Safewaters for Web

SafeWatersAI — Ocean Risk Forecasting Tool

SafeWatersAI is a web-based service that applies deep learning models to assess the likelihood of shark encounters using environmental and oceanographic data. The system delivers near real-time safety indicators to swimmers, surfers, lifeguards, and other beach visitors so they can make smarter choices about entering the water and taking precautions.

How it works

The platform ingests multiple data sources — for example, water temperature, current patterns, time of day, recent marine life sightings, and weather conditions — and feeds them into trained neural networks that output a risk score. Those scores are translated into simple advisories and visual cues that are updated continuously as new data arrives.

Devices and availability

SafeWatersAI will be offered as a responsive web application and as native mobile apps for both Android and iOS, enabling users to check beach conditions and risk levels while they are out and about.

Community, research, and conservation

A portion of the project’s earnings is earmarked for ocean stewardship: the app will dedicate 5% of profits to marine cleanup and conservation programs. In addition, the team plans to publish research findings and collaborate with other organizations to advance understanding of marine risk factors.

Planned highlights

• A public API to allow third-party apps and websites to incorporate SafeWatersAI’s risk assessments.
• Dedicated contributions toward ocean cleanup and conservation projects funded by a share of revenues.
• Regular research reports and shared datasets to support broader scientific efforts.
• Native mobile applications for on-the-go access on both major smartphone platforms.
• Targeted launch window scheduled for the summer season.

Recommended premium option

For users seeking an alternative paid solution, consider Jeeva (paid), which may offer complementary features or different pricing and support models.

Roadmap and next steps

The team is progressing toward a summer release while finalizing integrations, user testing, and partnerships with conservation groups. Future development priorities include expanding data sources, improving model performance, and rolling out the planned API.