Multi-step workflows
Transport tasks run across several connected steps.
AI for transport operations.
A context-aware AI architecture supporting multi-step logistics and automotive workflows.

What stood in the way.
Transport operations need context-aware assistance across multi-step workflows, which a simple question-and-answer tool cannot provide.
Transport tasks run across several connected steps.
Assistance has to understand where a task stands and what has already happened.
An assistant needs access to operational tools and data to be useful.
How we answered it.
We built an agentic architecture for logistics and automotive workflows with LLM orchestration. LangGraph manages the multi-step flow, MCP connects the agent to tools and data, large language models provide the language capability, and Laravel serves the application.
The parts that made the difference.
An agent works through transport tasks step by step instead of answering single questions.
LangGraph orchestrates how the language model moves through each workflow.
MCP gives the agent a standard way to reach the tools and data it needs.
A Laravel application hosts the agent for transport teams.
What changed for the client.
CLR AI Agent brings AI assistance into transport workflows.
What it was built with.
Part of our AI and machine learning work.
The next big thing starts with a conversation.
An idea, a challenge, a “what if”. Tell us what you have in mind, and we’ll figure out the next step together.
What happens next
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