s-ai-lor

Inspiration

This project began with something personal. After years of watching my brother sail across the world as a merchant mariner—often too busy or too far to collaborate—I finally had the chance to work with him. I’ve always shared with him my passion for AI agents, and he has always shared stories from life at sea: the pressure, the responsibility, the complexity, and the countless opportunities for automation that the maritime industry has yet to embrace. Combining our worlds for the first time felt meaningful. S-ai-lor was born not just from a problem we wanted to solve, but from a rare moment where two brothers could finally build something together.

What it does

S-ai-lor is an AI-native maritime voyage planning platform that centralizes and automates every major step in a sailor’s journey. Users can plot multi-stop voyages on an interactive Mapbox interface, automatically detect hazard zones and regulatory boundaries, receive AI-curated risk intelligence from live web sources, and generate optimized route recommendations. Once a route is selected, S-ai-lor uses AI agents to create a unified communication plan tailored to each port—drafting required emails, selecting relevant ship certificates from a knowledge base, and scheduling messages according to port rules. The result is a seamless workflow from route planning to regulatory compliance to port communications.

How we built it

We built S-ai-lor entirely using Cursor as the development environment, Mapbox for interactive marine route visualization, and Lovable for rapid prototyping and UI generation. Cursor’s AI agents handled much of the boilerplate architecture and code refinement, Mapbox created the dynamic plotting experience sailors need, and Lovable allowed us to iterate on interface concepts quickly. Together, these tools enabled us to simulate a production-grade system in a fraction of the usual time.

Challenges we ran into

One of the biggest challenges was deeply understanding the operational workflows of sea officers—everything from voyage planning to notices to mariners to port documentation. Integrating Mapbox for the first time also had a high learning curve, especially when simulating multiple route options and hazard overlays. Designing UI that meets real mariner usability standards required multiple iterations, and identifying the pain points where AI agents could deliver the strongest impact demanded careful thought, not just technical execution.

Accomplishments that we're proud of

We successfully built a simulated end-to-end system that demonstrates what a modern, AI-assisted three-stop voyage could look like: route plotting, regulation overlays, real-time risk scouting, and full automated communication plans. The project shows a clear vision of how the maritime industry—often slowed by manual processes—can be completely transformed through automation and intelligence.

What we learned

This journey massively expanded our understanding of the maritime world. From regulatory zones to port procedures to documentation frameworks, we gained deep insight into how mariners operate and where technology can meaningfully assist them. We also learned how to combine interactive mapping, AI reasoning, UI/UX design, and workflow automation into one cohesive system.

What's next for S-ai-lor

Next, we’re extending S-ai-lor with AI-native capabilities such as a RAG-powered chatbot trained on company-specific regulations, international standards, and safety procedures—helping crews with audits and operational queries. We aim to expand automated compliance, add real-time vessel intelligence, and introduce configurable company policy layers. Our long-term goal is to pitch this system to major maritime operators like MMC and Eaglestar in hopes of securing funding to bring S-ai-lor from prototype into a fully deployed solution.