Problem

Pallet addresses the administrative bottleneck in global freight and supply-chain operations. The company says 67% of supply-chain teams still rely on spreadsheets, while legacy systems and tribal knowledge leave carriers, brokers, shippers, warehouses, and 3PLs coordinating across disconnected tools. The economics are significant: Pallet estimates that $1 trillion of global supply-chain spend goes to administrative back-office work, while its Series B announcement characterizes logistics as an $11 trillion industry with roughly 10% of spend still devoted to manual administrative workflows. These tasks are costly because errors create rework, delayed billing, chargebacks, compliance risk, and slower customer service.

The killer use case is replacing repetitive shipment administration without requiring a company to replace its existing systems. For example, a freight operator can have Pallet read incoming tenders, extract and enrich shipment data, and enter completed loads into its transportation-management system. Pallet says one midsized carrier was able to reallocate 25 employees performing repetitive order entry and save millions, illustrating the labor-arbitrage and capacity benefit at scale.

Product / Service

Pallet sells an AI workforce for logistics, centered on CoPallet, that runs operational workflows on top of a customer’s existing TMS, WMS, email, phone, and third-party portals. It automates work from quote to cash, including document processing, quoting, order and tender entry, dispatch-related coordination, portal updates, shipment-status communication, billing, and customer service. Its agents combine a supply-chain-trained proprietary model with customer-specific operating rules, flexible workflow actions, validation steps, and human escalation; they can navigate portals even when no API or EDI connection exists.

The delivery model is customized deployment rather than a generic out-of-the-box chatbot. Pallet captures each customer’s procedures and edge cases, assembles and tests agents through Forge, and advertises production deployment in roughly six weeks rather than months. Its newer Custom Models are trained from real production workflows and can be dedicated to the customer’s infrastructure; Pallet reports that they reduce inference costs by 70% versus frontier models on the same workflows. The intended benefit is human-level reliability at materially lower cost and higher throughput, allowing logistics staff to shift from data entry and coordination to customer, sales, and exception-focused work.

Market

Pallet competes in vertical applied AI, logistics-operations automation, and AI-native supply-chain software. Its target customers include freight brokers, third-party logistics providers, freight forwarders, FTL and LTL carriers, warehouses, and shippers. The competitive set overlaps with transportation-management, visibility, and workflow-automation platforms rather than consisting only of identical AI-worker products. G2 lists project44, Descartes Aljex, and FarEye as Pallet alternatives, while Crunchbase identifies Reducto, ShipBob, and project44 as possible competitors; these should be viewed as adjacent alternatives and broader logistics platforms, not necessarily proof of direct feature-for-feature competition.

Pallet is not pre-revenue based on the evidence of named production customers and quantified operating results. In its 2026 Pallet Core announcement, the company said more than 70 supply-chain organizations, including Mallory Alexander, Knight-Swift Transportation, Lineage Logistics, STG Logistics, and Prism Logistix, were using Pallet in production. It also announced a $27 million Series B in May 2025, bringing total funding to $50 million. Reported customer outcomes include STG achieving 95% no-intervention order processing and a 5x ROI, Eassons reaching 98% touchless load building, and Prism improving net margin by 10%, although these figures are company- or customer-reported rather than independently audited.