why We Started Oway | Oway Research
URL: https://www.oway.io/research/why-we-started-oway/

Why We Started Oway. Running factories taught me that the hardest machine to build is the one that tells all the other machines what to do. By Phillip Nadjafov, April 6, 2026. Oway's founder on what building a defense vehicle manufacturer taught him about coordination, and why Oway started with the empty space in American trucks.

In brief: Every factory I ran had excellent machines and almost no way for them, or the companies around them, to share what they knew. Coordination ran on phone calls, email and spreadsheets. We started Oway in 2023 because AI could finally read the messy context that coordination depends on, and act on it inside clear limits. We started with trucks because freight is where the physical economy is most wasteful and easiest to measure, down to the pallet.

A factory is a stream of decisions A factory makes decisions all day. Which supplier ships first. Which line runs which order. Whether the part in one building is the part a team three states away has been waiting on since Tuesday. Whether a truck can take one more pallet today, or whether that pallet waits until Thursday. Every one of those decisions depended on information that lived somewhere else. The ERP knew what we ordered. The floor knew what we built. Suppliers knew what they shipped, carriers knew where their trucks were, and none of those systems could see each other. So people carried the information between them, by phone, by email and in spreadsheets that only one person fully understood. We were good at it. We grew because our people coordinated faster than our competitors did. But I always knew what we were paying for that speed: buffer inventory, idle lines, half-full trucks and engineers spending their afternoons chasing status updates. Every operator pays the same tax When I compared notes with other operators, I heard the same story in every industry. Different products, identical blind spots. Every company had spent heavily on machines and software. Almost none had a way for those systems to share what they knew with each other, or with partners, in real time and on their own terms. The cost is large. Moving goods through the US economy cost $2.6 trillion in 2024, about 8.7% of GDP. 1 A meaningful share of that pays for coordination failures: trucks driving empty, trucks driving half full, freight waiting in terminals and inventory parked as insurance against not knowing. For a long time there was a good reason nobody fixed it. Coordination depends on context, and context is messy: an EDI message from a system built in the 1980s, a PDF purchase order, an email that says "can we push to Thursday," a driver running late because a dock in Ohio is backed up. Software could store that context. It could never read it. That changed. AI can now read the messy, real-world context coordination depends on and act on it inside clear limits. We founded Oway in 2023 to work on problems that simply were not solvable before. Why trucks first We needed a first problem where the waste was enormous, the data existed and the result could be counted to the dollar. American freight checked all three boxes. One in every six miles a US truck drives is driven completely empty, and the average loaded truck carries a third to half of what it is rated to haul. 2 A few miles away from that empty space, a shipper is paying premium LTL rates to send six pallets through a chain of terminals where they will be unloaded, sorted and reloaded. The capacity and the demand are often on the same highway, at the same time, going the same direction. They cannot see each other. So we built the thing that lets them. Oway OS lets a shipper move 1 to 18 pallets in space a truck is already carrying, at up to 50% below market. Carriers earn more on routes they already drive. Juno, our industrial AI, prices and books each move in seconds. Today more than 10,000 vehicles are on the Oway network. Where this goes Trucks are the first surface. The same pattern shows up across industry: production lines, warehouses, robots and energy. Capacity that exists, demand that exists, and no shared, trusted way to match the two. That is why we are building IOI, the Internet of Industrials, so the systems the physical economy already runs on, and the autonomous machines arriving now, can share what they know and act on it together, with permission. This series is the story of what we learned along the way: what America's half-empty trucks cost, how China raised its own load factors with freight matching, how a regulation and a pandemic gave America the beginnings of a physical context layer, and what it takes to make the physical economy legible to software and AI. If that sounds like work you want to do, come build it with us . Council of Supply Chain Management Professionals and Kearney (2025). Annual State of Logistics Report.   ↩ American Transportation Research Institute (2025), An Analysis of the Operational Costs of Trucking , for 16.7% empty miles in 2024; American Trucking Associations, Trucking Trends , for average Class 8 payloads of about 29,000 lbs against rated capacities of 60,000 to 90,000 lbs.  ↩

## The New Rules of the Road: Fuel Costs and Freight | Oway
URL: https://www.oway.io/research/the-new-rules/

The New Rules of the Road. How Oway Turns Rising Fuel Costs into a Competitive Edge By Bart A. de Muynck, March 31, 2026. Rising fuel costs make half-empty trucks unsustainable. How consolidating freight into space already moving turns fuel cost into an advantage.

