sobr

Allows bar goers to track sobriety in real time and automatically notify them if they are at risk level (high BCA) and automatically notifies trusted contact and suggest safer routes for user.

Comment

Sobr — Sobr app

Las Vegas is globally recognized for its nightlife. From world-famous clubs to packed casino floors the city is known for its night and club life . But with that culture comes a serious issue. Nevada is ranked amongst the top 20 cities amongst ALL U.S. cities for DUI and drunk driving related incidents.

We were inspired and motivated by a simple question:

How do we turn nightlife safety to be proactive and ensure people's safety while out clubbing

Instead of waiting for something bad to happen, we wanted to build a system that steps in before a night turns into another Las Vegas statistic.

What it does

Sobr is a real-time sobriety awareness and intervention platform for nightlife venues.

It allows:

  • Customers to join a session via QR code
  • Bartenders to monitor session activity
  • The system to detect when someone may be too intoxicated
  • Automatic notification of a trusted contact if risk thresholds are exceeded
  • AI called "Breathy" that talks to the intoxicated/non-intoxicated person and gives them a breakdown of their levels and recommends next steps based on their recorded levels.

Rather than relying purely on human judgment, Sobr introduces structured tracking and safety automation.

Conceptually, intoxication risk can be thought of as a function:

[ R = f(d, t, w) ]

Where:

  • ( d ) = number of drinks consumed
  • ( t ) = time elapsed
  • ( w ) = body-related factors (weight, tolerance, etc.)

When ( R ) crosses a defined safety threshold:

[ R > R_{critical} ]

Sobr triggers a safety workflow (such as notifying a trusted contact).

The goal isn’t to police behavior, it’s to reduce preventable harm.

We want individuals that party in Vegas to be able to be safe while doing so.

How we built it

We built Sobr using:

  • Next.js for the frontend and routing
  • Supabase for authentication, database management, and real-time backend logic
  • QR-based session joining for seamless bar-to-customer onboarding
  • Server-side logic to manage drink sessions and intoxication thresholds

The flow works like this:

  1. A bartender generates a QR code.
  2. A customer scans and joins a live session.
  3. Drinks are logged to that session.
  4. Backend logic evaluates risk conditions.
  5. If necessary, a notification workflow is triggered.

We focused heavily on real-time session management and relational database design to ensure:

  • Each customer session is uniquely identifiable
  • Data integrity is maintained
  • Alerts cannot trigger without valid linked records

Challenges we ran into

1. Database Constraints & Relationships

We faced issues with foreign key constraints and null violations when linking sessions to customers.

This especially was prevalent when that a QR Code is generated (without it being scanned) it would generate a customer on the bartender's side regardless.

2. Real-Time Session Handling

Generating QR codes that linked to dynamic sessions (instead of static routes) required careful token management and routing logic.

3. Balancing Safety & Privacy

We had to think carefully about: When should an alert trigger? How do we prevent abuse of the system?

We didn’t want Sobr to feel invasive it needed to feel protective.

4. Defining “Too Drunk”

Alcohol affects everyone differently. There isn’t a universal formula. We had to design a flexible risk model rather than a rigid rule.

Accomplishments that we're proud of

Building a full-stack working demo Implementing QR-based session creation Creating a proactive safety mechanism rather than just analytics Designing a concept that fits directly into an existing nightlife

Most importantly, we transformed a social problem into a technical solution.

What we learned

Real-time systems require careful state management. Database design matters more than you think especially with foreign keys. Technical solutions to social problems must consider ethics. Simple in UX is also critical in high-noise, high-energy environments like clubs.

What's next for Sobr

Smarter risk modeling using more advanced statistical or ML techniques Integration with rideshare APIs to offer safe transport options Trusted-contact customization and user controls Expanding beyond Las Vegas to other nightlife-heavy cities

Long term, we envision Sobr as something that doesn't stop the party, it's about making sure everyone gets home safely.

Built With

Share this project:

Updates