Commentary: project44 acquires LunaPath.ai: a key step toward an AI-native supply chain
project44 acquired LunaPath.ai on April 9, 2026 to build AI agents that autonomously handle logistics tasks like carrier calls, delivery confirmations, and exception management. The combined platform processes 4.6 million shipments daily with AI trained specifically for logistics workflows.
This signals the logistics industry moving toward autonomous operations that could reduce fulfillment costs and improve delivery reliability for sellers. Expect similar AI automation to roll out across major fulfillment networks, potentially changing how sellers manage inventory distribution and carrier relationships.
This acquisition represents the broader shift toward autonomous supply chain operations that will eventually reduce logistics costs and improve delivery reliability across all ecommerce platforms.
Monitor your current 3PL and fulfillment partners for AI automation rollouts that could improve delivery speed or reduce costs.
Evaluate logistics partners based on their AI capabilities and data integration, not just current pricing and speed metrics.
Bottom Line
AI-native logistics automation means lower fulfillment costs for sellers.
Source Lens
Industry Context
Useful background context, but lower-priority than direct platform, community, or operator intelligence.
Impact Level
medium
AI-native logistics automation means lower fulfillment costs for sellers.
Key Stat / Trigger
4.6 million shipments processed daily
Focus on the operational implication, not just the headline.
Full Coverage
Jett McCandless is building an AI-native supply chain, both organically and through acquisition. The vision is clear: artificial intelligence that does not just analyze data or send alerts, but manages exceptions, optimizes routing, handles negotiations, and resolves problems without waiting for a human to step in. The acquisition of LunaPath.
ai, announced April 9th, 2026, is a concrete move in that direction. The Chicago-based leader in supply chain decision intelligence bought LunaPath in an all-cash deal. LunaPath builds AI-native agents made specifically for logistics execution and orchestration. These agents do not just handle tasks in isolation.
They coordinate across workflows, connecting systems, carriers, and operational context to drive resolution at scale. The high-volume, repetitive work that still burns hours across phone, email, and daily operations is exactly where they operate. This is not another supply chain tool.
It is about moving from scattered insights to real-time autonomous action grounded in actual operational context. window. googletag = window. googletag || {cmd: []}; googletag. cmd. push(function() {googletag. defineSlot('/21776187881/FW-Responsive-Main_Content-Slot1', [[300, 100], [320, 50], [728, 90], [468, 60]], 'div-gpt-ad-1709668545404-0').
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display('div-gpt-ad-1709668545404-0'); }); From Data Graph to Agentic Execution project44 has spent more than a decade building what they call the world’s largest and most accurate real-time logistics data graph.
It pulls together disconnected systems like ERP, TMS, visibility platforms, yard management, and others into one unified model of how freight actually moves. LunaPath’s agents now connect directly into that graph. They are not guessing. These agents understand what is happening with a shipment, why it matters, and what action makes the most sense.
They manage carrier check calls, pull proof of delivery, start claims, confirm appointments, and fix exceptions the way a seasoned logistics pro would. They prioritize based on real downstream impact. Jett McCandless and his team have been piloting different AI platforms for the past few years. They found LunaPath did the job better than the rest.
The team says LunaPath has a much more efficient model, largely because of how it was trained for logistics workflows. “We are building native AI solutions rather than trying to bolt AI onto legacy technology,” McCandless said. “It rarely works as advertised.” He hears the same thing from clients repeatedly.
There are plenty of AI tools on the market, but most fail to deliver because the AI was never built into the application from the start. project44, he noted, is the database of record for transactions, which makes the whole system smarter with every shipment that moves through it. The company now handles 4. 6 million shipments a day.
That volume makes the model far more robust and turns automation into something that actually works at scale. For global clients in particular, he noted that email is far more reliable than voice. AI still struggles with local languages, dialects, and especially industry jargon. Email avoids those problems and performs much better for consistent execution.
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enableSingleRequest(); googletag. pubads(). collapseEmptyDivs(); googletag. enableServices(); }); googletag. cmd. push(function() {googletag. display('div-gpt-ad-1665767553440-0'); }); This is project44’s second major AI-focused acquisition after ClearMetal in 2021.
Since then, their AI has already improved predictive ETAs, given deeper order-level visibility, and helped spot disruptions before they hit. LunaPath takes it further by turning intelligence into actual real-time execution. A Multi-Vendor Orchestration Strategy That Makes Sense project44 is not putting all its chips on one big AI model.
They are building an orchestration layer that runs on their supply chain data graph. Specialized agents like LunaPath for execution, plus partners such as Vooma and HappyRobot, each do what they do best. Everything stays under one Decision Intelligence Platform with unified control.
Over 16 months, project44 tested eight different AI agent providers in live operations. LunaPath stood out for high-volume voice and messaging work and fit smoothly into the platform. McCandless believes the future of AI in logistics needs technology that is built for the AI age from the ground up, not just an agent slapped on top.
Because project44 had to deve
Original Source
This briefing is based on reporting from Freightwaves. Use the original post for full primary-source context.
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