In brief: Record fuel prices make half-empty trucks and fragmented LTL routes unsustainable for carriers and shippers. Filling space already moving cuts shipper costs by up to 50% and raises carrier revenue by up to 30%, with fewer miles and lower emissions. Bart A. de Muynck places Oway at Level 4 (Autonomous) in his Better Supply Chains N.O.W. maturity model.

The Perfect Storm Hitting Transportation The transportation industry is currently facing a perfect storm. As fuel prices climb to historic highs, the traditional model of freight, characterized by half-empty trucks and fragmented routes, is no longer just inefficient. It's unsustainable. For carriers, high fuel costs eat directly into margins. For shippers, those costs are passed down as surcharges that hurt the bottom line. But what if there was a way to bypass these rising costs while actually speeding up delivery? Enter Oway, the next-generation logistics platform designed to optimize every mile driven. A New Ecosystem in Transportation Here is why the current economic climate makes joining the Oway ecosystem a strategic necessity for both carriers and shippers. 1. Slashing Costs Through Maximum Asset Utilization In an era of expensive diesel, "deadhead" miles (empty miles) and LTL (Less-than-Truckload) inefficiency are the enemies of profit. For Carriers: Oway's intelligent matching system ensures that your trucks aren't just moving; they're moving full. By consolidating shipments and optimizing route density, Oway helps carriers maximize revenue per gallon. You stop paying to haul air and start getting paid for every square inch of trailer space. For Shippers: By leveraging Oway's network of shared capacity, shippers can avoid the "fuel surcharge trap." Instead of paying for a whole truck when you only need half, Oway's platform finds the most cost-efficient slot in a vehicle already heading your way. 2. Speed: The Indirect Benefit of Smarter Routing Traditional logistics often trades speed for cost. Oway's platform uses advanced algorithms to prove you can have both. By utilizing a dynamic network of localized carriers and relay-style logistics, Oway minimizes the time goods spend sitting in massive, bloated distribution centers. Because the platform identifies the most direct and efficient path, often utilizing carriers already on that specific corridor, delivery times are slashed. In a world of next-day expectations, Oway gives you a head start without the premium price tag. 3. Sustainability: Lower Emissions as a Standard, Not an Option The most effective way to reduce carbon emissions in logistics is simple: drive fewer miles. The current fuel crisis has aligned economic incentives with environmental ones. Every gallon of fuel saved is a reduction in overhead and a reduction in carbon footprint. Oway's platform is built on the principle of logistics synchronization: by filling existing trucks and optimizing routes to prevent idling and backtracking, Oway helps partners meet their ESG (Environmental, Social, and Governance) goals naturally. Working with Oway doesn't just save money. It makes your supply chain part of the solution to a greener planet. How Oway Aligns with the Better Supply Chains N.O.W. Philosophy The Oway platform aligns closely with the N.O.W. Philosophy, particularly as an example of an "Autonomous Orchestrator" that addresses the inefficiencies of the Great Reconfiguration. By utilizing AI to monetize unused truck space (a $100B+ inefficiency), Oway moves beyond traditional, linear logistics into the realm of real-time, networked execution. N: Networked Intelligence Oway moves operations from siloed to connected by acting as a Cognitive Ecosystem. Autonomous Execution: Instead of manual tasks like calling brokers, Oway uses AI to automatically identify and match available pallet space with shipper