Navi

New York City sees over 60 million visitors each year. Most go to the same ten places. Meanwhile, local businesses outside those corridors struggle for visibility, and residents absorb the side effects of concentrated foot traffic.

Navi was designed to change that dynamic by connecting visitors to neighborhood-level experiences that return value to the communities they visit.

Before any of the user interviews, the first move was visual. Tourists overload a handful of spots, and the patterns are easy to picture: the swaths of people taking photos in front of the Brooklyn Bridge in Dumbo, the perpetual crush around Times Square. If a routing layer could see this imbalance in real time, the thinking went, it could steer visitors toward neighborhoods that get less attention.

Select a neighborhood to see how that early concept worked. Regions represent narrative emphasis, not live geo analytics. This is the prototype that would have shipped on day one. The research pushed back.

The instinct felt right, and also a little like a bandaid. Routing tourists somewhere quieter still lets them visit that place the same shallow way. So before committing to redirection as the lever, the next step was talking to the people who would actually use the thing.

We audited six travel platforms and ran a heuristic evaluation of Airbnb using Nielsen's ten usability heuristics. Most tools were either transactional or editorial, but few connected discovery to community impact.

We then surveyed tourism professionals, local business owners, and long-time residents. Residents provided the richest dataset and most strongly shaped the personas.

Early on, the team considered a heatmap solution to reroute tourists away from congestion. The research reframed the problem, and it wasn't about volume.

Residents consistently favored hidden gems over tourist traps and aligned with community-driven travel models.

Major concerns included overcrowding, rising local costs, and the loss of neighborhood authenticity. Participants were open to AI personalization only when it remained subtle and transparent.

Highlights from 14 survey responses: Reimagining NYC Tourism: A More Meaningful & Sustainable Experience (resident survey).

Three personas emerged: Cain (group planner), Ororo (newcomer needing context), and Selina (commuter needing precision filters).

Given semester time constraints and no engineering handoff, the team focused on a coherent concept with real neighborhoods, realistic pricing, and clear user pathways.

The resulting framework was Learn, Plan, Go:

• Learn surfaces local context.
• Plan helps users compare and organize.
• Go converts intent into bookings.

Journey mapping validated the structure across all three personas.

What started as a heatmap turned out to be solving the wrong problem. Spreading tourists out doesn't change how shallow the visit is. Navi came from designing for the connection between visitors and the neighborhoods they're in.

Next opportunities include deeper neighborhood pages within Learn (surfacing history, local voices, and seasonal rhythms), richer collaborative planning tools for group travelers, and onboarding paths for local hosts and businesses to list their own experiences.

In concept testing, 78% preferred neighborhood-led recommendations over generic top-ten lists. That number is what Navi proved: community-centered travel holds up when the recommendations carry local context.