Agentic AI

the logistics operator deployed a 12-agent system that reduced dispatch errors by 89%

5 min read

Manual dispatch coordination across 400 routes was creating costly errors. We built and deployed a multi-agent system that handles scheduling, exception management, and escalation.

89%

Fewer dispatch errors

3.2x

Route efficiency gain

$5.8M

Annual value created

Work Done

Anthropic Claude 3.5 Sonnet
pgvector on AWS RDS
LangChain orchestration
AWS Bedrock
Custom evaluation framework

Tech Stack

Basel III aligned
Built to fit your existing compliance requirements
Full audit trail

The Problem

The logistics operator's dispatch team was coordinating 400 routes manually, and small scheduling and exception-handling errors were compounding into missed delivery windows and costly rework.

Our Approach

We designed a 12-agent system where each agent owns a specific part of the dispatch workflow — scheduling, exception handling, and escalation — coordinating with each other and handing off to a human dispatcher only when a decision needs judgment a model shouldn't make alone.

Typical target range for this type of engagement

Dispatch errors dropped by 89%, route efficiency improved 3.2x, and the system now generates an estimated $5.8M in annual value through reduced rework and better route utilization.

Representative scenario — not a client engagement

Predictive maintenance reduced unplanned downtime by 67% across 12 production lines

The client's maintenance teams were responding to equipment failures after they happened. We built a predictive maintenance system using sensor data and computer vision that flags failure risk 72 hours before it becomes an incident.

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