Rivur Verify

Codename: I-TRAP (Intelligent Talent Risk Assessment Program). Automate collection, monitoring, and analysis across Surface Web, Deep Web, and Dark Web to uncover hidden relationships and risk indicators that traditional methods miss with our bespoke Investigate AI agents.

Intelligence Workflow

End-to-end automation from discovery to actionable intelligence

Pre-Screening & Input

Initial entity identification and data ingestion from multiple sources. System accepts names, emails, corporate identifiers, and social handles.

Automated Collection

Investigate AI agents query structured and unstructured datasets across Surface, Deep, and Dark Web sources using commercial APIs and webhooks.

AI Analysis & Scoring

CRAWL methodology processes data to identify patterns, relationships, and risk indicators. Each entity receives Red-Yellow-Blue risk scoring.

Knowledge Graph Generation

Dynamic Vis.js visualization creates interactive relationship maps with risk-weighted connections and expandable discovery nodes.

Report & Monitor

Export formatted PDF reports with comprehensive risk assessments. Enable continuous monitoring with automated alerts for profile changes.

Ethical AI Governance

Guided by the Artificial Intelligence and Data Act (AIDA) framework

Transparency

Clear documentation of AI decision-making processes and data sources used in risk assessments.

Accountability

Full audit trails and compliance with Canadian legal, ethical, and privacy laws.

Safety & Fairness

Continuous monitoring for bias and adherence to human rights principles in all assessments.

CRAWL Methodology

AI-Powered Intelligence Gathering Framework

C
Communicate
R
Research
A
AI Analysis
W
Write
L
Listen

Communicate

Define the process

Communicate what data sources will be used to conduct the screening.

Research

Gather the data

Gather the data from the information given from the sources defined in the communicate step.

Analysis

Advanced Pattern Recognition & Risk Assessment

Investigate AI agents analyze collected data using machine learning algorithms to identify patterns, relationships, and potential risk indicators. Each entity is assigned a Red-Yellow-Blue risk score based on the analysis.

Write

Comprehensive Report Generation & Visualization

The report is generated based on the analysis and presented in a dynamic Vis.js knowledge graph format, illustrating relationships and risk-weighted connections. The report can be exported as a formatted PDF document for easy sharing and review.

Listen

Continuous Monitoring & Alert Management

Monitor profiles continuously for changes or new risk indicators. Automated alerts notify users of significant updates, ensuring ongoing vigilance and risk management.