needs in real-time. Multi-Tier Connectivity: By integrating directly with carrier networks and telematics, it normalizes data across a broad partner network, ensuring intelligence is based on live capacity rather than historical averages. Decision Velocity: The platform transforms simple visibility into rapid action, allowing shippers to book shipments in seconds, drastically increasing supply chain speed. O: Orchestrated Agility Oway provides the elastic infrastructure necessary to pivot operations in hours, not months. Event Orchestration: The platform moves beyond alert fatigue by triggering automated workflows: when a truck has extra space, the system proactively offers that capacity to a shipper who needs it. Dynamic Optimization: It replaces static routing guides with continuous scenario simulation, adjusting assets instantly to fill unused space in the network. Governed Autonomy: While the AI handles complex matching and pricing, it operates within the guardrails of shipper requirements, ensuring high-speed execution remains controlled and reliable. W: Wide-Angle Visibility Oway treats transparency as a new currency by eradicating the blind spots inherent in traditional LTL shipping. Eradicating Blind Spots: By tracking the exact location and available volume of vehicles in the network, Oway provides visibility that extends from the carrier's current route to the shipper's final mile. Real-Time Intelligence: The platform uses high-fidelity data to provide accurate predictive alerts, moving away from average lead times that lead to stockouts or excess inventory. Multi-Dimensional Analytics: Because the system maps existing routes, it can provide an accurate real-time carbon footprint for every SKU, calculating the emissions saved through optimized ridesharing. Overcoming Legacy Constraints Oway's architecture directly counters the "Not-NOW" trap of legacy logistics thinking: From Deterministic to Probabilistic: Instead of relying on fixed lead times, Oway's AI uses probabilistic reasoning to suggest detours and batching based on current truck movements. From Monolith to Elastic: As an API-first marketplace, Oway represents the modular architecture required to scale without a costly rip-and-replace cycle. From Scribe to Steward: The platform automates documentation (like Bills of Lading) and communication, freeing logistics professionals to focus on strategic trade-offs rather than manual data entry. Strategic Impact for 2026 In an environment where agility is the only currency, Oway provides a documented ROI: Financial Viability: Shippers can see cost reductions of up to 50%, while carriers increase annual revenue by up to 30%. Sustainability: By filling empty trucks, the platform directly improves the carbon footprint of individual SKUs, a key requirement for 2026 ESG targets. Oway fits the Level 4 (Autonomous) status in the N.O.W. Maturity Model because it utilizes Agentic AI for self-correcting workflows and offers holistic value-chain visibility through its rideshare protocol. By adopting Oway, an organization moves from Level 1 (Reactive) firefighting to Level 4 (Autonomous) orchestration. The Time to Act Is Now The old way of shipping relied on finding the best rates, but inadvertently perpetuated systemic inefficiency. Those days are over. For Carriers: Stop leaving money on the table. Join the Oway ecosystem to access a steady stream of high-intent shippers and fill your empty capacity with high-margin freight. For Shippers: Stop overpaying for under-optimized shipping. Complement your transportation services with Oway for a faster, cheaper, and greener way to move your products. In an ever-more complex world of freight, lowering costs while improving quality of service and having a positive impact on the environment isn't just a goal. It's finally within reach.

