AI · Logistics · Workflow Automation

AutoOps Intelligence

An LLM-powered workflow automation platform built for a global logistics operator. Reduced manual operations by 78%, freed 11,000 hours per quarter, and paid for itself within 6 weeks of go-live.

Client
LogiGlobal Corp
Industry
Logistics / Supply Chain
Engagement
11 Months
Status
Live

11,000 Hours Per Quarter, Lost to Email.

The client operates one of the busiest cold-chain logistics networks in Asia-Pacific. The bottleneck wasn't trucks or warehouses — it was a 12-person ops team drowning in email-PDF-spreadsheet workflows that hadn't been redesigned since 2014.

We built an agentic platform that ingests their inboxes, extracts structured data from messy PDFs, executes deterministic workflows in their TMS, and escalates only the genuine edge cases to humans.

AGENTEMAILPDFTMSSLACKINGEST · REASON · EXECUTE

11 Months, Hard ROI

78%
Manual Ops Reduced
11K
Hours Freed/Quarter
6wk
Payback Period
94.7%
Auto-Resolution Rate
$1.8M
Annualized Savings
99.2%
Extraction Accuracy

A Five-Stage Pipeline

01

Inbox Ingestion

Microsoft Graph + custom parsers monitor 4 shared mailboxes, classifying messages by intent within 200ms.

02

Document Extraction

Vision-language model extracts structured fields from BOLs, invoices, customs forms — 99.2% field accuracy.

03

Reasoning Layer

LLM agents reconcile extracted data against the TMS, flag mismatches, and propose resolution actions.

04

Execution

Deterministic workflows update the TMS, dispatch confirmations, trigger downstream notifications — fully audited.

05

Human Escalation

Only genuine edge cases reach the ops team — with the agent's reasoning trail attached for fast triage.

AI Production Toolchain

Claude / GPT-4
LangGraph
Temporal
Pinecone
Python / FastAPI
PostgreSQL
AWS Bedrock
Langfuse

Drowning in Manual Ops?

If your team is spending more time forwarding emails than actually doing the work — let's talk. Most AutoOps deployments pay back in under 90 days.