LuggageDrop

• 

  App Screenshot

Inspiration

Travellers frequently face a frustrating dilemma: early flight arrivals hours before accommodation check-in, or late-night departures long after check-out. Millions of travellers lose 4 to 8 hours of exploration time stuck at airport terminals, sitting in lobbies, or dragging heavy bags through crowded city streets. Meanwhile, local urban venues—boutique hotels, cafes, and shops—have underutilised space and empty foot traffic during off-peak hours. LuggageDrop was inspired by the desire to bridge this gap, giving travellers hands-free freedom while creating an effortless revenue stream for local businesses.

What it does

LuggageDrop is an AI-powered, two-sided micro-locker and courtesy holding marketplace:

For Travellers: Provides affordable, pay-as-you-go ($2–$3/hour) short-term bag storage at vetted nearby host venues. Travellers can search, reserve, pay, and securely verify bag drop-off and pickup using digital QR codes in the app.

For Host Venues: Monetiżes unused backrooms or closets into passive income while driving foot traffic and sales (e.g., offering travellers 15% off breakfast at check-in).

How we built it

LuggageDrop is built using a modern mobile frontend coupled with an advanced Jac & Jaseci graph backend architecture: Data Modelling via Jac Graphs: Replaced traditional relational databases with dynamic graph nodes (Traveller Node, Host Venue Node, and Storage Unit / Bag Tag Node) and relational edges (HOLDS, BOOKED_BY) to represent live spatial relationships. Autonomous Walkers for Logic: Utilised Jac Walkers to execute asynchronous workflows:

search_hubs: Traverses host nodes based on real-time traveller geolocation, operating hours, and capacity. booking_process: Calculates hourly rates, verifies availability, and reserves space. qr_verification: Executes the check-in/check-out handshake, shifting status from Pending $\rightarrow$ Stored $\rightarrow$ Released.

AI Enclave & Actions: Integrated native NLP and recommendation models to route context-aware partner promotions directly to travellers upon check-in and handle host vetting/risk management pipelines.

Challenges we ran into

Graph Modelling vs. Traditional Relational Storage: Designing dynamic state changes (such as tracking real-time capacity and dual-authentication QR status) across custom Jac graph nodes required rethinking traditional database patterns.

Real-time Geolocation Matching: Optimising Walker traversal speeds so travellers receive instantaneous venue recommendations based on live GPS coordinates without high latency.

Establishing Dual Security & Trust: Balancing a seamless, fast check-in experience with rigorous security measures (preventing bag mix-ups via digital cross-referenced QR handshakes and host verification pipelines).

Accomplishments that we're proud of

Fully Functioning Graph Engine: Successfully implemented Jac and Jaseci to power real-time matching, booking, and QR verification asynchronously.

Win-Win Ecosystem Design: Created a viable business model that simultaneously solves traveller mobility, generates host venue revenue, and drives local retail sales via context-aware AI promotions.

Secure QR Verification Handshake: Built an end-to-end digital-to-physical tagging flow that guarantees luggage protection and accurate release.

What we learned

The Power of Graph Architectures: Leveraging Jac and Jaseci significantly streamlined modelling multi-entity relationship workflows (Traveller $\leftrightarrow$ Storage Unit $\leftrightarrow$ Venue) compared to traditional SQL/NoSQL setups. AI-Driven Contextual Upselling: Integrating native AI models at the check-in touchpoint creates measurable value for hosts beyond mere storage fees.

What's next for LuggageDrop

Partner Network Expansion: Onboarding more local boutique hotels, cafes, and urban retail spaces to build higher density in primary travel hubs.

Enhanced Protection Add-ons: Rolling out optional premium liability guarantee tiers per bag booking directly in the app.

Scale User Acquisition & Mobile Experience: Refining the iOS and Android frontend UX/UI and launching targeted marketing for digital nomads, layover travellers, and short-term rental guests.

Built With

• jac
• jaseci