Chat interface over your LinkedIn graph: ask "Who in my network works on climate AI?" and get context-aware answers.
Problem
Professional networks are large and difficult to navigate. Finding the right person in your network for advice, opportunities, or introductions is time-consuming and inefficient. As networks grow, it becomes impossible to remember who works where, what their expertise is, or how they might be connected to opportunities. Manual searching through hundreds or thousands of contacts is impractical.
Overview
This project builds an AI-powered assistant that understands your personal professional network and answers questions like "Who do I know working in climate AI?" or "Which of my contacts is connected to robotics research?" The system creates a searchable, intelligent representation of your professional connections, making it easy to leverage your network when you need it most.
How It Works (Approach)
Using exported LinkedIn data, the system constructs a graph of connections where nodes represent people and edges represent relationships. Profile information (job titles, skills, companies, education) is embedded using semantic models. An LLM interprets natural language queries, understands the intent, and traverses the graph to surface the most relevant contacts. The system considers both direct connections and second-degree relationships, ranking results by relevance and connection strength.
Impact / Value
Professionals gain faster, smarter access to their own network, improving opportunity discovery and relationship-building. The tool helps users identify warm introductions, find expertise within their network, and maintain relationships by surfacing relevant connections when needed. By making networks more navigable, the system helps professionals unlock the full value of their relationships.
Key Features
- Graph-based representation of your network
- Natural language contact search
- Semantic profile matching
- Privacy-safe analysis using exported data
- AI-assisted professional navigation