📅 July 2025 | 👩‍💻 UX designer/researcher, software developer | 🛠 Figma, Lovable

💼 original product designed and developed for potential acquisition

Find a free, public space to work remotely from.

Remote Rover is a tool I designed and developed to help remote workers find free, work-friendly public spaces, particularly cafes, libraries, hotel lobbies, and food courts with reliable Wi-Fi.

Built around Google Maps and Yelp APIs.

😀 Solution: Remote Rover simplifies the search for remote work-friendly spaces by providing a curated, searchable map of cafes, libraries, and more with reliable Wi-Fi and other desired qualities like good food.

Intended project impact:

Remote Rover enables users to work more flexibly by removing the friction of finding reliable, free remote workspaces. It helps users discover new, underutilized locations — boosting foot traffic for independent cafes, libraries, and other venues that support remote work.

This can have a positive impact on the employers of such remote workers: discovering more suitable environments to work from can support productivity and work-life balance. By surfacing hidden gems and reducing decision fatigue, Remote Rover contributes to a more distributed, accessible, and adaptable remote work ecosystem for all.

😩 Problem: Finding remote work-friendly spaces is tedious: users must sift through reviews and photos on Google Maps, and new or lesser-known locations often lack clear signals of laptop suitability.

⚠️ Note: the product currently uses partial placeholder data due to high data costs, but the structure is built for scale.

🛣 A deeper look into the journey of finding workspaces online

I began my user research with secondary research on the process of finding remote workspaces online.

This is how users typically find spaces so far:

  • Google searches for “where to work remotely” that turn up articles from various publications with titles like 29 SF Coffee Shops For Getting Work Done

  • Asking “Favorite cafes to work from” on discussion forums such as Reddit

  • Searching Google Maps with keywords like “coffee shops with wifi”, then sifting through photos, reviews, and other details to assess if needs and wants are present such as a quiet atmosphere AND strong wifi

  • Asking people you know

💼 📈 Taking business needs into account: This product doesn’t just impact users, but also the businesses who are likely to receive increased traffic.

Taking this into account, I reviewed the potential impact of Remote Rover on business operations as depicted in this discussion, such as potential business conflict and profitability.

The overall takeaway seems to be that while Remote Rover is likely to increase the amount of patrons who use such spaces without contributing to sales, increasing traffic is likely to lead to greater net profits overall.

🔍 An example of the reviews users look for to determine if a space is work-friendly.

🕵️‍♀️ Wait! Do I have any competitors?

Of course, I looked for whether anyone else had built this concept. There were a few websites and mobile apps, but only two are currently active.

😬 I found them both very difficult to use.

❌ Nomadable immediately opens up with a completely random list of cafes in Southeast Asia. (For the record, I’m located in California.) To check in, you have to log in, which you aren’t informed of until clicking the Check In button as depicted.

✅ Each location features a good quality photo, a label of the place category, and a review snippet of how work-friendly it is.

❌ Remote Work Cafe only features cafes despite there being other free work-friendly workspaces, such as food courts and libraries. It’s also based in Greece 🇬🇷 and doesn’t function for locations in the US 🇺🇸 as intended.

🤔 So what do users look for in workspaces? What are their goals?

Below is a summary of typical user needs and concerns I discovered through discussion forums, articles, and Google Maps and Yelp reviews from remote workers.

🤫 Physical environment

  • Is the place quiet enough?

  • Is there a lot of seating available?

  • What is the aesthetic and ambiance?

  • Is it close to me and/or to public transit? Is there parking?

🔌 Technical elements

  • Is there strong wifi?

  • Are a lot of power outlets available?

  • Is there space for non-computer work such as reading, writing on paper, or knitting?

⚠️ Space policies

  • Can I sit here for hours at a time?

  • Is this space accessible for folks on a wheelchair?

  • Do I have to buy something to get the wifi password?

  • When does the location open or close?

🍔 Food & drink options

  • Does the place serve any food or drinks?

  • What’s on the menu?

  • How affordable is the food and drinks?

⚠️ Possible biases and constraints to consider

✅ As someone currently working remotely, this is an app I and many of my friends would use every week. I’ve worked remotely in several states and countries so my experiences with public workspaces is much broader than the majority of my potential users. In regards to use cases, I’ve also used such workspaces for non-computer and non-corporate work, such as sketching art and writing postcards.

❌ I’ve only worked remotely from densely populated urban areas. User needs may differ in less urban areas. For the usability testing described below, I’m currently trying to recruit participants who have worked remotely in less urban areas to account for this lack of experience and potential bias.

🔍 A more in-depth look at our prospective users

I used the information above to put together user personas and a journey map featuring common but diverse user journeys and needs, taking into account a wide range of events contrasting in theme and cultural background.

The main two user personas I designed the user flows around were:

  1. “The Local Regular” — remote workers looking for a local change of scenery from their homes

  2. “The Digital Nomad” — remote workers traveling to other states and countries

💼📈 Taking business needs into account: The following two personas are hiring musicians on behalf of organizations, thus their user needs reflect their organization’s needs of improving the organization’s reputation, increasing revenue, maintaining organized records, promoting their community, and more.