## Euri Lighting Case Study: 53% Lower Freight, Zero Claims | Oway
URL: https://www.oway.io/research/case-study-euri-lighting/

Euri Lighting.  By Will Honda, February 21, 2026. How Euri Lighting cut freight costs by up to 53%, saved hours of quoting every week and shipped about 200 loads with zero damage claims.

In brief: Euri Lighting, a maker of lighting, EV chargers and water conservation products, was filing one or two damage claims a month with traditional LTL carriers. On Oway Rideshare its fragile freight rides straight through without warehouse handling, and freight costs fell by up to 53%. After about 200 Euri shipments there have been zero insurance claims, and Oway now runs all of its California shipments.

The company Euri Lighting, founded in 2013, makes innovative, high-quality lighting, EV chargers and water conservation products. It ships a high volume of freight every day. The challenge In a competitive industry, a single damaged or late shipment can send a customer to a competitor. Euri's daily volume made shipping complex and costly, with frequent damage claims and slow quoting and scheduling. "Every month we had at least one or two insurance claims with traditional carriers like FedEx, XPO, Custom, and Central Transport due to damaged shipments." Madhara, Euri Lighting Traditional carriers move freight through warehouse hubs, and every extra touch raises the risk of damage to fragile products made of glass and fine components. The solution Euri moved its freight to Oway Rideshare, which uses machine learning to route shipments into unused space on trucks already on the road. Each truck works like a mini-warehouse: freight is loaded once and goes directly from pickup to delivery. The results Lower freight costs. Shipping costs fell by up to 53% compared with Euri's previous contracted LTL carriers, often saving hundreds of dollars per shipment, with no contracts or subscriptions. Hours saved every week. Traditional carriers asked for freight class and dimensions. Oway quotes from four inputs: pickup ZIP, delivery ZIP, number of pallets and total weight. Quotes take seconds, which at Euri's volume adds up to hours each week. Zero claims. Across more than 4,000 Oway shipments, including about 200 for Euri, there have been no insurance claims. "Since switching to Oway, we haven't had a single claim for damaged or missing products." Madhara, Euri Lighting Same-day delivery. When Euri needed urgent same-day deliveries of close to 100 miles for its biggest customers, Oway delivered within a few hours at about 20% of what other carriers quoted. Responsive service. Support answers in minutes, from the same dedicated team every time. Some of Euri's customers now ask for Oway by name. Since partnering with Oway, Euri Lighting has achieved Up to 53% lower freight costs. Hours saved each week on quoting and scheduling. Zero insurance claims across about 200 shipments. Reliable, affordable same-day delivery. Higher customer satisfaction and retention. "Oway now runs 100% of our shipments in California." Madhara, Euri Lighting Ship with Oway Shippers sign up for free, with no contracts. Get an instant quote or see how Oway OS works .

## Vybes Case Study: Up to 63% Lower Freight Costs | Oway
URL: https://www.oway.io/research/case-study-vybes/

Vybes.  By Will Honda, February 21, 2026. How a fast-growing beverage brand cut freight costs by up to 63% across California and Nevada by shipping on trucks already making the trip.

In brief: Vybes, a fast-growing beverage brand, faced high freight costs and unreliable shipping as it grew across California. Moving pallets on Oway Rideshare cut its freight costs by as much as 63%, with freight riding straight through and skipping warehouses. Simple ordering and tracking let the team run shipping in minutes while it built toward national distribution.

The company Vybes is a fast-growing beverage company known for its magnesium-based energy drinks, a healthier option for people looking for an afternoon boost. Its drinks are sold widely in grocery and convenience stores across California. The challenge As Vybes grew, high freight costs and unreliable shipping started to threaten that growth. Getting product to distributors meant paying national carrier rates and absorbing surprise fees for liftgates and freight reclassifications. "One of the biggest cost drivers in my business is actually getting my product to the distributor. Because I'm shipping all over the country, it can be very expensive." Jonathan, CEO, Vybes LA First-mile shippers like distributors and manufacturers carry the heaviest freight costs in a supply chain, and traditional LTL pricing reflects that. The solution Vybes moved its pallets to Oway Rideshare. Oway uses machine learning to find unused space on trucks already driving the same direction, so freight rides straight from pickup to delivery with no warehouse transfers. Oway absorbs the accessorial fees that usually show up after the fact, and prices are up to 50% below the large national carriers. The results Lower freight costs. Vybes saved as much as 63% on shipments. "You guys have kind of come in and found this really great niche for Vybes. When I first started working with you guys, I wasn't sure if that meant the quality would suffer… But overall, it's been just as good as Echo [our traditional carrier], and you're actually saving me money." Jonathan, CEO, Vybes LA Faster, more dependable delivery. Skipping warehouses removed the handoffs where shipments usually slow down. Easy to run. Ordering and tracking fit into the team's day without new work. "Your software is super easy to use. It's very intuitive… And it reminds the customer you're always saving them money, which is really important." Jonathan, CEO, Vybes LA Oway's mid-mile and long-distance lanes across Southern California and Nevada have helped Vybes strengthen its regional position while it builds toward national expansion. Ship with Oway Shippers sign up for free, with no contracts. Get an instant quote or see how Oway OS works .

