WAIT

💎 Idea

Today, hydrological satellite data analysis is bottlenecked by high entry barriers. Building custom solutions requires a rare mix of domain expertise, DevOps, and programming. Stakeholders are forced to waste time on repetitive tasks, while existing market tools are prohibitively expensive to implement. 

WaterTech start-ups who try to use satellite data to solve real life water crises face a huge difficulty. According to Gartner, 60-80% of their time is spent (or better say wasted) on raw satellite data. Gathering it, making sense of it.

It’s holding back the Time-to-Market of new projects for 3-6 months. With the WaterTech industry projected to grow to $300 billion until 2030, that represents billions of dollars of wasted resources and missed revenue.

That's why we introduce Breezy.

With Breezy, we eliminate that 80% waste. Think of it as ChatGPT for hydrological satellite data. We’ve built a No-Code Platform that sits on top of data from Copernicus, Sentinel Hub and other releveant data sources. 

You describe your idea in plain English, and our engine handles the complex selection of spectral bands, filters, and processing in the background. 

We turn months of engineering into minutes of insight, lowering the entry barrier from thousands of dollars to as low as $50.

🛰️ EU space technologies

We use Copernicus Sentinel data via Copernicus Data Space / Sentinel Hub APIs.

Main sources:

• Sentinel-2 L2A — multispectral optical imagery used to detect water colour changes, turbidity, sediments, brown-water signals and possible bloom-related patterns.
• Sentinel-1 GRD — radar data used as an optional support layer to improve water detection and reduce uncertainty in cloudy or difficult conditions.

From Sentinel-2 bands we calculate indicators such as:

• NDWI - Normalized Difference Water Index
• MNDWI - Modified Normalized Difference Water Index
• NDTI - Normalized Difference Turbidity Index
• NDSSI - Normalized Difference Suspended Sediment Index
• NDMI - Normalized Difference Moisture Index

These are converted into simple risk layers:

• turbidity risk
• sediment risk
• brown-water risk
• possible bloom proxy
• optical pollution load
• monitoring priority

This brings value because raw satellite data becomes an easy-to-read water risk map showing where conditions look unusual and where field checks should be prioritised.

 💦EU Space for Water

We are addressing the challenge “Tracking and preventing water pollution”, but our approach is platform-based.

Our solution is a PaaS layer for water-related Earth Observation data. It transforms raw Copernicus Sentinel data into simplified, ready-to-use water intelligence layers such as turbidity risk, sediment risk, brown-water signals, possible bloom proxy, optical pollution load, hotspot anomalies and field-check priority.

On top of this, we provide no-code workflows and a natural language interface, so users can build analyses or query water data without needing to understand satellite bands, Copernicus APIs, raster processing, masking or geospatial indexing.

This allows startups, municipalities, NGOs and environmental companies to build their own SaaS products much faster and at lower cost. Instead of spending months preparing satellite data infrastructure, they can consume clean, contextualised and API-ready water risk data.

By lowering the technical, time and cost barriers to EU space data, we help more actors create tools for pollution monitoring, recreational water safety, environmental reporting, field inspection planning and public alerts.

In short: we shorten the path from raw satellite data to real water-management applications.

🤼 Team:

Piotr Antoniszyn - Project Manager/Data Analyst

I transform raw data into clear narratives, identifying the patterns and insights that drive strategic decisions. I bridge the gap between user requirements and technical execution, ensuring every priority is aligned. This is my second hackathon taking the lead to keep the momentum high and — as Radiohead would say — to make sure everything is in its right place.

Maks Borysławski - AI Developer/Enterpreneur

Maciej Max - Solution Architect

Solution Architect with 20+ years of experience in data engineering, cloud-native data platforms, and large-scale architecture, with a strong background in GIS and geospatial technologies.

Oskar Molewski - Entrepreneur/Physicist/Sales

Dariusz Szałucki - GIS & Cloud Data Engineer

Data engineer focused on turning complex data ecosystems into practical, usable products. Especially interested in democratizing access to data — making advanced sources like satellite imagery, geospatial datasets and environmental signals easier to use for people who do not have deep technical expertise