🤔 How might we fulfill these user needs and goals with design?

Our user goals:

  1. To know what the artist sounds like live ➡️

  2. To know the artist’s overall branding ➡️

  3. To know how an artist charges ➡️

  4. To know the artist’s behavior when performing ➡️

  5. More consolidated, less scattered info on artist across multiple disparate sources ➡️

  6. A search engine that organizes artists by factors users often look for such as genre and location ➡️

Possible design opportunities:

  1. Sections for artist audio and/or video

  2. Profile should feature multiple photos

  3. A section that lists multiple pricing options

  4. Social proof, like a section for reviews from other users

  5. A detailed profile section that includes years of experience, genre, and more

  6. Search results inspired by Google search results that tag artist rating, genre, and other key highlights

✨ The product so far

I designed each screen on Figma and brought it to life on AI vibe coder tool Lovable. The bulk of the code is AI-generated from my prompts and Figma files, but some of it is manually written by me.

The homepage: a simple location search bar

Based on my research and personas above, I figured users want to start searching for a location right away upon opening the site — contrary to the user flows of the competitors above. No need for logging in.

Like on Google, the screen asks the user for permission to detect and fill in their current location. The user can use their current location or enter any city/ZIP.

The user can open each location on Google Maps to view the location’s full details and navigate there.

Keeping the human users in the loop: location cleanup

I’m aware that the search algorithms (that I wrote myself) and generative AI won’t always be 100% accurate. That’s why users are encouraged to take down inappropriate locations to account for technical gaps.

All images are from the final developed product, NOT a Figma prototype.

Based on what many users look for in a workspace, I added a few default filters to make it easier for the users to find such spaces.

Viewing location search results

The search results and location details panels are inspired by the design of Google Maps.

✨ Each location has an AI-generated summary derived from the location’s Google Maps or Yelp reviews. Each review here summarizes how work-friendly the location is to provide immediate verification to the user.

🖼 The photo section provides additional verification by pulling photos of customers working at the location, falling back to photos of seating areas if work photos are unavailable.

⚠️ Note that the photos and AI summaries are currently placeholders as to limit data costs at this time (the location data is being requested from Yelp and Google Maps).

Crowdsourcing locations

To account for locations the algorithm may be erroneously skipping, users always see a dismissible floating banner mentioning they’re always welcome to submit a location. Users must confirm the submission has characteristics that make it work-friendly.

Filtering spaces by location type

Some users may only be in the mood for certain types of work-friendly spaces, such as food courts or libraries. They can remove unwanted categories by removing each tag.

Each map pin’s color matches the color of each tag depicted for coordination.

Curated reviews for extra human verification

Knowing that not everyone trusts generative AI’s accuracy, each location uses APIs and the algorithm to display selected reviews from Google Maps and Yelp from users expressing that it’s indeed work-friendly.

📊 Hypothetical KPIs

🤔 To measure the success of this product, I would look at:

  • amount of user signups

  • amount of musicians contacted per user

  • amount of musicians booked by user

  • search completion time

  • how often users refine their searches

  • most and least-used filters and types of search terms (genre, style, community, sound, mood, etc)

  • amount and content of booking error/support tickets

  • user reviews, especially feature complaints and requests

  • referral rate

  • average booking value

  • the percentage of new users who successfully complete a booking within a specific time frame

🏆 Hypothetical wins would look like:

  • users reducing the overall time they spend finding the right musician, as well as reducing the time spent in the overall cycle of hiring and completing the payment

  • users praising in the reviews “this made it so much easier to find the right person who align with my organization’s values!”

  • users acknowledging and praising redesigns that improved their user experience in any way such as “I’m glad they added a feature for showing other venues the artist has performed at!”

  • an increasing growth in the percentage of artists booked per user or completion of the booking cycle/decrease in user abandonment

  • artists gradually increasing how often other artists to join the platform

  • an increasing rate in user satisfaction with the band’s performance, which indicates truly finding the right fit beyond the initial search

🤔 Hypothetical future considerations

If I were to further build this product with unlimited resources, I would:

  • Test it on users from an even wider variety of event planning backgrounds, including professional event planners. This would include card sorting exercises to better understand other ways users might seek artists.

  • Perform user research that takes wider accessibility needs into account

  • A/B test multiple versions of the same flows and page designs

  • Perform further user testing on other flows beyond just finding an artist, such as setting up the performance.

  • Heuristic reviews by other designers

  • Interview artists who would be featured on the platform about the booking journey from their perspective, create empathy maps

  • Build and test features for users to report unwanted, inappropriate behavior by artists and for artists to report such user behavior as well

  • Figure out ways to further refine search accuracy for users to further reach the objective stated above of finding the right artist

  • Come up with edge cases, such as some pretty bad conflict between an artist and an organizer

  • Test it for the KPIs mentioned above

  • …and much more!