Pallet raises $27M Series B to automate logistics back-office with AI agents amid tariff-driven supply chain volatility

Summary

Pallet, a logistics back-office automation platform, has raised a $27 million Series B led by General Catalyst — just seven months after closing a $21 million Series A. Founder and CEO Sushant says most of the Series A capital was still unspent, making the new round entirely opportunistic. General Catalyst's appeal was specific: the firm helped build Samsara, widely regarded as the defining logistics software company of the last decade.

What Pallet does

The $11 trillion logistics industry spends roughly $1 trillion on back-office tasks — data entry, quoting, appointment scheduling, container tracking. Pallet deploys AI agents to automate those workflows, but keeps humans in the loop to QA outputs, which is how it pitches near-guaranteed accuracy. Customers include major cold-chain storage companies, large shippers, and freight forwarders. The entry point varies by client — some start with data entry, others with spot-market quoting or appointment scheduling — but the technology is built to generalize across use cases.

The onboarding pitch is deliberately low-friction: Pallet ingests a customer's standard operating procedure, or even a screen recording of their workflow, and spins up a matching agent without requiring any changes to the customer's existing tech stack.

Tariffs as a tailwind

Tariff volatility has sharpened Pallet's value proposition in real time. Container volumes at Long Beach spiked in April, then dropped, then reversed again as court rulings came and went. Logistics operators can't hire and fire contractors fast enough to absorb that kind of swings. Pallet's agents scale with inbox volume instead, which Sushant frames as the clearest argument for outcome-based pricing — customers pay per successful task completion, not a flat seat license.

On pricing, Sushant is direct: if a task costs a dollar in-house, Pallet charges $1.30, and the pitch is guaranteed ROI with immediate time-to-value. He acknowledges his former boss David at Retool thinks outcome-based pricing is "BS" — Sushant disagrees and sees it as a structural advantage, particularly when selling into volatile operating environments.

Go-to-market

About 30% of Pallet's staff came from logistics companies — C.H. Robinson, Uber Freight, Flexport — and the sales team is coached to demonstrate industry fluency before mentioning the technology. Sushant's early customer acquisition involved driving around Stockton and East Bay knocking on warehouse doors. Cold calling, conferences, and inbound now run in parallel.

On large enterprise deals, management consulting firms are functioning as referral partners rather than competitors. CIOs and CEOs at companies like DHL, Kuehne+Nagel, and Estée Lauder are confused about which AI model to use for which problem, and consulting firms are helping them filter to the right vendor. Sushant says a CEO at one of those firms has personally opened doors for Pallet.

Forward-deployed engineering

Pallet requires engineers to spend one week per month at a customer site — not optionally, but as policy. Last month the entire team was at a customer's back office learning their operations. Sushant built the forward-deployed engineering function at Retool before founding Pallet, and argues it is essential for any product that has to hook into complex internal systems and drive change management without disrupting existing workflows. As software costs trend toward zero, he says deep workflow knowledge is what separates useful products from ones that get ignored.

The new capital goes toward accelerating enterprise sales and product velocity. Sushant believes the industry is at an inflection point — large logistics operators know they need AI but don't know how to deploy it, and Pallet is positioning itself as the translation layer between frontier models and operational workflows.