## RTD Delivery Systems: Carrier Revenue From Empty Space | Oway
URL: https://www.oway.io/research/rtd-delivery-systems/

RTD Delivery Systems.  By Will Honda, February 21, 2026. How a regional carrier filled the empty space on its existing lanes and earned $1,520 in extra revenue from five shipments in its first week on Oway.

In brief: RTD Delivery Systems, a regional carrier since 2005, ran its busiest lanes at 75 to 80% full and some routes half empty. Oway filled that space with pre-priced loads on routes RTD already ran, booked in seconds by email. In its first week RTD earned an extra $1,520 from five shipments, with 95% of stops completed in under 10 minutes.

The carrier RTD Delivery Systems has been a trusted regional carrier since 2005. As the freight market tightened, broker rates stalled, capacity went unused and operating pressure grew. RTD wanted more profit without adding work. The challenge RTD worked with brokers whose rates had not kept pace with the market. Its most popular lanes ran at 75 to 80% capacity, and some routes ran half full. Filling that space meant cold outreach and manual broker coordination, which pulled people away from growing the business. Working with Oway Oway fills unused space on routes a carrier already runs, with no detours and no extra effort. For RTD, that meant loads matched to its existing routes and fully automated booking: click the email, review the load and accept it in seconds. No calls, no negotiation and no surprises at pickup or delivery. The results In its first week, RTD earned an additional $1,520 from five shipments, all in space that would otherwise have driven empty. Loads were secured in seconds. 95% of pickups and drop-offs were completed in under 10 minutes , and every shipment went smoothly. RTD called Oway faster and easier than any platform it had used before. "It's crazy out there. We don't know how some carriers are surviving. Oway helped us fill empty space and earn extra income with virtually no extra effort. Just click and go." RTD Delivery Systems Inc. Conclusion RTD wanted a better way to fill the space it was already driving, without a new dispatch team or a complicated tech stack. Oway delivered profit from underused capacity and automated booking with no back-and-forth . In a market defined by volatility and thin margins, RTD now runs leaner, moves faster and earns more without changing how it operates. Haul with Oway Carriers join free, with no minimums. See Oway for Carriers .

## Reindustrialization | Oway Research
URL: https://www.oway.io/research/tags/reindustrialization/

Oway Research on Reindustrialization: Reindustrialization Needs a Coordination Layer, The Physical Economy Has No Index, Why We Started Oway.

## Physical economy | Oway Research
URL: https://www.oway.io/research/tags/physical-economy/

Essays, case studies and notes from Oway Research on physical economy, including Reindustrialization Needs a Coordination Layer.

## IOI | Oway Research
URL: https://www.oway.io/research/tags/ioi/

Oway Research on IOI: Reindustrialization Needs a Coordination Layer, Juno AI, IOI Search: Ask Your Operation Anything, and more.

## Context layer | Oway Research
URL: https://www.oway.io/research/tags/context-layer/

Oway Research on Context layer: Reindustrialization Needs a Coordination Layer, Inside an IOI Node, Introducing IOI, the Internet of Industrials, and more.

## Juno | Oway Research
URL: https://www.oway.io/research/tags/juno/

Oway Research on Juno: Juno AI, Letting AI Act in the Physical World, Pricing Capacity That Already Exists.

## Industrial AI | Oway Research
URL: https://www.oway.io/research/tags/industrial-ai/

Oway Research on Industrial AI: Juno AI, Letting AI Act in the Physical World, IOI Search: Ask Your Operation Anything, and more.

## Security | Oway Research
URL: https://www.oway.io/research/tags/security/

Oway Research on Security: Letting AI Act in the Physical World, Inside an IOI Node. Essays, case studies and notes on industrial AI and the physical economy.

## Freight | Oway Research
URL: https://www.oway.io/research/tags/freight/

Oway Research on Freight: Coordination Is the Bottleneck, The Accidental Context Layer, The Half-Empty Truck, and more.

