Agentic Document Compliance Pipeline
For global logistics firms, the manual processing and compliance verification of cross-border documentation (Bills of Lading, Commercial Invoices, Packing Lists) is a multi-day bottleneck prone to costly human error. I architected and deployed the Agentic Document Compliance Pipeline, a state-of-the-art, 6-agent orchestration system powered by LangGraph. By fine-tuning LLaMA-3 with QLoRA and routing verifications through a custom Model Context Protocol (MCP) layer, the pipeline cut manual document-processing time by ~92%, raising extraction accuracy to 96% F1 while drastically reducing hallucination and false-approval rates.
Project: Agentic Document Compliance Pipeline
Role: AI Automation Architect / Lead Machine Learning Engineer
Technologies: Python 3.10+, LangGraph, Model Context Protocol (MCP), Hugging Face, PyTorch, FastAPI, QLoRA (LLaMA-3)
Domain: Supply Chain Logistics, Autonomous Agents, LLM Fine-Tuning, Regulatory Compliance
Executive Summary
For global logistics firms, the manual processing and compliance verification of cross-border documentation (Bills of Lading, Commercial Invoices, Packing Lists) is a multi-day bottleneck prone to costly human error. I architected and deployed the Agentic Document Compliance Pipeline, a state-of-the-art, 6-agent orchestration system powered by LangGraph. By fine-tuning LLaMA-3 with QLoRA and routing verifications through a custom Model Context Protocol (MCP) layer, the pipeline cut manual document-processing time by ~92%, raising extraction accuracy to 96% F1 while drastically reducing hallucination and false-approval rates.
The Challenge
Automating customs and supply-chain document processing extends far beyond simple OCR. It requires multi-step reasoning, regulatory knowledge, and rigorous fact-checking.
- Accuracy vs. Hallucinations: Standard LLMs frequently hallucinate regulatory codes (e.g., HS Codes, SCAC).
- Tool Integration: Connecting an LLM to disparate internal APIs (customs registries, hazmat rules, OFAC sanctions) creates brittle, high-maintenance codebases.
- Human-in-the-Loop (HITL): Full automation is reckless in high-stakes logistics; the system must intelligently know when it is unsure and route ambiguous documents to human reviewers without bottlenecking the entire pipeline.
The Solution: A 6-Agent LangGraph Architecture
The system abandons monolithic prompting in favor of a stateful, multi-agent workflow graph. Each agent operates with a strict, narrow mandate, reducing cognitive load on the LLM and enabling deep deterministic routing.
Core Technical Pillars:
- Agentic Specialization (LangGraph):
- Extraction: Utilizes a custom fine-tuned LLaMA-3 model (QLoRA optimized on an A100 GPU) to extract 12+ highly structured entities.
- Verification: A dedicated agent cross-verifies fields against original text passages, requiring exact character offsets. This effectively dropped hallucination rates from 11% to under 2%.
- Model Context Protocol (MCP): Implemented an MCP Tool Routing Layer exposing 9 internal APIs (e.g.,
tariff_lookup,incoterms_rules_engine,sanctions_sanctioned_entity_check). Using standard MCP cut new-integration onboarding time from ~2 weeks to 2 days. - Dynamic Confidence Routing: Before approval, a heuristic agent calculates a multi-dimensional certainty score (Grounding, Compliance, Completeness, Layout). Documents scoring below 0.88 are seamlessly routed to a HITL UI gate.
Key Features & Business Impact
1. Massive Operational Efficiency
By automating the bulk of standard documentation and intelligently queuing edge cases, processing time for a single complex shipment dropped from 3 days to under 4 hours.
2. Enterprise GPU Optimization
To make the system cost-effective, I engineered a dynamic batching and request-queuing scheduler on top of the PyTorch inference layer, reducing GPU idle time by 35% across the cluster.
Empirical Evidence & Outcomes
- 96% F1 Accuracy: Reached production-grade structured data extraction without sacrificing speed.
- Zero-Trust Compliance: The Verification Agent's strict citation requirement ensures that the Compliance Agent never evaluates hallucinated inputs, rendering the system auditable and safe for global customs compliance.
"The Agentic Document Compliance Pipeline showcases the bleeding edge of Enterprise AI. By transitioning from simple RAG to a strict, stateful agent graph combined with standardized MCP tooling, it proves that Generative AI can be safely deployed in high-liability, heavily regulated supply chains."