AI-Powered Invoice Processing Platform
A multi-agent orchestration system deploys 7 specialized AI agents that collaboratively process invoices end-to-end. The platform automates document extraction with GPT-4 Vision, validates against business rules, performs 3-way PO matching, resolves exceptions using historical patterns, routes approvals intelligently, and continuously learns to improve accuracy.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Input & Trigger Dashboard - System status with 7 AI agents, sample invoice selection, and agent fleet overview for initiating processing
Agent Orchestration Network - Multi-agent coordination view showing all 7 specialized agents with real-time processing pipeline status
Processing Pipeline & Reasoning - Chain of Thought visualization with agent reasoning traces, processing metrics, and confidence scores
Output & Results - Extracted invoice data with field-level confidence scores, processing summary, and approval status
AI Agents
Specialized autonomous agents working in coordination
Workflow Coordinator
Complex invoice processing requires coordinating multiple specialized tasks, managing priorities based on amount and due date, and ensuring seamless handoffs between processing stages without bottlenecks or lost context.
Core Logic
The Orchestrator Agent (GPT-4 Turbo) serves as the central coordinator, analyzing incoming invoices, querying vector stores for similar historical documents, assembling the appropriate agent team based on complexity, distributing tasks with priority assignments, and broadcasting communications across all agents. It maintains workflow state, tracks historical patterns, and ensures quality gates are met before advancing to each stage.
Document Intelligence Agent
Invoice documents arrive in varied formats—PDFs, scans, emails—with inconsistent layouts, handwritten annotations, and poor image quality, making accurate data extraction challenging for traditional OCR systems.
Core Logic
DocuVision (GPT-4 Vision) performs multi-modal document analysis with advanced OCR achieving high clarity scores. It detects document layout, extracts structured fields (vendor, amount, line items, dates), processes tables with line-item recognition, identifies signatures and annotations, and generates per-field confidence scores. Handwritten content is flagged for human review.
Validation Engine Agent
Extracted invoice data may contain errors, duplicates, fraudulent patterns, or compliance violations that require sophisticated rule-based and AI-powered validation before processing can continue.
Core Logic
ValidatorX (Claude 3 Opus) executes comprehensive validation checks including schema validation, business rule enforcement, duplicate detection, fraud scoring, SOX/GDPR compliance verification, vendor approval status, and tax calculation validation. It generates detailed reasoning traces for each check and routes exceptions to resolution agents.
PO Matching Agent
Three-way matching between invoices, purchase orders, and goods receipts requires complex ERP queries, line-by-line comparison, and variance analysis with configurable tolerances to prevent overpayment.
Core Logic
MatchMaster uses a Custom Matching Engine v2 to query ERP systems (SAP/Oracle), retrieve purchase orders and goods receipts, execute line-by-line matching algorithms, calculate price variances within configurable tolerance, detect quantity discrepancies, and categorize variances by financial impact. Matching results include detailed variance analysis with root cause identification.
Exception Resolver Agent
Invoice exceptions (price variances, missing POs, duplicates, compliance issues) require intelligent analysis to determine root causes and generate appropriate resolution recommendations without manual investigation.
Core Logic
ResolutionAI (GPT-4 Turbo) performs root cause analysis on multiple exception types, matches against historical cases in the vector store, analyzes vendor relationship patterns, calculates financial impact, and generates AI-driven recommendations with confidence scores. It provides approve/reject/escalate decisions with detailed rationale.
Approval Routing Agent
Invoice approvals require dynamic routing based on amount thresholds, exception types, and approver availability, with SLA tracking and escalation to prevent payment delays.
Core Logic
ApprovalRouter (Rules Engine v3) implements a configurable multi-level approval matrix based on amount thresholds. It checks approver availability, sets SLA deadlines, determines auto-approval eligibility for simple invoices with high confidence, enriches approval context with exception summaries, and manages escalation workflows for overdue requests.
Learning Engine Agent
Invoice processing systems typically degrade over time without feedback loops, failing to adapt to new vendor formats, changing business rules, or evolving fraud patterns.
Core Logic
LearnEngine (Custom ML v1) implements continuous improvement through pattern recognition and storage, extraction model weight adjustment, vendor-specific format learning, processing efficiency analysis, and knowledge base enrichment. It tracks extraction accuracy improvements, analyzes process bottlenecks, and generates optimization recommendations for the orchestrator.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization