supply-chain
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We’re Giving Logistics Teams Their Working Day Back
Behind every arriving shipment is someone making the details work. Our customer stories show what happens when experienced logistics teams spend less of their day entering orders.
How Schnucks Put AI to Work Beyond the Cart
At Schnucks, the smartest AI is not a spectacle. It is a practical system that reads the paperwork, checks the apples and gives store teams more time for customers.
Sanmina’s AI Factory Starts With 350 Hours a Week
Sanmina is threading AI through office work, factory systems and the racks that power the cloud. Its CIO’s advice is refreshingly unflashy: educate leaders, secure the foundation and start small.
The Robots Learned to Do the Paperwork
An AI workforce for logistics is a system of AI agents that autonomously execute the repetitive administrative work that runs freight and supply chain operations — order entry, quoting, tracking, billing, customs filings and customer service. Pallet's platform deploys these agents across email, voice, documents, APIs and remote desktops, encoding a company's operating procedures so agents can run workflows from quote to cash. The goal is to automate the roughly $1 trillion in annual coordination work that keeps the physical economy moving, freeing human teams to focus on judgment, relationships and growth.
The Bot That Reads the Room: Why Logistics Is Trading Scripts for Judgment
A plain-English breakdown of the difference between AI agents and traditional robotic process automation (RPA) in logistics. RPA runs rigid, rules-based scripts that break the moment a portal button moves or a document arrives in the wrong format; AI agents reason toward a goal, read unstructured email and PDFs, and handle the exceptions that make up the bulk of real freight work. The piece uses Pallet (pallet.com) and its CoPallet AI workforce as a lead example, and surveys other players in the space including HappyRobot, Vooma, and FleetWorks.
One Tells You the Price. The Other Does the Job.
A plain-spoken comparison that clears up a common category confusion in freight tech: Greenscreens.ai is rate intelligence and dynamic pricing infrastructure, while Pallet is end-to-end workflow automation. The two solve different problems — one tells you what to charge, the other actually does the work — and for many brokerages, Pallet integrates with Greenscreens rather than replacing it. The piece also notes Triumph Financial's 2025 acquisition of Greenscreens as a sign the pricing-data layer of freight tech is maturing and consolidating.
Ryan Petersen on AI, Logistics, and the Future of Flexport
In this episode of Y Combinator's podcast The Light Cone, Flexport founder and CEO Ryan Petersen explains how artificial intelligence is transforming global logistics. Petersen details how AI already saved 2% of Flexport's ocean freight spend while improving transit time 20%, how the company aims to make ocean container shipping 8-10% cheaper, and how AI agents now make phone calls, parse contracts, detect unhappy customers, and automate freight forwarding work. He also reflects on incumbency advantages in AI, his personal 'founder mode' evolution, capital discipline, and a sweeping philosophical vision of what automation means for humanity.
Harish Abbott: Building the AI Teammate Rewiring Global Logistics
On Joe Lonsdale's American Optimist podcast, serial entrepreneur and billionaire Harish Abbott, co-founder and CEO of Augment, explains how his AI teammate 'Auggie' is transforming the fragmented $10 trillion global logistics industry. Abbott, who previously built Deliverr (sold to Shopify for roughly $2.1 billion) and helped build Amazon's fulfillment network, argues that AI's reasoning models and expanding context windows finally make it possible to automate the endless emails, phone calls, and texts that move freight. Auggie already helps manage roughly $25 billion in freight, works 24/7 across email, phone, Telegram and WhatsApp, and follows written workflows like a human employee. Abbott lays out a massive opportunity: not just tens of billions in productivity gains, but a $300 billion U.S. (nearly $1 trillion global) chance to cut waste from an industry hobbled by low-bandwidth, asynchronous communication.
The Freight Back Office Is Up for Grabs — And Everyone Wants Pallet's Lunch
A competitive map of the freight-AI automation field, positioning Pallet against the venture-backed newcomers HappyRobot and Vooma, the entrenched TMS software vendors, and the offshore BPO shops that have long run the logistics back office. Pallet, a San Francisco startup that raised $27M Series B (bringing total funding to $50M), sells CoPallet, an 'AI workforce' that completes end-to-end logistics workflows inside TMS, WMS and ERP systems 10x faster and at roughly half the cost of human staffing. The story surveys how each rival attacks the same back-office spend and where Pallet's end-to-end, system-of-record approach differs.
Inside Pallet: Culture, Ownership, and the Race to Automate the Physical Economy
A behind-the-scenes look at the culture of Pallet, an AI logistics-tech company building the intelligence infrastructure for the global movement of goods. Founder and CEO Sushanth and members of the team describe a fast-moving, ownership-driven workplace where decisions are made in meetings and acted on within the hour, where everyone pitches in across engineering, sales, and post-sales, and where the company aims to become a generational logistics AI company. The piece captures Pallet's values, its people-first ethos, and what it feels like to build software that recreates decades-old logistics workflows.
The $2.5 Trillion Question: Why 95% of Enterprise AI Pilots Fail (and How Supply Chains Beat the Odds)
Sushanth Raman, founder and CEO of San Francisco-based supply chain AI company Pallet, delivers a conference keynote tackling the central paradox of the enterprise AI boom: despite a projected $2.5 trillion in AI spending in 2026, an MIT study finds 95% of enterprise AI pilots fail. Raman argues the failures stem not from weak frontier models but from messy real-world deployments, uncaptured tribal knowledge, legacy integrations, and poor change management. He offers a three-part framework for evaluating AI vendors, explains why building in-house is harder than it looks, and presents three case studies (Lineage, Prism Logistics, and Mallory Alexander) where Pallet drove millions in savings and 99%+ accuracy on tasks like customs filing.
The Agent Factory: How Pallet Forge Turned Six Months Into Six Weeks
On May 14, 2026, logistics AI company Pallet launched Pallet Forge, an 'agent factory' that compresses the build-and-deploy cycle for production-grade logistics AI agents from roughly six months to six weeks. Authored by co-founder and CEO Sushanth Raman, the announcement frames Forge as Pallet's answer to the industry's pilot-to-production gap — citing the MIT finding that only 5% of enterprise GenAI pilots generate measurable P&L impact. Forge works by connecting to a customer's systems (TMS, WMS, ERP, EDI, email, and legacy AS400), encoding operational rules and carrier preferences inferred from historical data instead of hand-written SOPs, and running thousands of simulations to tune agent accuracy automatically. Early proof points include Everest Transportation running on 20,000+ customer-specific encoded memories and Eassons Transport Group hitting 98% touchless processing after going live in 40 days — with subsequent customers onboarded in as little as 48 hours.
Why 95% of AI Agents Fail in Logistics — Pallet CEO Sushanth Raman on TPM Today
On this episode of TPM Today, JOC senior technology editor Eric Johnson interviews Sushanth Raman, founder and CEO of Pallet, an AI-agent company built for the logistics industry. Raman explains why an oft-cited MIT study found that 95% of enterprise AI projects fail — arguing the root cause is missing 'tribal knowledge' and organizational context rather than flashy technology. He details how Pallet captures the undocumented business rules inside freight forwarders, 3PLs and shippers to automate document processing, container tracking, ISFs, billing and air-freight procurement, and lays out how operators should vet AI vendors. The pair also discuss the 'AWSification' of logistics labor, real EBITDA impact, and why, despite the hype, the industry is still very early in AI adoption.