requirements-doc.md

# OSINT Money Laundering Detection Application
## Requirements and Implementation Plan

## 1. Executive Summary

This document outlines the requirements and implementation plan for an Open Source Intelligence (OSINT) application designed to identify potential money laundering red flags associated with individuals and businesses. The application will leverage CrewAI as the agent orchestration framework, Brave MCP for web searches, and frontier Large Language Models (LLMs) for information analysis and structured output generation. The user interface will be built using Gradio.

## 2. Project Goals

- Create an OSINT tool that gathers comprehensive information about individuals and businesses from publicly available sources
- Identify potential money laundering indicators based on analysis of the gathered information
- Present findings in a structured, actionable format to assist in financial crime investigations
- Provide an intuitive user interface that allows for easy input and clear presentation of results

## 3. Technical Architecture

### 3.1 Core Components

1. **CrewAI Framework**: Orchestrates autonomous agents to perform specialized tasks
2. **Web Search Module**: Utilizes Brave MCP for comprehensive web searches
3. **LLM Analysis Engine**: Leverages frontier LLMs to process and analyze gathered information
4. **Gradio Frontend**: Provides the user interface for interaction with the system

### 3.2 Architecture Diagram

```
┌───────────────────┐          ┌──────────────────────┐
│                   │          │                      │
│  Gradio Frontend  │◄────────►│  CrewAI Controller   │
│                   │          │                      │
└───────────────────┘          └──────────┬───────────┘
                                          │
                                          ▼
                    ┌──────────────────────────────────────┐
                    │                                      │
                    │      Agent Orchestration Layer       │
                    │                                      │
                    └───┬─────────────┬─────────────┬──────┘
                        │             │             │
            ┌───────────▼───┐ ┌───────▼───────┐ ┌───▼────────────┐
            │               │ │               │ │                │
            │ Search Agent  │ │ Analysis Agent│ │ Reporting Agent│
            │ (Brave MCP)   │ │ (LLM)         │ │ (LLM)          │
            │               │ │               │ │                │
            └───────────────┘ └───────────────┘ └────────────────┘
```

## 4. Detailed Requirements

### 4.1 Data Collection Requirements

#### 4.1.1 Target Entities
- Individual profile information
  - Personal identifiers (name, age, location)
  - Professional history
  - Social media presence
  - Public records (property ownership, legal filings)
- Business profile information
  - Corporate structure
  - Ownership information
  - Business registration details
  - Financial disclosures if publicly available
  - Business relationships and partnerships
  - Industry reputation

#### 4.1.2 Search Parameters
- Primary identifiers (full name, business name)
- Secondary identifiers (location, industry, associates)
- Customizable search depth (standard, deep)
- Date range filtering capabilities
- Geographic focus areas

### 4.2 Analysis Requirements

#### 4.2.1 Money Laundering Indicators
The system should detect and flag the following potential indicators:

- **Structural Red Flags**
  - Complex corporate structures with no clear business purpose
  - Companies registered in high-risk jurisdictions
  - Shell companies with minimal operational footprint
  - Frequent changes in business structure or ownership

- **Transactional Red Flags**
  - Inconsistencies between public business activity and apparent resources
  - Involvement with industries known for money laundering risks
  - Connections to entities on sanction lists or watchlists
  - Unusual growth patterns or business expansions

- **Reputational Red Flags**
  - Negative news coverage related to financial crimes
  - Past investigations or regulatory actions
  - Association with politically exposed persons (PEPs)
  - Inconsistencies in public statements and actual business operations

#### 4.2.2 LLM Analysis Capabilities
- Extract and correlate information from diverse sources
- Identify patterns and anomalies in collected data
- Apply AML (Anti-Money Laundering) expertise to evaluate findings
- Generate confidence scores for identified red flags
- Explain reasoning behind flagged items

### 4.3 User Interface Requirements

#### 4.3.1 Input Interface
- Target entity input fields (individual name, business name)
- Search parameter configuration options
- Investigation depth selector
- Search history functionality

#### 4.3.2 Results Display
- Summary dashboard with key findings
- Detailed report section with evidence
- Visualization of entity relationships
- Red flag severity indicators
- Source citations for all information
- Option to export findings in various formats (PDF, CSV, JSON)

#### 4.3.3 User Experience
- Progress indicators during search and analysis
- Responsive design for desktop and tablet use
- Clear navigation between different report sections
- Ability to save and reload previous investigations

## 5. Agent Structure (CrewAI Implementation)

### 5.1 Agent Roles and Responsibilities

#### 5.1.1 Research Agent
- **Objective**: Gather comprehensive information about target entities
- **Tools**: Brave MCP search API
- **Capabilities**:
  - Execute multi-faceted search queries
  - Follow information trails across multiple sources
  - Filter and prioritize relevant information
  - Store and organize gathered data

#### 5.1.2 Analysis Agent
- **Objective**: Process gathered information to identify potential money laundering indicators
- **Tools**: Frontier LLM API
- **Capabilities**:
  - Apply AML expertise to evaluate gathered information
  - Cross-reference findings against known money laundering patterns
  - Identify and categorize potential red flags
  - Assign confidence scores to findings

#### 5.1.3 Reporting Agent
- **Objective**: Create structured, clear reports from analysis findings
- **Tools**: Frontier LLM API
- **Capabilities**:
  - Organize findings in a logical structure
  - Generate concise summaries of complex information
  - Create visualizations of entity relationships
  - Format reports for readability and impact

