Enterprise-Grade Knowledge Extraction & 9-Way Transaction Reconciliation

ECL is a sophisticated Mixture-of-Experts (MoE) extraction pipeline designed to transform massive volumes of unstructured enterprise data into high-fidelity, actionable knowledge graphs. By integrating multi-modal semantic extraction with a rigorous 9-way reconciliation engine, ECL identifies financial leakage, operational risks, and hidden revenue opportunities at scale.

ECL addresses the "dark data" problem—the 80% of enterprise information trapped in PDFs and documents that traditional ETL processes ignore. It provides AI agents with a structured, traceable, and private memory layer (FalkorDB), enabling complex reasoning with $0 cloud LLM overhead.

The ECL framework orchestrates a seamless flow from raw data to business intelligence:

graph TD
    raw["Unstructured Documents"] --> moe["MoE Expert Pipeline"]
    moe --> vald["Validation / Hallucination Guard"]
    vald --> llm["Ollama / Local LLM"]
    llm --> db[("FalkorDB Knowledge Graph")]
    
    data["8 Data Sources"] --> recon["Reconciliation Engine"]
    recon --> dash["Impact Dashboard ($47.3M ROI)"]
    
    db --> mcp["MCP Tool Orchestration"]
    mcp --> agents["AI Reasoning Agents"]
    dash --> agents

The crown jewel of the ECL framework is its capability to reconcile 8 disparate data sources across 9 critical business dimensions.

Quantified Performance:
Based on simulated portfolio optimization: 2,810 Discrepancies Handled | $47.3M Estimated Annual Savings

.
├── core/                     # Core Extraction Engine
│   ├── ecl_poc.py            # MoE Expert Orchestrator
│   ├── ecl_llm.py            # Local LLM (Ollama) Integration
│   ├── ecl_falkordb.py       # Graph Persistence Layer
│   └── ecl_tracing.py        # Audit Trail & Pipeline Tracing
├── reconciliation/           # Reconciliation & Simulation Suite
│   ├── reconcile_contracts.py # 9-Way Cross-Reference Engine
│   ├── generate_contracts.py  # synthetic Lease Generator (1,250 docs)
│   └── generate_erp_invoices.py # Financial Data Generator
├── platform/                 # User Interface & API
│   ├── ecl_app.py            # ECL Studio (Streamlit Dashboard)
│   ├── ecl_server.py         # Application Backend
│   └── ecl_connectors.py     # SharePoint & Dynamics 365 Integration
├── telecom_reit/             # Domain-Specific REIT Pipeline
├── assets/                   # Presentation & Documentation
└── tests/                    # Robustness Verification (44/44 Passing)

Ensure you have Python 3.10+ and the following services active:

• Ollama: Running locally with llama3:8b
• FalkorDB: Active on localhost:6379

# Install dependencies
pip install -r requirements.txt

# Execute base extraction pipeline
python ecl_poc.py

# Launch the visual intelligence suite
streamlit run ecl_app.py

# Generate synthetic datasets
python generate_erp_invoices.py
python generate_tower_ops_data.py

# Run the reconciliation engine
python reconcile_contracts.py

• 100% Data Residency: No data leaves your infrastructure; all inference is local.
• Explainable AI: Every graph node includes a source-text "trace" for manual auditing.
• Deterministic Validation: Hallucination guards ensure that extracted entities exist in the source document.
• Horizontal Scalability: Distributed MoE experts allow for parallel document processing.

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