Agent in a Box

Autonomous Freight Audit and Dispute Resolution Agent

operations

Autonomous Freight Audit and Dispute Resolution Agent

Problem Statement

Mid-market logistics firms and high-volume shippers face a systemic "leakage" problem in freight spend, typically losing 3% to 8% of their annual transportation budget to billing inaccuracies. The core issue is the massive discrepancy between quoted rates, contracted tariffs, and final carrier invoices. Freight invoices are notoriously complex, often arriving as unstructured PDFs or EDI feeds containing fragmented data points like fuel surcharges, accessorial fees (lumper, detention, residential delivery), and weight re-classifications.

Currently, finance and operations teams manually cross-reference these invoices against original Rate Confirmations (RateCons) and Master Service Agreements (MSAs). This process is reactive, slow, and prone to human error. Disputes are often missed because the labor cost of investigating a $50 discrepancy exceeds the recovery value. Furthermore, when a dispute is identified, the manual back-and-forth with carrier billing departments via email can take weeks, leading to strained carrier relationships and cash flow bottlenecks. Startups and growing manufacturers lack the headcount to audit every single line item, meaning they effectively pay a "complexity tax" to their carriers. There is no centralized system that autonomously validates the data, identifies the specific contract violation, and initiates a legally/contractually backed dispute without human intervention.

What the Agent Does/Doesn't Do

What it does:

  • Extracts structured data from unstructured carrier invoices (PDF/Image/EDI) using Autonomous Document AI Invoice Processing Agent.
  • Matches invoices against internal Rate Confirmations and contract MSAs.
  • Identifies discrepancies in base rates, fuel surcharges, and accessorial fees.
  • Automatically drafts and sends dispute emails to carrier billing aliases with supporting evidence.
  • Tracks dispute status and flags "unresolved" items for human escalation after 72 hours.

What it doesn't do:

Workflow

  1. Ingestion & Extraction: The agent monitors a dedicated billing inbox or API endpoint to ingest new invoices. It uses OCR to pull Carrier ID, Pro Number, Weight, Class, and Line-item charges.
    • Input: PDF Invoice / EDI 210.
    • Output: Structured JSON of invoice line items.
  2. Contractual Cross-Reference: The agent queries the internal TMS (Transportation Management System) or Database to retrieve the corresponding "Agreed Rate" for that Pro Number or Load ID, ensuring Autonomous Supply Chain Contract Compliance Agent standards are met.
    • Input: Pro Number.
    • Output: Contracted Rate vs. Invoiced Rate comparison table.
  3. Discrepancy Analysis: The agent applies logic to determine if the variance exceeds a pre-defined threshold (e.g., >$5.00). It categorizes the error (e.g., "Duplicate Fuel Surcharge" or "Unauthorized Detention Fee").
    • Input: Comparison table.
    • Output: Boolean (Audit Passed/Failed) + Error Category.
  4. Dispute Generation: For failed audits, the agent generates a professional dispute letter citing the specific contract clause or RateCon ID that was violated, attaching the original agreement as proof. This mirrors the logic used in our Automated B2B Invoice Reconciliation & Dispute Agent.
    • Input: Error Category + Contract PDF.
    • Output: Drafted Dispute Email.
  5. Resolution Tracking: The agent updates the internal ERP/TMS status to "In Dispute" and monitors the thread for carrier "Accepted" or "Rejected" responses.
    • Input: Carrier Email Response.
    • Output: Status Update (Approved for Pay / Escalated to Manager).

Success Metrics

  • Recovery Ratio: Total dollar value of successfully disputed invoices vs. total freight spend.
  • Audit Coverage: Percentage of invoices audited (Target: 100%).
  • Touchless Resolution Rate: Percentage of disputes resolved without human intervention.
  • Processing Time: Average time from invoice receipt to "Ready for Payment" status.

Tool Stack

  • Rossum.ai - High-accuracy document extraction for complex freight invoices.
  • AWS Textract - Managed service for extracting text, forms, and tables from logistics documents.
  • Make.com - Visual orchestration to connect email, OCR, and databases.
  • LangChain (LangSmith) - Framework for LLM orchestration and audit tracing.
    • Pricing: $39/seat per month for Plus Plan; Free Developer Plan available (Pricing) ✓ Verified 2026-01-11
  • GPT-4o (OpenAI) - Advanced LLM for analyzing contract nuances and drafting professional disputes.
  • Supabase - Backend database for storing audit logs and contract rates.
  • Airtable - Low-code database for managing dispute statuses and carrier aliases.
  • SendGrid - API for sending automated dispute emails.
  • Gmail API - Direct integration for monitoring billing inboxes.

Quick Integration

Rossum.ai: Uploading Freight Invoice for Extraction

import requests

# Configuration
API_KEY = 'your_api_key_here'
QUEUE_ID = 'your_queue_id_here'  # The ID of the queue where documents are processed
FILE_PATH = 'freight_invoice.pdf'
BASE_URL = 'https://elis.rossum.ai/api/v1'

headers = {
    'Authorization': f'Token {API_KEY}'
}

# 1. Upload a document (e.g., a Freight Invoice) to a specific queue
with open(FILE_PATH, 'rb') as f:
    files = {'content': (FILE_PATH, f, 'application/pdf')}
    response = requests.post(
        f'{BASE_URL}/queues/{QUEUE_ID}/upload',
        headers=headers,
        files=files
    )

if response.status_code == 201:
    document_data = response.json()
    print(f"Successfully uploaded! Document ID: {document_data['document']['id']}")
else:
    print(f"Upload failed: {response.status_code}")

Source: Rossum API Docs

AWS Textract: Analyzing Invoice Structure

import boto3

textract = boto3.client('textract', region_name='us-east-1')

def analyze_freight_invoice(file_path):
    with open(file_path, 'rb') as document:
        image_bytes = document.read()

    response = textract.analyze_document(
        Document={'Bytes': image_bytes},
        FeatureTypes=['FORMS', 'TABLES']
    )
    return response

Source: AWS Textract Docs

Real-World Examples

Trax achieved a 70% reduction in exception handling time and autonomous resolution of over 1 million exceptions per quarter using AI-powered decision making. Read case study

Sphere Partners reported an 800% ROI from automation vs manual auditing, allowing 100% of invoices to be audited without growing headcount. Read case study

Last Verified: 2026-02-16

Implementation Details

⏱️ Deploy Time: 30–45 minutes (n8n, intermediate)

✅ Success Checklist

  • Invoice PDF data is correctly extracted into JSON format via AWS Textract or GPT-4o Vision
  • The agent successfully matches the 'Pro Number' from the invoice to a record in the TMS/Airtable
  • Discrepancy logic triggers for variances exceeding the defined threshold (e.g., >$5.00)
  • Dispute emails are drafted with specific line-item evidence and sent to the carrier alias
  • Audit status is updated in the database (Approved vs. Disputed)
  • Error handling catches missing Rate Confirmations and flags them for manual review

⚠️ Known Limitations

  • Handwritten notes or extremely poor quality scans on BOLs may require human OCR verification
  • Complex fuel surcharge formulas that vary weekly require a live API feed to validate with 100% accuracy
  • The agent cannot resolve disputes; it only initiates them and tracks the 72-hour follow-up window