An AI agent that automates the auditing and verification of Bills of Lading (BOLs). It intelligently extracts data from various BOL formats, validates it against purchase orders, sales orders, carrier agreements, and actual shipment details, identifies discrepancies, ensures compliance with shipping terms and regulatory requirements, and flags issues for resolution, thereby supporting accurate freight payment and claims processing.
Bills of Lading (PDF, EDI, XML; or scanned images). Purchase Orders (for inbound) and Sales Orders/Shipment Orders (for outbound). Carrier Booking Confirmations / Rate Confirmations / Service Level Agreements. Packing Lists and Commercial Invoices (for cross-validation).
Bills of Lading (PDF, EDI, XML; or scanned images). Purchase Orders (for inbound) and Sales Orders/Shipment Orders (for outbound). Carrier Booking Confirmations / Rate Confirmations / Service Level Agreements. Packing Lists and Commercial Invoices (for cross-validation).
ERP Systems (e.g., SAP S/4HANA, Oracle NetSuite, Microsoft Dynamics 365) Epicor Truckmate
High potential for costly errors from manual data handling.
Significant time and resources are spent on repetitive, low-value work.
The manual process is difficult to scale without increasing headcount.
Process bottlenecks lead to delays and missed deadlines.
a) ingests BOL documents received from carriers, freight forwarders, or internal shipping departments. b) extract key fields from diverse BOL formats. Extracted fields include: Shipper, Consignee, and Carrier names and addresses. PRO number, BOL number, Tracking numbers. Pickup and actual/estimated delivery dates. Freight description (items, SKUs, quantity, weight, dimensions, pallet/piece count). NMFC (National Motor Freight Classification) or HS (Harmonized System) codes, if present. Freight terms (e.g., Incoterms like FOB, CIF; payment terms like Prepaid, Collect). Special handling instructions (e.g., temperature control, fragile). Declared value for carriage. Signatures and endorsements (presence/absence).
a) Validates that freight terms and payment terms (e.g., prepaid, collect) on the BOL align with the agreed terms in underlying sales/purchase contracts or as recorded in the ERP. b) Checks for consistency of weights, dimensions, and piece counts across all related documents. c) Identifies if any unexpected accessorial charges are indicated on the BOL that might later appear on the freight invoice.
a) For hazardous materials (Hazmat) shipments, the AI agent verifies that proper Hazmat declarations, UN numbers, and endorsements are present on the BOL. b) Ensures adherence to specific customer shipping instructions or internal routing guide policies.
identifies and flags a wide range of discrepancies, such as: Mismatches in quantities, weights, SKUs, or product descriptions between BOL and PO/SO. Incorrect or conflicting freight terms or Incoterms. Unauthorized carrier or service level usage. Missing or improper Hazmat declarations. Absence of required signatures, dates, or endorsements. Discrepancies between BOL declared value and commercial invoice value. Indications of potential damage or shortages noted on the BOL by the consignee.
Dramatically reduce the time and manual effort required to complete the process.
Eliminate human error to ensure data integrity and reduce financial risk.
Free your team from monotonous tasks, allowing them to focus on strategic work that requires their expertise.
Handle growing volumes of work without a proportional increase in operational costs.
Maintain a complete, auditable trail of every action the AI agent takes, described in plain English.
This use case solution follows these general steps at a high level: