Enterprise AI-Driven Petroleum Trading System Architecture & Execution Blueprint
System Spec v1.0
Core Business Purpose: Ducagini Petroleum requires an institutional-grade operating system designed to automate the complete petroleum trading lifecycle—integrating KYC/AML compliance, AI-based Buyer-Seller matching, Spread Optimization, Document Automation, Multi-Tier CRM, and Mobile Applications into a single secure platform.
Target Architecture
Microservices
Estimated Timeline
17 Weeks
Core Modules
17 Engines
Security Protocol
AES-256 / TLS 1.3
1. End-to-End Operating Workflow
Client Invite
2FA Login
KYC Upload
OCR Data Extraction
AML/PEP Check
MLRO Approval
Marketplace Onboard
AI Matching Engine
Spread Optimization
Auto Doc Gen
Trade Execution
2. Core Modules Architecture & Scope Breakdown
1. Authentication & Security
Institutional login system with granular role-based access control (RBAC).
End-to-end trade flow testing, security penetration testing, and API sanity checks.
Commercial Note: All development estimations include end-to-end design, software engineering, testing, and cloud deployment. Third-party cloud infrastructure costs (AWS Hosting, OpenAI usage, AWS Textract OCR, SMS 2FA gateways) are variable operational expenses to be billed directly to Ducagini Petroleum.
6. Risk Assessment & Mitigation Strategy
Identified Technical Risks
Poor Quality Scanned Documents: Failure of OCR engines on blurry or handwritten scans.
AML Screening False Positives: Excessive false-positive flags halting legitimate trades.
Third-Party API Gating: Integration delays for external live sanctions or vessel APIs.
Mitigation Protocol
Side-by-Side Review UI: Implement a manual correction view for OCR verification inside the CRM.
MLRO Score Thresholds: Provide configurable match sensitivity scores for compliance officers.
Modular API Gateway: Build with mock interfaces to prevent third-party API delays from stalling backend development.