Agent in a Box

Autonomous Supply Chain Contract Compliance Agent

operations

Autonomous Supply Chain Contract Compliance Agent

Problem Statement

In global supply chains, the "compliance gap"—the discrepancy between negotiated contract terms and actual vendor performance—costs mid-market enterprises 3% to 9% of their total annual spend. Most companies rely on manual, retrospective audits conducted months after a transaction, making it impossible to recover lost value or correct systemic vendor failures in real-time. This is where an AI-driven supply chain compliance agent becomes essential.

The specific problem lies in the fragmentation of data across Master Service Agreements (MSAs), Purchase Orders (POs), and Bills of Lading (BoL). A typical contract might specify a "Tier 1 shipping rate if delivery occurs within 48 hours, reverting to a 15% penalty discount if delayed." However, Accounts Payable (AP) teams often pay invoices based on the PO price rather than the performance-adjusted price because they lack a mechanism to cross-reference real-time logistics data with legal stipulations. This is closely related to the challenges solved by the Automated B2B Invoice Reconciliation & Dispute Agent.

Furthermore, "hidden" compliance triggers—such as CO2 emission caps, specific packaging requirements, or ethical sourcing certifications—are buried in 50-page PDFs that procurement teams rarely check against daily operations. This leads to legal exposure, overpayment, and a lack of leverage during contract renegotiations because the data on vendor infractions is anecdotal rather than empirical.

What the Agent Does/Doesn't Do

What it does:

  • Contract Intelligence: Extracts specific performance SLAs, pricing tiers, and penalty clauses from legal PDFs using OCR.
  • Real-time Monitoring: Monitors ERP and Logistics data to flag discrepancies between "Contracted" vs. "Actual" performance.
  • Automated Dispute Resolution: Automatically drafts "Notice of Non-Compliance" emails or dispute claims when a breach occurs.
  • Vendor Risk Management: Maintains a live "Vendor Health Score" based on contractual adherence, similar to an Autonomous Vendor Risk Assessment Agent.

What it doesn't do:

  • Legally execute contract terminations (it only flags the right to do so).
  • Negotiate new terms with vendors.
  • Handle physical goods inspection or quality control (requires manual input or IoT sensors).

Workflow

  1. Ingestion & Extraction: Agent monitors a shared folder or CLM (Contract Lifecycle Management) tool. It uses OCR to parse MSAs and SOWs, extracting structured data on lead times, pricing scales, and penalties.
    • Input: PDF Contracts. Output: Structured Compliance Schema (JSON).
  2. Data Synchronization: Agent connects to the ERP (e.g., SAP, NetSuite) and Logistics providers (e.g., Project44) to pull actual delivery dates, quantities, and costs. This ensures data integrity across the Autonomous Last-Mile Delivery Exception Agent workflow.
    • Input: API Webhooks/Polls. Output: Normalized Performance Stream.
  3. Compliance Cross-Check: The agent compares the "Actual" data against the "Contractual" schema using a logic engine to identify deviations (e.g., late delivery, over-billing).
    • Input: Performance Stream + Compliance Schema. Output: Discrepancy Flag.
  4. Automated Reconciliation/Dispute: If a discrepancy exceeds a pre-set threshold, the agent generates a dispute ticket in the ERP and drafts a notification to the vendor with the specific contract clause cited.
    • Input: Discrepancy Flag. Output: Email Draft / ERP Ticket.
  5. Executive Reporting: Agent populates a dashboard showing "Total Value Leakage Prevented" and vendor ranking based on compliance.
    • Input: Aggregated Discrepancy Data. Output: Compliance Analytics Dashboard.

Success Metrics

  • Leakage Recovery: Total dollar value of credits/penalties identified and reclaimed.
  • Audit Efficiency: Reduction in hours spent on manual contract-to-invoice reconciliation.
  • Dispute Accuracy: Percentage of AI-flagged discrepancies that are validated by the procurement team.

