Handwritten notes on PDFs caused 4-hour queue delays. AI now deciphers the unstructured data to automate 5,500 daily orders.
One of the world's largest logistics providers manages 37 million shipments annually across ocean, air, rail, and truck for 83,000 customers.
The company receives 15,000 daily shipping requests containing inconsistent formatting and handwritten notes on PDFs. Staff spent seven minutes...
C.H. Robinson's Email order processing is part of this use case:
Third-party logistics and supply chain management services provider.
Framework and developer platform for building LLM-powered applications.
Related implementations across industries and use cases
Manual processes bottlenecked 34 ports and 270 vessels. Now, employees deploy AI agents to balance containers and optimize fleet speeds.
Rate limits capped geocoding at 2,000 requests/min. A fine-tuned LLM now clears 8,000/min—cutting costs by 80%.
Ops manually chased drivers via chaotic calls. An AI voice agent now automates outreach, updating a dozen orders in a single interaction.
Highly manual AP processes couldn't keep pace. Now, AI handles invoices from receipt to creation, freeing staff for customer value.
Associates manually routed complex tickets and managed 47K product tags. Now, AI agents triage requests and classify attributes.
Manual checks of varied supplier documents bottlenecked financial close. Now, AI matches contracts, routing discrepancies to specialists.
Rate limits capped geocoding at 2,000 requests/min. A fine-tuned LLM now clears 8,000/min—cutting costs by 80%.
Manual maintenance reports and routine purchases bottlenecked staff. AI agents now accurately classify faults and recommend suppliers.
QA teams manually searched vast incident logs to assess deviations. Now, multi-agent AI synthesizes past cases into verified summaries.
Handwritten notes on PDFs caused 4-hour queue delays. AI now deciphers the unstructured data to automate 5,500 daily orders.
One of the world's largest logistics providers manages 37 million shipments annually across ocean, air, rail, and truck for 83,000 customers.
The company receives 15,000 daily shipping requests containing inconsistent formatting and handwritten notes on PDFs. Staff spent seven minutes...
C.H. Robinson's Email order processing is part of this use case:
Third-party logistics and supply chain management services provider.
Framework and developer platform for building LLM-powered applications.
Related implementations across industries and use cases
Manual processes bottlenecked 34 ports and 270 vessels. Now, employees deploy AI agents to balance containers and optimize fleet speeds.
Rate limits capped geocoding at 2,000 requests/min. A fine-tuned LLM now clears 8,000/min—cutting costs by 80%.
Ops manually chased drivers via chaotic calls. An AI voice agent now automates outreach, updating a dozen orders in a single interaction.
Highly manual AP processes couldn't keep pace. Now, AI handles invoices from receipt to creation, freeing staff for customer value.
Associates manually routed complex tickets and managed 47K product tags. Now, AI agents triage requests and classify attributes.
Manual checks of varied supplier documents bottlenecked financial close. Now, AI matches contracts, routing discrepancies to specialists.
Rate limits capped geocoding at 2,000 requests/min. A fine-tuned LLM now clears 8,000/min—cutting costs by 80%.
Manual maintenance reports and routine purchases bottlenecked staff. AI agents now accurately classify faults and recommend suppliers.
QA teams manually searched vast incident logs to assess deviations. Now, multi-agent AI synthesizes past cases into verified summaries.