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CoLab Week Three // Submission // Spec Sheet V1

Photo from Sequoia, 2023

For week three of the CoLab PM Bootcamp, I took the initial concept and qualitative research for Sojourn and developed it into a spec sheet. Below is a draft: peer-reviewed, awaiting professional feedback. This is an early stage outline—the result of a sprint, still requiring more robust research, plus discussions with engineers, designers, and stakeholders on aspects of feasibility.


Discover travel partners through matching itineraries, budgets, and preferences.


Sojourn uses machine learning to help solo and small group travelers meet before and during trips. Quickly and easily match, chat, and make plans with compatible travelers based on similar interests, itineraries, and budget constraints. Make memories with new friends on your terms.

Problem Background

The goal of Sojourn is to improve the traveling experience. After conducting four in-depth research interviews, I learned that solo and small group travelers tend to use inefficient, unintentional platforms like dating apps and HostelWorld to make new friends while abroad. Each of these cater to specific user-types: single people already on dating apps, and hostel-users who tend to be younger and willing to sacrifice sleep while on vacation. They do not help travelers who hope to make friends, plan experiences, and save money in advance of traveling, not centered around dating or partying.

This gap in resources will continue to be felt: from December 2020 to April 2022, the search term “solo travel” increased by 267% and the number of digital nomads grew by 42%.

Many of the other platforms which offer travel accommodations either lack a connecting component (TripAdvisor), feel outdated or evoke safety concerns (Meetup, Couch Surfer), or are more expensive and require committing to a long trip with a group (digital travel agencies like G Adventures). The closest offering is GAFFL, which helps pair travelers with one another for activities, but does not leverage a matching algorithm, which means users must sift through potential travel buddies manually.


  • Allow users to quickly and accurately be matched with potential travel friends, digital nomads, and locals

  • Build an active user base of 1 million global travelers

User Stories

Based on target segments:

  1. Post-graduate solo travelers, all genders, approximate age range 25 - 35

  2. Digital nomads working abroad and traveling in-between gigs, all genders, approximate age range 30 - 40

  3. Locals interested in meeting travelers to show them around their city and meet foreigners, all genders, approximate age range 30 - 45

  • As a solo traveler, I want to be able to easily connect with other travelers, so that I can share experiences while abroad without relying on “serendipity”

  • As a solo traveler, I want to be able to chat ahead of time with fellow travelers to have a few plans in place before departure

  • As a traveler, I want to meet other travelers with similar enough traveling style but unique enough interests so that I can expand my cultural horizons

  • As a digital nomad, I want to connect with curious travelers and show them a more local perspective of the culture I’ve come to love

Proposed Solution

With Sojourn, a user can input their itinerary, budget range, general flexibility, and travel preferences and wishlist. Sojourn’s algorithm then helps travelers match with other compatible travelers for shared bookings, expeditions, and group discounts. Users (or groups of users) can chat ahead of time to make sure they’re aligned on their travel desires, and feel safe and compatible enough to meet up in real life. The app can also offer recommendations based on users’ profiles, past experiences they enjoyed, and those rated highly by others.

Safety is of the utmost importance for Sojourn, and will use AI text analysis to seek and flag bots and concerning language. Sojourn will also have a support team for user concerns, verification measures to authenticate users, and helplines to provide on-the-ground resources in each supported country.

MVP Features:

  • User profile authentication

  • Users input travel itinerary (complete or incomplete), travel habits and preferences, preferred budget range, and any elements which are fully booked vs. remain flexible

  • Algorithm matches users based on similarities

  • Users can chat ahead of travel to determine compatibility, comfort level, and refine itineraries together

  • Receive trip recommendations based on past reviews

  • Review activities and companions to improve machine learning algorithm

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