Tool Stack

  • Docsumo – Intelligent document processing for extracting SLA terms from MSAs.
  • LlamaIndex – RAG framework for querying contract clauses and performance data.
  • GPT-4o (OpenAI) – LLM for complex extraction and dispute drafting.
  • Merge.dev – Unified API for connecting to ERPs like SAP and NetSuite.
  • Project44 – Real-time logistics visibility for delivery performance data.
  • Make.com – Workflow orchestration for connecting document triggers to ERP actions.
  • Supabase – Database for storing normalized performance streams and discrepancy logs.
  • SendGrid – Automated email delivery for dispute notifications.

Quick Integration

Extracting Contract Data (Docsumo)

import requests
import time

API_KEY = "YOUR_DOCSUMO_API_KEY"
BASE_URL = "https://app.docsumo.com/api/v1/ee/"

def upload_document(file_path):
    url = f"{BASE_URL}identify/"
    headers = {"X-API-KEY": API_KEY}
    files = {'file': open(file_path, 'rb')}
    response = requests.post(url, headers=headers, files=files)
    return response.json()

def get_extracted_data(job_id):
    url = f"{BASE_URL}get_data/"
    headers = {"X-API-KEY": API_KEY}
    params = {"job_id": job_id}
    while True:
        response = requests.get(url, headers=headers, params=params).json()
        if response.get('status') == 'success':
            return response.get('data')
        time.sleep(2)

Source: Docsumo API Docs

Cross-Referencing Performance (LlamaIndex)

from llama_index.core import VectorStoreIndex, Document

contract_data = [
    Document(text="MSA Clause 4.2: Tier 1 shipping rate applies if delivery < 48h. 15% penalty if delayed."),
    Document(text="Bill of Lading #992: Picked up 2023-10-01 08:00. Delivered 2023-10-04 10:00."),
    Document(text="Invoice #992: Charged at full Tier 1 rate of $5,000.")
]

index = VectorStoreIndex.from_documents(contract_data)
query_engine = index.as_query_engine()
response = query_engine.query("Based on the MSA and Bill of Lading, was Invoice #992 charged correctly?")
print(response)

Source: LlamaIndex Concepts

Fetching ERP Data (Merge.dev)

import requests

headers = {
    'Authorization': 'Bearer YOUR_PRODUCTION_API_KEY',
    'X-Account-Token': 'YOUR_LINKED_ACCOUNT_TOKEN',
    'Content-Type': 'application/json'
}

def get_linked_account_data():
    # Example targeting unified accounting/ERP invoices
    response = requests.get('https://api.merge.dev/api/accounting/v1/invoices', headers=headers)
    return response.json()

Source: Merge.dev API Reference

Real-World Examples

Project44 helps global brands like ExxonMobil and Mondelēz achieve real-time visibility into their supply chains, enabling them to track contractual delivery windows and reduce manual tracking efforts by up to 80%. Read case study

Docsumo enabled a logistics provider to automate the extraction of data from thousands of Bills of Lading, reducing manual data entry time by 90% and ensuring that contractual shipping terms were accurately reflected in their billing systems. Read case study

Last Verified: 2026-02-11

Implementation Details

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

✅ Success Checklist

  • Contract PDF is successfully parsed into structured JSON schema
  • ERP/Logistics data is fetched and normalized for comparison
  • Logic engine correctly identifies SLA breaches (e.g., late delivery)
  • Draft dispute emails are generated with specific clause citations
  • Workflow logs all discrepancies to a central database (Supabase/Airtable)
  • API credentials for OpenAI and ERP/Logistics are validated

⚠️ Known Limitations

  • OCR accuracy may vary with low-quality scans or complex multi-column legal tables
  • Real-time logistics data depends on the update frequency of the carrier's API (e.g., Project44)
  • Automated dispute drafting requires human review before sending to maintain vendor relationships
  • Complex 'Force Majeure' or conditional legal logic may require manual override