Back to case studies
LogisticsAI implementation case study
Logistics – Digital Freight Brokerage: Load-to-Carrier AI Matching
AI for load ingestion, carrier qualification, dynamic pricing, dispatch, and claims documentation automation.
This case study documents a real client implementation. Identifying details may be withheld where confidentiality applies.
Key takeaway
PrimeAxiom automated freight brokerage with AI agents matching carriers, pricing, and claims—end-to-end.
The engagement
From operational friction to a connected AI system
01 · Context
Business overview
Industry: digital freight brokerage. Complexity: volatile spot market.
02 · Challenge
Core problem
Manual matching, compliance gaps, claims drag.
03 · Approach
Solution
PrimeAxiom built a brokerage OS: NLP on loads, carrier graph matching, pricing agents with market feeds, dispatch automation, and claims packet agents.
04 · Impact
Results
Cover time −35%; fall-through rate −22%; claims cycle time −48%; gross margin per load +1.9 pts.
System design
The full automation system
The implementation connects triggers, AI decisions, business rules, human approvals, and downstream actions in one controlled workflow.
Load Agent, Match Agent, Price Agent, Claims Agent.
Workflow
How the automation runs
1. Load posted.
2. AI parses details.
3. Carrier search.
4. Price suggestion.
5. Dispatch.
6. POD capture.
7. Claims if issues.
Intelligence layer
AI agents and decision logic
Parser Agent, Matcher Agent, Risk Agent.
Under the hood
Technical architecture
Graph of carriers, real-time rates, LLM for unstructured load notes.
Build your use case
Want a system like this?
Book a strategy call and we'll map the workflow, tools, integrations, and automation opportunities for your business.