Travlbe
A social discovery platform for travelers that connects people based on shared values, interests, and travel styles rather than logistics alone.
Discuss this ventureWhy this problem persists.
The travel industry optimizes for logistics — where to go and how to get there — but ignores the social dimension. Solo travelers and digital nomads have no reliable way to find compatible companions before booking.
What we are building.
Travlbe moves from a transactional booking model to an experiential belonging model. Users discover travel companions aligned with their values and interests, with AI-powered matching and geospatial proximity features.
The intended users.
What the product does.
Values and interest-based traveler matching
AI-powered compatibility scoring
Activity discovery and creation
Real-time messaging between matched travelers
Geospatial proximity alerts
User profile with travel style and preferences
Dashboard with upcoming journeys and recommendations
Review and rating system
JWT-based authentication
Mobile-responsive interface
The structural gap.
Travel platforms optimize for logistics — flights, hotels, itineraries. Social networks optimize for existing connections. Neither is designed to help people find compatible travel companions before they book. The result is that solo travelers and digital nomads either travel alone by default or rely on informal, unreliable methods to find people who share their travel style and values.
Travlbe is in active development following a significant architectural migration to Next.js 16 with the App Router. Core infrastructure — authentication, activity discovery, dashboard, messaging schema, and AI matching data model — is implemented. Real-time messaging, full profile management, and the AI matching engine are in active development. The product is being built toward a pilot release.
Built on a shared foundation.
Travlbe applies AI matching and social discovery patterns that complement the professional identity work in Zinfo. Both products address the same underlying problem — that existing platforms fail to surface meaningful connections — in different contexts. The shared MackJoy infrastructure, including the AI integration layer, authentication patterns, and deployment foundation, accelerates development across both ventures.
We are actively seeking collaborators.
How it is built.
Built with Next.js 16, React 19, TypeScript, and Material UI. Drizzle ORM provides a type-safe interface to a MySQL database. Zustand manages client state and TanStack React Query handles server state. JWT authentication is implemented with the jose library. The AI matching system is schema-defined with matchScore and matchReason fields, with the LLM integration layer in development.