Open-source, locally-hosted, fully customizable, contextual warning generator.

Problem:
Countries implementing Early Warning Systems (EWS) under the UN EWEA initiative face a common bottleneck: converting meteorological data into actionable, sector-specific alert language in a way that is fast, consistent, and at scale.

PROBLEM STATEMENTS:
– Generic language doesn’t move people: A flood warning that does not mention the school, the clinic, or the road the community depends on does not produce action. Specificity saves lives.

– Drafting under pressure is error-prone: Warning officers work under time pressure. Without structured support, alert language is inconsistent, incomplete, or misaligned with CAP standards or local protocols.

– Data is there but connecting it is complex: Authorities often have GIS data on infrastructure, schools and health facilities but no fast way to connect that data to the impact footprint of an incoming hazard.

PROCESS
1. Prepare the relevant exposed assets layers: Load your own GIS data sources, local files, ArcGIS REST services, WFS endpoints, or shapefiles. Draw a polygon on the map (or paste coordinates from a met service warning), and the tool instantly queries every layer to find what is inside: schools, health facilities, roads, population centres, critical infrastructure.
2. Review and configure: Select which layers to include in the alert. Set the hazard type, severity, and authority level. Load your organization’s pre-validated early action library (.db file) to ground the output in approved language.
3. Generate sector-specific CAP-aligned descriptions: The tool queries your library for matching pre-validated messages, then passes them with GIS impact data to the AI model of your choice (all openAI standards work, including locally-hosted models). It produces sector-specific and blocks ready to paste into your CAP editor, each tagged by source. It is also possible to run without AI-integration.

Every output block is tagged so you always know the source of the message:
“Library” means your validated message, used as written, with local details slotted in.
“Contextualised” means your validated message, adapted with specific local data from the polygon.
“Synthesised” means no library match existed; the message was generated from your GIS data alone.

User data need ever leave their machine. For authorities handling sensitive infrastructure data, data sovereignty is non-negotiable. ARIA is built around that principle. Every component is open source or an open standard.

Solution:
A free and open tool that helps emergency authorities query their own GIS data within an impact polygon, then generate sector-specific, library-grounded text for Common Alerting Protocol warnings.