### 5.2 Agent Communication Workflow

1. User initiates search through Gradio interface
2. Research Agent conducts initial search based on provided parameters
3. Research Agent iteratively refines search based on initial findings
4. Analysis Agent processes gathered information from Research Agent
5. Analysis Agent identifies potential red flags and areas of concern
6. Reporting Agent structures findings into comprehensive report
7. Gradio interface displays final report to user

## 6. Implementation Plan

### 6.1 Phase 1: Core Framework Setup (Weeks 1-2)
- Set up development environment
- Implement basic CrewAI framework configuration
- Create agent templates and communication protocols
- Establish Brave MCP integration for basic searches
- Implement LLM API connections

### 6.2 Phase 2: Agent Development (Weeks 3-5)
- Develop and test Research Agent capabilities
- Implement Analysis Agent with basic AML pattern recognition
- Create Reporting Agent with standard report templates
- Test agent communication and data handoffs

### 6.3 Phase 3: Frontend Development (Weeks 6-7)
- Design and implement Gradio interface
- Create input forms and configuration options
- Develop results display components
- Implement export functionality

### 6.4 Phase 4: Integration and Testing (Weeks 8-9)
- Integrate all components into unified system
- Conduct performance testing
- Optimize search algorithms and analysis pipelines
- Perform security review

### 6.5 Phase 5: Refinement and Launch (Weeks 10-12)
- Conduct user acceptance testing
- Refine UI/UX based on feedback
- Optimize LLM prompts for improved analysis
- Prepare documentation and launch materials

## 7. Technical Requirements

### 7.1 Development Requirements
- Python 3.9+ environment
- CrewAI framework (latest version)
- Brave MCP API access credentials
- Access to frontier LLM APIs (Claude, GPT-4, etc.)
- Gradio UI framework

### 7.2 Deployment Requirements
- Server environment with Python support
- Minimum 8GB RAM, 4 CPU cores recommended
- API key management system
- Secure credential storage
- Rate limiting implementation for API calls

### 7.3 Security Requirements
- Encrypted storage of search results
- Secure API key management
- User authentication for accessing the application
- Audit logging of all searches conducted
- Compliance with relevant data protection regulations

## 8. Evaluation Metrics

### 8.1 Performance Metrics
- Search completion time
- Analysis accuracy (compared to expert review)
- System resource utilization
- API cost efficiency

### 8.2 Quality Metrics
- Red flag detection accuracy
- False positive rate
- Source diversity
- Explanation quality for identified red flags

## 9. Limitations and Ethical Considerations

### 9.1 Technical Limitations
- Reliance on publicly available information only
- API rate limits may affect search depth
- LLM hallucination risks require human verification
- Limited to text-based information analysis

### 9.2 Ethical Guidelines
- System should be used as an investigative aid, not as sole decision basis
- All findings require human verification before action
- Use limited to legitimate AML and financial crime prevention purposes
- Compliance with privacy laws and regulations required
- Application should not be used for harassment or unauthorized surveillance

## 10. Code Structure Overview

### 10.1 Main Components

```python
# Project structure
osint_aml_app/
├── app.py                      # Main application entry point
├── config/                     # Configuration files
│   ├── config.yaml             # General configuration
│   └── agent_configs.yaml      # Agent-specific configurations
├── agents/                     # CrewAI agent implementations
│   ├── research_agent.py       # Web search agent
│   ├── analysis_agent.py       # AML analysis agent
│   └── reporting_agent.py      # Report generation agent
├── tools/                      # Tool implementations
│   ├── brave_search.py         # Brave MCP search integration
│   ├── llm_interface.py        # LLM API interfaces
│   └── data_processor.py       # Data processing utilities
├── ui/                         # Gradio UI components
│   ├── input_forms.py          # Input interfaces
│   ├── results_display.py      # Results visualization
│   └── export_tools.py         # Report export functionality
├── models/                     # Data models
│   ├── entity.py               # Entity representation
│   ├── red_flag.py             # Red flag classification
│   └── report.py               # Report structure
└── utils/                      # Utility functions
    ├── validators.py           # Input validation
    ├── parsers.py              # Content parsing
    └── security.py             # Security utilities
```

## 11. Budget and Resource Requirements

### 11.1 Development Resources
- Developer time: 12 weeks (1-2 developers)
- LLM API costs: Estimated $500-1000 for development and testing
- Brave MCP API costs: Based on search volume (approximately $200-500)
- Infrastructure costs: $100-200/month for development servers

### 11.2 Operational Resources
- Ongoing API costs: Dependent on usage volume
- Maintenance: 10-15 hours per month
- Infrastructure: $200-400/month depending on scale

## 12. Expansion Possibilities

- Integration with financial database APIs
- Addition of document analysis capabilities
- Implementation of temporal analysis (tracking changes over time)
- Development of collaborative investigation features
- Integration with case management systems
- Support for additional languages and jurisdictions

## 13. Success Criteria

The application will be considered successful if it:
- Accurately identifies at least 85% of known money laundering indicators in test cases
- Maintains a false positive rate below 15%
- Completes standard searches in under 5 minutes
- Receives positive usability feedback from AML professionals
- Provides clear, actionable intelligence that enhances investigation capabilities