A shipper receiving 25,000 freight invoices monthly from 40+ carriers had the capacity to audit only 5% of invoices. Industry studies suggest 3-5% of freight invoices contain overcharges, meaning the company was losing an estimated $4-6M annually in undetected errors.

Invoice formats varied across carriers, making comparison against contracted rates a manual, error-prone process. Accessorial charges were particularly opaque. When discrepancies were found, filing disputes required 45 minutes per claim, and recovery tracking was done in spreadsheets with 30% of credits never received.

The company needed 100% invoice audit coverage with automated dispute filing and recovery tracking.

The Solution

Agents that extract invoices, validate rates, flag discrepancies, file disputes, and track recovery

Vijan.AI deployed 5 agents. The Invoice Extractor parses carrier bills in any format via OCR and structured data extraction. The Rate Validator checks every line item against contracted rates, accessorial schedules, and fuel surcharge tables. The Discrepancy Flagger identifies overcharges, duplicate invoices, and billing errors. The Dispute agent files claims with carriers including supporting documentation. The Recovery Tracker monitors credit issuance and escalates unresolved disputes.

Autonomous Agents

How each agent reasons, decides, and acts

Step 1 · Invoice Audit

Freight Invoice Auditor

Automated Invoice Validation & Error Detection

Ingests carrier invoices via OCR and EDI, autonomously cross-checking rates, weights, and zones against contracted tariffs to flag discrepancies and duplicate charges.

Input

Carrier invoices, contracted rates, BOLs, shipment metadata

Output

Validated invoices, error flags, overcharge alerts, duplicate detections

Calls OCR engine to extract line items from PDF/image invoices
Calls rate database to verify contracted pricing and fuel surcharges
Autonomous decision: approve, flag for review, or auto-dispute overcharges
Routes flagged invoices to Cost Analyzer and Accessorial Auditor

Step 2 · Cost Analysis

Cost-per-Mile Analyzer

Lane-Level Cost Intelligence

Calculates cost-per-mile by lane and carrier, autonomously identifying pricing anomalies and optimization opportunities using historical data and market benchmarks.

Input

Invoice data, shipment miles, lane volumes, market rate indices

Output

Cost-per-mile reports, anomaly alerts, carrier performance rankings

Calls analytics database to aggregate shipment costs by lane and carrier
Calls benchmark API to compare internal rates vs. market averages
Autonomous decision: flag high-cost lanes for re-bidding or carrier switch
Routes cost insights to Spend Reporter for executive dashboards

Step 3 · Accessorial

Accessorial Charge Auditor

Accessorial Charge Validation

Audits detention, liftgate, and residential delivery fees against carrier tariffs and shipment logs, autonomously disputing invalid charges and recovering overcharges.

Input

Invoice accessorials, tariff rules, shipment notes, driver logs

Output

Validated accessorials, dispute claims, recovered amounts

Calls tariff engine to retrieve carrier-specific accessorial pricing rules
Calls dispute API to file claims for charges unsupported by shipment evidence
Autonomous decision: accept, dispute, or negotiate accessorial fees
Routes validated invoices to Payment agent for final settlement

Step 4 · Payment

Carrier Payment Agent

Reconciliation & Payment Execution

Processes audited invoices through ACH or wire transfer, autonomously holding payments for disputed items and releasing funds only after resolution or approval.

Input

Audited invoices, dispute statuses, payment terms, bank accounts

Output

ACH transfers, payment confirmations, held payment reports

Calls invoice API to retrieve final approved amounts post-audit
Calls payment gateway to execute ACH transfers per carrier payment terms
Autonomous decision: pay immediately, hold pending dispute, or request manual review
Routes payment confirmations to Spend Reporter for financial reconciliation

Step 5 · Reporting

Transportation Spend Reporter

Spend Visibility & Executive Reporting

Aggregates audit results, cost-per-mile trends, and recovered overcharges into executive dashboards, autonomously generating monthly spend reports and savings summaries.

Input

Audit results, cost trends, payment confirmations, dispute recoveries

Output

Executive dashboards, monthly spend reports, savings summaries

Calls BI platform to create visualizations of spend by lane, carrier, and mode
Calls export API to generate PDF/Excel reports for finance and operations teams
Autonomous decision: highlight cost-saving opportunities and high-variance lanes
Routes reports to CFO, logistics leadership, and procurement stakeholders

Results

Measurable impact within 90 days of deployment

$4M

Annual Savings

Recovered $4M in carrier overcharges annually. 4.2% of invoices contained billing errors.

100%

Audit Coverage

Every invoice audited automatically, up from 5% manual sampling. Zero overcharges go undetected.

92%

Recovery Rate

Dispute recovery rate improved from 70% to 92% through automated escalation and documentation.

< 2min

Per Invoice

Average audit time reduced from 45 minutes manual to under 2 minutes automated per invoice.

Implementation

From pilot to production in 12 weeks

Week 1-4

Agent Design & Tool Integration

Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.

Week 5-8

Shadow Mode & Autonomous Tuning

Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.

Week 9-12

Full Autonomous Deployment

Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.

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