Context, Approach & Methodology

The logistics operator’s customs department relied heavily on manual document review, with each TIFF file requiring between 3 to 4 hours of processing. The volume made it impossible to catch discrepancies in real time, leading to operational delays and a $4M accounting mismatch. Our goal was to build a resilient, compliant, and fully automated reconciliation pipeline using multi-agent AI.

We began by analyzing file structures, defect types, and variations in handwriting to determine the optimal OCR and cleaning approach. From there, we designed a multi-agent system where each agent independently handled specific responsibilities — image cleaning, artifact detection, OCR extraction, structured data mapping, validation, and WMS submission.

The pipeline allowed agents to interact, validate each other’s outputs, and escalate exceptions through a human-in-the-loop (HITL) interface. This ensured accuracy while maintaining full auditability and compliance.

Once validated, the system integrated directly with the WMS, synchronizing reconciled data automatically and maintaining a transparent digital trail.

The Groundwork

1. Document Preparation & Cleaning

• Removed ink artifacts, pen strokes, and smudges.
• Eliminated blank/duplicate pages.
• Normalized TIFF files into OCR-ready format.
• Applied AI-based layout detection for fragmented documents.
  

2. OCR, Extraction & Structuring

• Multi-pass OCR for handwritten and typed fields.
• Field-level confidence scoring.
• Automated mapping into standardized customs templates.
• Agent-to-agent validation for robustness.
  

3. Reconciliation & WMS Integration

• Auto-comparison against financial and inventory records.
• Detection of mismatches contributing to the $4M discrepancy.
• Automatic submission of reconciled entries into the WMS.
• HITL dashboard for exception handling.

Analysis

Throughout the project, we evaluated extraction accuracy, error patterns, and mismatch categories to refine the pipeline. The multi-agent architecture allowed parallelization and redundancy, ensuring error handling without blocking the entire workflow. This approach revealed that the majority of previous discrepancies stemmed from manual keying errors, missing fields, and unreadable pages — all eliminated in the automated pipeline.

By shifting from manual to AI-based reconciliation, the client gained full visibility into customs flows, leading to significant financial recovery and operational stability.

Results

The multi-agent AI system transformed a previously unmanageable process into an automated, scalable workflow:

• Processing time dropped from 3–4 hours per document to 3–4 minutes.
• $1.5M in outstanding debt was recovered through accurate reconciliation.
• Annual OPEX savings of $200k–$300k were achieved.
• Full automation eliminated manual workload and human error.
• The customs department now operates with real-time visibility, accuracy, and compliance.
  

This engagement demonstrates how AI-ready workflows and multi-agent orchestration can drive operational excellence at scale.