## Load factor | Oway Research
URL: https://www.oway.io/research/tags/load-factor/

Oway Research on Load factor: Coordination Is the Bottleneck, Pricing Capacity That Already Exists, The Half-Empty Truck.

## Oway Rideshare | Oway Research
URL: https://www.oway.io/research/tags/oway-rideshare/

Essays, case studies and notes from Oway Research on oway rideshare, including The Half-Empty Truck.

## Case study | Oway Research
URL: https://www.oway.io/research/tags/case-study/

Oway Research on Case study: Euri Lighting, Vybes, RTD Delivery Systems. Essays, case studies and notes on industrial AI and the physical economy.

## Distribution | Oway Research
URL: https://www.oway.io/research/tags/distribution/

Oway Research on Distribution: Euri Lighting, Vybes. Essays, case studies and notes on industrial AI and the physical economy.

## About Oway
URL: https://www.oway.io/company/

About Accelerating the deployment of reindustrialization. Founded in 2023, Oway was created to solve inefficiencies in the physical economy that could not be solved before AI optimization. We’re building the common language of the physical economy, so the companies that make and move things can coordinate through one shared layer. Mission Make the physical economy programmable. The physical economy runs on systems, machines and companies that have no common way to understand or command each other. The waste that creates, from idle capacity to stranded inventory, was too complex to coordinate by hand. AI can finally optimize it, once it can read the operation. IOI is our answer: one protocol that turns physical state into context any company, developer or AI agent can build on. Juno, our industrial AI, acts on that context, and Oway OS is where operations run. Our first solution sells the empty space on trucks already making the trip, since half of US truck space goes unused. It made freight cheaper for shippers and more profitable for carriers, and it is one of many products we’re building to accelerate America’s reindustrialization. Team The people building Oway. A team of engineers, operators and researchers on both coasts. PN Phillip Nadjafov Founder and CEO HR Henry Raschke Head of Engineering JJ John Daily Jr. Head of Product FA Frank Albanese Senior Engineer WH William Honda Chief of Staff + Join the team 11 open roles Founder Phillip Nadjafov. Founder and CEO Phillip Nadjafov Phillip co-founded Isotrex, a multinational OEM hardware defense manufactu

## Careers at Oway
URL: https://www.oway.io/careers/

Open roles: Staff Engineer, IOI Protocol (San Francisco, in person, $220K to $280K + equity); Senior Full-Stack Engineer, IOI Applications (San Francisco, in person, $180K to $230K + equity); Forward Deployed Engineer, Industrial Systems (San Francisco, 30 to 50% travel, $170K to $220K + equity); Quantitative Researcher, IOI Index (San Francisco, in person, $220K to $320K + equity); Strategic Partnerships Lead, Enterprise (San Francisco or Seattle, $180K to $220K base, $350K to $450K OTE + equity); Enterprise Engagement Partner (San Francisco, in person, $160K to $200K base, $250K to $320K OTE + equity); Fleet Network Partnerships Director (US remote, Midwest or Texas preferred, $150K to $190K base, $250K to $350K OTE + equity)

## Staff Engineer, IOI Protocol at Oway
URL: https://www.oway.io/careers/staff-engineer-ioi-protocol/

Staff Engineer, IOI Protocol. You'll be the technical owner of the IOI protocol. You'll define the primitives every industrial system speaks, the node architecture every company runs, the permission model that keeps each node private, and the command layer that lets AI agents act safely in the physical world. Your decisions become the foundation that customers, partners, and eventually outside developers build on. You'll work directly with Phillip and our engineering leads.

## Senior Full-Stack Engineer, IOI Applications at Oway
URL: https://www.oway.io/careers/senior-full-stack-engineer-ioi-applications/

Senior Full-Stack Engineer, IOI Applications. IOI is only as valuable as what runs on it. You'll build those applications end to end: Juno's reasoning and execution workflows, Oway Rideshare, IOI Search over a company's operations, and the consoles operators use to set up nodes, grant permissions, and supervise autonomous work. You'll own features from data model to interface, on top of live physical systems. You'll work closely with Phillip, our engineering leads, and the teams who deploy IOI with customers.

## Forward Deployed Engineer, Industrial Systems at Oway
URL: https://www.oway.io/careers/forward-deployed-engineer-industrial-systems/

Forward Deployed Engineer, Industrial Systems. You'll take an enterprise from kickoff to a live IOI node, then put that node to work. You'll sit with IT, operations, and supply chain teams, map their ERP, warehouse, transportation, and sensor data into IOI's primitives, and build the automations that replace their manual coordination. Then you'll turn what you learned into connectors and playbooks so every next deployment goes faster. You'll work with Phillip, our engineering leads, and our enterprise commercial team.

## Quantitative Researcher, IOI Index at Oway
URL: https://www.oway.io/careers/quantitative-researcher-ioi-index/

Quantitative Researcher, IOI Index. The price of physical capacity is still set by phone calls, stale rate tables, and guesswork. You'll build the index that changes that. When Juno finds open space on a truck already headed from Dallas to Houston, your models decide what that space is worth, how likely a shipper is to book it, and how likely the carrier is to accept, all within seconds. Every quote, win, and loss flows back into the index and makes it sharper. Freight is the first market. Over time the same index will price warehouse space, production capacity, and work for autonomous systems. You'll work directly with Phillip and our engineering leads, and you'll own pricing from research through production.

## Strategic Partnerships Lead, Enterprise at Oway
URL: https://www.oway.io/careers/strategic-partnerships-lead-enterprise/

Strategic Partnerships Lead, Enterprise. You'll own Oway's partner-led enterprise motion. Working alongside our cloud, technology, and systems integrator partners, you'll reach COOs, heads of supply chain, and CIOs at Fortune 500 manufacturers, retailers, and industrial companies. You'll close six-figure annual IOI node deployments that start with one operation and grow across the enterprise. You'll report to Phillip and work closely with our forward deployed engineers and enterprise engagement partners.

## Enterprise Engagement Partner at Oway
URL: https://www.oway.io/careers/enterprise-engagement-partner/

Enterprise Engagement Partner. Oway's enterprise team works in pods, and each pod owns a book of accounts worth $5M to $10M in annual contract value. As an Engagement Partner, you'll lead a pod. You'll diagnose how each customer's physical operations really run, find where IOI and our applications automate decisions and cut cost, design the rollout, and expand from the first application to more of the operation. It's a strategy engagement where you also own the revenue. You'll work directly with Phillip, our forward deployed engineers, and our fleet network team.

## Fleet Network Partnerships Director at Oway
URL: https://www.oway.io/careers/fleet-network-partnerships-director/

Fleet Network Partnerships Director. Fleets are the first physical assets on IOI, and every fleet that joins makes the whole network more capable. When a fleet runs an IOI node, its trucks become discoverable capacity that agents can price, coordinate, and fill. Through Oway Rideshare, fleets earn roughly 30% more from trips they already make, with no extra miles. You'll bring asset-based, private, and dedicated fleets onto IOI, region by region, alongside partners such as Ryder. Enterprise demand is already waiting on the other side, so every fleet agreement you sign opens new lanes for our customers. You'll work directly with Phillip, our enterprise engagement partners, and the engineers who bring each fleet live.

## Contact Oway
URL: https://www.oway.io/contact/

Contact Contact Us Ask about the platform, pricing, security, research, carriers or partnerships, and the right person on our team will reply. Leave empty Full name Work email Company Topic Request a demo IOI for my enterprise Juno and AI agents Moving goods with Oway OS Joining as a carrier or fleet Security Research and benchmarks Partnerships Press Something else Message For emergencies with Juno, IOI or Oway OS, email help@oway.io . This address is for registered users only, and messages from non-registered users will not receive a response. Support: help@oway.io Send Message Message sent. We’ll reply within one business day.

