Trust Signal quantifies the connection strength between two people using real data — shared employers, mutual LinkedIn connections, shared schools, and geographic proximity — and returns a 0–100 score with a per-signal breakdown.
- Hackathon filtering — rank attendees by connection strength to you or your team before the event
- VC → portco intros — surface the warmest path from a VC's network to a target company's team
- Internal team intro — find who on your team is closest to a specific target person
- Target-specific intro — given a specific person at a target company, find who can bridge you to them
- User/partner intro — identify warm intro paths through your existing customers or partners
- Help reverse lookup — given a person, surface concrete shared context you can lead with
| Signal | Max Points | Logic |
|---|---|---|
| Mutual LinkedIn connections | 30 | Count of shared 1st-degree connections |
| Shared employer | 30 | Overlapping tenure weighted; same team = bonus |
| Shared school | 20 | Same institution; same graduation era = higher |
| Geographic proximity | 20 | Same city > same region > same country |
Total: 0–100. Each signal returns a score, its weight, and human-readable evidence strings (e.g. "Both worked at Stripe 2019–2021").
| Layer | Choice |
|---|---|
| Framework | Next.js 14 (App Router) |
| Language | TypeScript |
| Styling | Tailwind CSS |
| External API | Crustdata (people enrichment, connections, employment, education) |
git clone https://github.com/virang-nimrobo/context-con.git
cd context-con
cp .env.example .env
# Add your CRUSTDATA_API_KEY to .env
npm install
npm run devOpen http://localhost:3000, enter two LinkedIn URLs, and get a trust score.
CRUSTDATA_API_KEY=your_key_here
Get an API key at crustdata.com.