Sushanth Raman announced the launch of Custom Models for supply chain teams.

More AI spend won’t fix your supply chain

Last week, we launched Custom Models to enable every supply chain team to unlock the highest ROI with their AI in the industry, from quoting and tendering loads, to sourcing the right carrier, to optimizing inventory allocation in seconds.

Enterprise AI has entered its sovereignty era. Leading enterprises are realizing that the real cost of AI isn’t just token spend, but also the proprietary knowledge they pay by contributing every time they use AI. The more useful you want your AI to be, the more of your business you have to expose to the frontier labs like Anthropic and OpenAI. At the same time, open-source models like the recently released Kimi K3 are closing the gap with the frontier.

In response, they’re demanding sovereignty: control over their compute, their models, their data, and ultimately their edge. They want AI to be an asset they own and govern, yet the domain where proprietary knowledge matters most has largely been left out of this conversation.

That industry is supply chain.

Unlike industries protected by patents, brands, or physical infrastructure, supply chain advantage lives almost entirely in information. Frontier models are trained on the public corpus of human knowledge, but your operation isn’t part of that corpus. Your carrier and supplier relationships, routing logic, and customer nuances were never published, which means a model that “knows everything” still knows nothing about your business.

Making frontier models useful in supply chains creates two problems.

The first is cost. Real workflows require multiple model calls per transaction. Multiply that across millions of shipments, invoices, orders, and exceptions, and token spend quickly becomes a variable tax on running your business, especially as volume and context grow. Token usage becomes a direct function of how complex your operation is, and this cost is not clearly tied to value. You pay for every token processed, not outcomes achieved, while pricing power sits with the provider.

The second is control. Every time a frontier model learns your business, you’re giving away some of the intelligence that makes your operation valuable. Providers are incentivized to absorb and generalize this intelligence, turning your proprietary knowledge into baseline capability others can access. That’s a bad trade for any enterprise, especially in supply chain, where that knowledge is the core moat. Even if providers promise not to train on your data, you still don’t own the system running your operation, leaving you exposed to pricing changes, outages, policy shifts, and geopolitical constraints.

Pallet trains a dedicated Custom Model for every supply chain team that learns exclusively from its operations and encodes that knowledge as a persistent memory optimized for its outcomes. This intelligence compounds inside your business instead of being diluted across a vendor’s customer base, so the model directly reflects how you run freight, inventory, and service. The result is AI with the highest ROI: higher accuracy from a system that reflects your reality, lower cost by eliminating repeated context loading, and true ownership of your entire AI stack.

Agents run your workflows, memory learns the context, and custom models trained exclusively on your operations retain it.

Agents run your workflows, memory learns the context, and custom models trained exclusively on your operations retain it.

None of this means rejecting frontier models. They’re essential for discovery, when problems are undefined, and flexibility matters more than efficiency. However, supply chain is past discovery. Core workflows like quoting, shipment entry, tracking, and auditing are well understood, high-volume, and operationally constrained. At this stage, general intelligence becomes overhead. What matters is precision, consistency, and cost.

For decades, proprietary knowledge has been fragile and transient. For the first time, it can be captured, compounded, and embedded into the business itself, but only if the enterprise owns the resulting intelligence.

In the AI era, a company’s intelligence is its enterprise value. The future of supply chain companies is many models, each learning from a single operation and owned by the business it serves.