## Terms of Service | Oway
URL: https://www.oway.io/terms/

Legal Terms of Service On this page 1. Agreement and scope 2. Oway’s role 3. Eligibility and accounts 4. Quotes, booking and rate confirmations 5. Payment 6. Carrier obligations 7. Shipper obligations 8. Communications and recorded calls 9. Juno and AI features 10. Cargo claims and liability 11. Acceptable use and security 12. Intellectual property and data 13. Confidentiality 14. Third-party services 15. Disclaimers 16. Limitation of liability 17. Indemnification 18. Term and termination 19. Dispute resolution 20. Governing law 21. Changes 22. Contact Effective October 1, 2024. Last updated October 9, 2026. These Terms of Service (the “Terms”) are an agreement between you and Oway Inc. (“Oway,” “we,” “us”). They govern your use of shipoway.com, the Oway shipper, carrier and driver applications, Oway OS, IOI, Juno, our APIs and MCP server, our phone lines and messaging, and any related services (together, the “Services”). Oway Inc. USDOT 3961202 · MC 1477326 · Property broker authority, BMC-84 surety bond on file 2261 Market Street, STE 86015, San Francisco, CA 94114 6 St Johns Lane, New York, NY 10013 help@oway.io 1. Agreement and scope By creating an account, requesting or accepting a quote, booking or accepting a load, using our APIs, or taking a call or message from Oway, you agree to these Terms on behalf of yourself and the business you represent. If you have a separate signed agreement with Oway, that agreement controls where it conflicts with these Terms, except for Section 8 (Communicat

## Privacy Policy | Oway
URL: https://www.oway.io/privacy/

Legal Privacy Policy On this page 1. Information we collect 2. Calls and voice 3. How we use information, and our legal bases 4. Cookies and similar technologies 5. How we share information 6. International transfers 7. Retention 8. Security 9. Your rights 10. How to exercise your rights 11. California notice at collection 12. AI and automated decisions 13. Children 14. Changes 15. Contact Effective October 1, 2024. Last updated October 9, 2026. This Privacy Policy explains how Oway Inc. (“Oway,” “we,” “us”) collects, uses, shares and protects personal information when you use shipoway.com, the Oway applications, Oway OS, IOI, Juno, our APIs, phone lines and messaging (the “Services”). It also explains your choices and rights, including under the EU and UK General Data Protection Regulation (GDPR) and the California Consumer Privacy Act as amended by the CPRA (CCPA). Controller Oway Inc. · USDOT 3961202 · MC 1477326 2261 Market Street, STE 86015, San Francisco, CA 94114 Privacy requests: Your Privacy Choices or help@oway.io We do not sell your personal information or share it for cross-context behavioral advertising. 1. Information we collect Category Examples Source Identifiers and contact details Name, business email, phone number, company, job title You Account and verification Login credentials, USDOT and MC numbers, insurance certificates, W-9, identity and fraud checks You, FMCSA and verification providers Commercial and payment Quotes, bookings, invoices, settlement and bank details for payouts You, paymen

## Your Privacy Choices | Oway
URL: https://www.oway.io/privacy-choices/

Privacy Your Privacy Choices Control how Oway uses your personal information. Oway does not sell your personal information or share it for cross-context behavioral advertising, and you can make that choice binding here. Do not sell or share my personal information Turns off marketing cookies and records your opt-out of sale, sharing and targeted advertising in this browser. Opt out of sale or sharing Global Privacy Control: Checking your browser. Cookie settings Choose which analytics and marketing cookies Oway can use. Strictly necessary cookies are always on. Manage cookies Make a privacy request Ask to know, access, delete or correct your information, or appeal a decision. We verify every request and respond within 45 days under US state laws and within one month under GDPR. Authorized agents must include written permission. Leave empty Full name Email Request Know or access my information Delete my information Correct my information Opt out of sale, sharing or targeted advertising Limit use of sensitive information Appeal a decision Something else State or country Details I am an authorized agent submitting on someone else’s behalf Or email help@oway.io Submit Request Request received. We’ll verify your identity and respond within the time the law requires. Read the full Privacy Policy . We will not discriminate against you for exercising your rights.