Splice Insights
Technology, Logistics and Supply Chain Perspectives

It’s feasible to create agentic AI for logistics powered by large language models (LLMs). Still, going from concept to a working logistics AI agent is not trivial. It requires a different architecture, and in this article, we break down the components required to build an AI agent for logistics, including what current limitations you need to plan for.

Data integrity is essential for AI in logistics. "Garbage in, garbage out” (GIGO) is more relevant than ever, and logistics data is known to be messy and siloed. That's where an integration platform like Splice has a key role in automating logistics and supply chain processes.

As logistics companies explore artificial intelligence (AI) to increase efficiency and reduce costs, a critical question emerges: What kinds of problems are well-suited to agentic AI? And equally important: Which are not? Agentic AI is most useful when workflows involve unstructured data, unclear instructions, or frequent exceptions. In other cases, use workflow orchestration, a capability inherent in Splice's platform.

As artificial intelligence gains traction in the logistics industry, many supply chain leaders are asking: What is the difference between traditional automation and agent-based AI? More importantly, when should you use one over the other? In this article, we'll explore the distinction between workflow orchestration (deterministic automation) and agentic AI (autonomous, adaptive systems), helping you understand when each approach is best suited for your logistics operation.

Explore what agentic AI actually is, how it differs from traditional workflow automation, and where it fits within the broader landscape of supply chain and logistics technology.

AI in Logistics - Who's wearing rose-colored glasses about? And exporters continue to see challenges managing export receiving dates, and no wonder as 1 in 3 ships are late per Sea Intelligence. We address the systemic issues that go beyond tardy vessels and share a few key reads to keep you on top of resin exports.

There’s a growing belief in logistics that once you’ve built a few APIs, you’re ready for AI. But here’s the truth: AI in logistics doesn’t just need access to data—it needs the right structure, timing, and context. That's where Splice fits in.

On June 17, 2025 at the Agriculture Transportation Coalition (AgTC) Annual Meeting, Kevin Speers had the opportunity to share an update on one of the most persistent challenges in containerized agricultural exports: Changes to Earliest Receiving Dates (ERDs) and Cut-offs.

APIs are emerging as the preferred choice for modern supply chains over EDI. This article explains why.

APIs and webhooks are related, but what’s the difference between APIs and webhooks, and when should you use one over the other?

Interoperability is a central tenent of Splice. When data moves easily between systems, the result is faster processes and lower costs.

Splice Exports now includes rail receiving dates at 170+ intermodal terminals, helping exporters, forwarders, and truckers align with ERD and vessel cut-off times.

Event-driven automation creates a lot of value because it can affect so many aspects of shipping and logistics.

Our 2024 AgTC presentation on receiving dates and the various reasons they are hard to set and track. Spoiler alert: Using Splice will help!

Let’s get into what earliest receiving dates (ERD)and cut-off dates entail and why they matter.

Export visibility data are key to success. Earliest receiving dates, cut-offs, and vessel schedules underpin good management and planning.

This article delves into the intricacies of export demurrage at marine terminals, exploring its causes, costs, and potential solutions.

Drayage is critical to export container logistics. A key factor to success are timely updates to the earliest receiving dates.

February 26, 2024 — Greenabl, the logistics industry's leading partner in achieving global supply chain decarbonization, has joined...