aftermath

Inspiration

We were inspired by the idea that history doesn't just exist in museums; it lives in our neighborhoods. Natural disasters, weather patterns, and past claims leave behind patterns that can help predict future risk, but most people never see or understand that data.

We wanted to reimagine risk assessment as something meaningful and engaging, combining historical data with a museum-style experience. By turning past events into "exhibits," we aimed to help users learn from history, understand their personal risk, and take action to reduce it.

What it does

Aftermath is a personalized risk assessment mobile web application that transforms your ZIP code and home profile into a living museum of risk.

Users complete a short intake quiz, and the app generates a personalized risk score based on historical hazard patterns and home characteristics. It clearly explains what raised or lowered the score and presents exhibits that visualize risk over time.

Moreover, Aftermath encourages taking action. Users receive tailored safety recommendations, real-time alerts, and a set of steps to reduce their risk score. Each action contributes to a shared neighborhood chronicle, creating a community-driven record of preparedness. Over time, these actions become a part of a "ledger" that reflects both individual and collective risk reduction.

How we built it

We built Aftermath as a web application using Next.js, React, and Tailwind CSS.

The core of the app is a transparent risk scoring engine that combines ZIP-level historical hazard exposure, home characteristics (roof age, home age, type), and existing protections. Instead of using a black-box model, we designed an interpretable scoring system so users can clearly understand the factors that affect their risk.

On top of this, we layered an AI-inspired interpretation system that translates risk factors into natural-language explanations, generates personalized safety recommendations, and connects historical patterns to present-day risk.

We also designed a multi-page experience, including an onboarding quiz, a personalized "exhibit" score view, risk galleries, a historical archive, a community chronicle and ledger, and an actions and rewards system with badges and streaks.

Challenges we ran into

One of our biggest challenges was coordinating multiple interactive pages within Next.js. Since features like the quiz, chronicle, and ledger rely on user state and routing, we had to carefully structure them as client components while keeping navigation smooth across the App Router.

Building the quiz flow was also more complex than expected. The multi-step form needed to handle ZIP input, validation between steps, protection selections, and then persist the data correctly before transitioning to the exhibit page.

Another challenge was connecting all of the pages into a cohesive experience. Different parts of the app were built separately(quiz, exhibit, cases, chronicle, ledger), so we had to go back and properly link them with navigation and ensure the flow made sense from a user perspective.

We also spent time refining consistency across the app. As new pages were added, some had different layouts and styles, so we had to restructure them to match the same theme, dimensions, and typography so the entire experience felt unified.

Finally, we had to think carefully about how to represent risk in a way that felt meaningful but still simple. Designing a scoring system that responded to user inputs while remaining understandable and explainable was an ongoing challenge.

Accomplishments that we're proud of

We're most proud of turning a dry concept, insurance risk, into an engaging, narrative-driven experience.

Some highlights:

A full end-to-end product flow from intake to action
A transparent risk scoring system with clear drivers
A museum-inspired interface that makes exploration intuitive
A community-driven chronicle and ledger system
An actions and rewards layer that encourages real behavior change

We also successfully integrated AI-style explanations in a way that enhances clarity.

What we learned

We learned that users trust systems more when they can understand them. Building a transparent scoring model made it easier to explain risk and build credibility. We also learned how powerful design and storytelling are in shaping user behavior. Framing risk as a "museum" made the experience feel less intimidating and more engaging. And finally, we learned that AI doesn't always have to be used to replace logic. It can work best as a layer that explains, guides, and personalizes.

What's next for Aftermath

Next, we want to make Aftermath into a more dynamic and real-world connected platform.

We plan to integrate real-time weather and hazard APIs so that risk assessments and alerts reflect live conditions, not just historical patterns. This would allow users to receive timely, location-aware warnings tied directly to current exposure.

We also want to expand beyond the Dallas area by incorporating more geographic regions, enabling broader coverage, and making the platform useful to a wider range of users.

Another key focus is strengthening the community layer. By increasing neighborhood participation, we can build a sense of awareness and trust, where users can learn from collective behavior and feel more confident in their understanding of risk.

Finally, we want to deepen personalization. Future versions will deliver more tailored alerts and recommendations based on user behavior, seasonal trends, and evolving conditions, making the experience more adaptive and proactive over time.

Our goal is to turn Aftermath into a continuous risk companion that not only informs users but actively helps them stay ahead of potential threats.