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 startups that 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, wasted — on raw satellite data: gathering it, processing it, and making sense of it.

This holds back the time-to-market of new projects by 3–6 months. With the WaterTech industry projected to grow to $300 billion by 2030, that represents billions of dollars in wasted resources and missed revenue.

That is why we introduce Breezy.

With Breezy, we eliminate that 80% waste.

Think of it as ChatGPT for hydrological satellite data. We have built a no-code platform that sits on top of data from Copernicus, Sentinel Hub, and other relevant sources.

Users describe their 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.

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🛰️ 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.

Indicators calculated from Sentinel-2 bands:

• 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

Converted into risk layers:

• Turbidity risk
• Sediment risk
• Brown-water signals
• Possible bloom proxy
• Optical pollution load
• Hotspot anomalies
• Field-check 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.

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💦 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.

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.

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🤼 Team

Piotr Antoniszyn - Project Manager / Data Analyst

Point of contact

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 / Entrepreneur

AI Developer and entrepreneur passionate about building practical products at the intersection of data, automation, and user experience.

A strong advocate of vibe coding, he believes that modern AI tools can radically shorten the path from idea to working prototype, helping teams test, iterate, and ship faster.

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

Entrepreneur and physicist with a strong focus on turning complex ideas into practical business opportunities. He brings the sales perspective to the team, helping connect technical innovation with real market needs, customer value, and go-to-market strategy.

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.