SpotU

Inspiration

SpotU started from a simple question: if I’m looking at a neighborhood, how do I know what kind of business actually belongs there?

Most location tools can tell you what is already nearby, but they usually stop there. I wanted to go one step further and turn that information into a decision.

Instead of just showing restaurants, cafés, shops, and venues on a map, SpotU looks at the cultural signals behind a place and asks: what fits this area, and what might still be underrepresented?

That became the idea behind SpotU.

What it does

SpotU is a location opportunity tool powered by Qloo.

You can search for a location and choose a business concept such as Restaurant, Café, Retail, Wellness, Entertainment, Art & Culture, or Lifestyle.

SpotU then looks at nearby places through Qloo and builds a picture of the area's cultural identity.

From there, it calculates:

• a Cultural Fit Index for the location
• a Cultural Match for each business concept
• a Market Gap Signal based on how represented that concept already is nearby
• an overall Opportunity Score

SpotU then ranks the concepts and shows which ones look strongest for that location, along with the signals that influenced the result.

You can also compare the same concept across two different locations, save analyses, and move around the map to analyze a new area.

How we built it

SpotU is built with Next.js, React, TypeScript, Tailwind CSS, Mapbox, Qloo, and Vercel.

When a user selects a location, SpotU sends the coordinates to a server-side API route.

That route calls Qloo's /v2/insights endpoint and retrieves nearby place entities within a 1.5 km radius.

From those places, SpotU looks at recurring Qloo tags such as ambience, neighborhood characteristics, culinary style, interests, and categories.

Those signals are used to build the location's Neighborhood DNA.

SpotU then evaluates eight different business concepts against that DNA.

For each concept, it calculates: Cultural Match based on how often relevant signals appear nearby. Market Gap Signal based on how represented that concept already is among the nearby Qloo places.

The final Opportunity Score is:

65% Cultural Match + 35% Market Gap Signal

The results are then ranked and shown back to the user with the strongest supporting signals.

Mapbox is used for the interactive map, location movement, and reverse geocoding.

The Qloo API key stays on the server and is never exposed in the browser.

Challenges we ran into

One of the biggest challenges was making the result useful without turning SpotU into a black-box score.

It was easy to calculate a number. The harder part was making it clear why a concept ranked highly.

That is why we added the strongest signals and the "How SpotU decided" section to the recommendation view.

Another challenge was defining the Market Gap Signal carefully.

A low number of similar businesses nearby does not automatically mean there is demand, so we made sure SpotU describes it as relative underrepresentation rather than vacancy or guaranteed opportunity.

We also spent time making the map experience feel natural. Users can pan to a different area, let SpotU detect the new location, and run the analysis again without restarting the whole flow.

Accomplishments that we're proud of

I'm proud that SpotU became more than a map with Qloo data on top of it.

It actually takes the Qloo signals, evaluates several possible business concepts, compares them, ranks them, and explains the result.

I'm also proud that the full experience is live and usable end to end:

• search and analyze a location
• explore the map
• rank business concepts
• inspect the signals behind a recommendation
• compare two locations
• save an analysis
• run the same workflow again somewhere else

The scoring is also visible and explainable instead of hidden behind a single unexplained recommendation.

What we learned

The biggest thing I learned is that cultural data becomes much more useful when it is turned into a decision instead of just displayed.

Qloo gave us signals about the character of a location, but the interesting part was figuring out how to turn those signals into something a founder or operator could actually act on.

I also learned how important it is to separate fit from opportunity.

A concept can fit a neighborhood culturally but still be heavily represented already. SpotU tries to look at both sides instead of treating them as the same thing.

Building SpotU also reinforced how important explainability is. If a user is making a real-world decision, showing the reasoning behind a score matters just as much as the score itself.

What's next for SpotU

The next step is to make SpotU useful for deeper real-world location decisions.

I would like to add more Qloo signals, more flexible business concepts, and stronger comparison tools.

I also want to combine cultural fit with additional data such as foot traffic, commercial rent, demographics, vacancy, and business performance where available.

That would allow SpotU to move from an early opportunity signal into a much more complete location decision tool.

Longer term, I see SpotU becoming something founders, operators, developers, and property teams can use before deciding what to open and where to open it.

Built With

• lucide-react
• mapbox
• next.js
• qloo-api
• react
• tailwind-css
• typescript
• vercel