In production

Snowy: a weather platform with maps, real-time data and AI.

Snowy is a weather platform for forecasts, maps, stations, alerts, reservoirs, air quality, earthquakes and smart tools, in a fast experience aimed at real decisions.

It combines sixteen weather models into a single forecast that states how much confidence it deserves, generates its own radar from the source data and has an assistant that answers with the network's data. Over the last three months it has recorded 16.8 million impressions on Google, 257,000 clicks and more than 1,400 registered users.

Open Snowy See CV

Role: Design, development and infrastructure
Period: Since 2025, ongoing
Scope: Whole product, from database to SEO
Reach: 16.8M impressions in 90 days

Snowy main interface with AI assistant, search and weather planner

Sixteen models that disagree.

Each source gives a different forecast for the same point, and the further ahead you look, the further apart they drift. The product work is not showing them all: it is giving one answer and saying how much it can be trusted.

Spread between modelsThe answer

Now+3 days+10 days

A schematic of the behaviour, not a measurement: what it shows is that uncertainty grows with lead time.

What Snowy is

A weather platform for Spain: multi-model forecasting over live data, with interactive maps and an assistant that answers in plain language.

Multi-model forecasting

Comparison of weather models, forecasts by location and tools to make sense of uncertainty.

Weather map

Radar, stations, alerts, earthquakes, air quality and environmental layers in one interactive interface.

Live stations

A station network with current and historical data, plus an onboarding flow for users.

SEO content

Indexable pages for locations, phenomena, pollen, air quality, reservoirs and WikiMeteo.

AI assistant

Conversation, voice, specialised tools and answers grounded in weather data.

Derived products

Energy, embeddable widgets, an eclipse product and new verticals on the same technical base.

Different surfaces, one architecture.

Each module answers a specific question with its own data sources, and all of them share the same technical foundation.

AI assistant

Answers in plain language with station and model data, such as what to wear or whether alerts are active.

Reservoirs

Reservoir levels from official data, their weekly evolution and comparisons by river basin.

Historical climate

Historical series showing how temperature has changed in each area.

Earthquakes

Snowy earthquake monitor showing a recent seismic event

Seismic activity in real time from official sources, with the detail of each event and reports from users.

Stations

Weather station detail in Snowy with live metrics

The page for each weather station, with its live data and its history.

Ski resorts

Forecast, snow report and piste map for 30 ski resorts in Spain and Andorra.

Organic growth, with no ad spend.

SEO, performance and product usefulness already show up in usage: organic search, clicks and registered users on a platform of my own.

16.8M

impressions

last 3 months in organic search

242k

clicks

traffic from Google over 3 months

1,400+

registered users

an owned base for community and new features

models

ECMWF, GFS, ICON, ARPEGE, GEM and more

1,000+

stations

official network and Snowy community

370+

reservoirs

status and evolution across Spain

1,000+

terms

WikiMeteo in Spanish

20+

AI tools

assistant, voice and daily decisions

10+

map layers

radar, stations, risks and air

How it is built.

Snowy runs on a decoupled architecture: Next.js for SSR, SEO and UI; NestJS for business logic and data; Redis for cache; MySQL for persistence; and separate services where radar, CMS or jobs carry different loads.

FrontNext.jsServer rendering, SEO and interface

EngineNestJSBusiness logic, integrations and data model

CacheRedisWhat gets asked a lot and changes little

PersistenceMySQLThe state that has to survive

Separate

Radar
CMS
Jobs

They split off when their load looks nothing like the rest: the radar renders tiles, the jobs run on a schedule.

Front

Next.js · React · TypeScript · Tailwind · MapLibre

Backend

NestJS · Prisma · MySQL · Redis

Infrastructure

Docker · Coolify · Cloudflare · Own VPS

AI and data

AI SDK · RAG · Batch jobs · GRIB2

Architecture

A multi-repo ecosystem with a Next.js front, NestJS engine, Vite CMS, Node.js radar, batch jobs and cross-cutting documentation.

Technical SEO

Indexable pages for cities, models, tools, stations, reservoirs, earthquakes, air quality, pollen, alerts, WikiMeteo and special content.

Data

Professional models, stations, reservoirs, earthquakes, air quality, pollen and official sources unified under one internal model.

Infrastructure

Production on VPS, Docker, Caddy, Cloudflare, GHCR, GitHub Actions, healthchecks, rollback and operational runbooks.

Radar

Interactive map with radar, stations, earthquakes, air quality, risk zones and environmental layers in real time.

AI

A weather assistant with natural language, voice mode and specialised tools that turn weather data into practical decisions.

B2B

Snowy Energy, embeddable widgets and sector verticals as a natural extension of the core weather product.

SEO, data and performance as architecture decisions.

Users and Google both need fast answers. That's why the project runs on SSR, per-domain cache, an internal data model, IndexNow, revalidation and provider abstraction.

Integrated sources

AEMET
Stations and warnings
Euskalmet
Basque Country stations
MeteoGalicia
Galicia stations
MITECO
Reservoirs and water reserve
IGN
Earthquakes in Spain
USGS
Earthquakes worldwide
CAMS
Air quality and pollen

The goal is to unify heterogeneous providers into one consistent model, precompute the expensive parts and answer the end user very fast.

Energy, widgets and sector verticals.

The same data, maps, forecasts and AI make derived products possible: energy forecasting, embeddable widgets and tools for specific cases.

Snowy Energy

Renewable forecasting, simulator and dashboard for solar energy as a B2B vertical inside the Snowy ecosystem.

snowy.es/productos/energia

B2B widgets

An embeddable SDK to bring weather data, maps, tools or the AI assistant into third-party sites.

snowy.es/productos/widgets

Eclipses

A content and planning product: it launched with the total eclipse of August 2026 and already runs for the 2027 one.

snowy.es/eclipse-2027

Precipitation, live.

Snowy's precipitation radar, half an hour per step, over the 1,862 stations reporting live.

From scattered data to a useful tool.

Snowy home with search, assistant and map entry points

Bringing very different sources together
Public data from official agencies, weather stations run by individuals and values Snowy computes from the forecast models. The work is ordering them and presenting them so they help people make everyday decisions.
Snowy Developer, the data through an API
With that data, Snowy has become one of the reference weather accounts in Spain. Snowy Developer is the portal that offers it to anyone who wants to build it into their own services, with public documentation and a live demo that needs no sign-up.

Snowy has had public reach too.

The project was born out of LaRiojaMeteo and has appeared in press, radio and public portals. That's a signal of a real product, a community and continuity.