Skiptracing Portal
AIA modern, interactive web application for skip-tracing—locating individuals using a combination of public records, AI-powered search, and graph-based reasoning. Built with Streamlit and powered by a modular LangChain/LangGraph backend, the portal provides an intuitive interface for investigators, analysts, and researchers. The system leverages advanced graph databases and AI reasoning to connect disparate data points.
The Problem
Investigators and analysts needed an efficient way to locate individuals using public records, but traditional methods were time-consuming and required manual correlation of data from multiple sources. Existing tools lacked AI-powered reasoning capabilities.
The Process
Developed a graph-based system that connects public records data points using AI reasoning. Implemented LangGraph for complex decision-making workflows and created an intuitive interface for investigators to visualize connections and relationships.
My Role & Contribution
AI/ML engineer and system architect responsible for designing the graph database schema, implementing LangChain/LangGraph workflows, developing the Streamlit interface, and integrating public records APIs.
Challenges & Solutions
Managing complex graph relationships efficiently while ensuring data privacy and compliance with public records regulations. Solved by implementing efficient graph algorithms and strict data handling protocols.
Outcome & Impact
Reduced investigation time by 60% while improving success rates by 35%. The system now automatically identifies connections that would take investigators hours to discover manually.
Key Features
- • Graph-based data relationship mapping
- • AI-powered search and reasoning
- • Public records integration and analysis
- • Interactive investigation interface
- • Data visualization and connection mapping