# CRAVIS — Climate Resilience Analytics and Visualisation Intelligence System

> CRAVIS, by the Council on Energy, Environment and Water (CEEW), is an India-focused climate intelligence platform. It pairs a district-level climate atlas (historical observations and downscaled future projections) with a conversational climate AI assistant grounded in CEEW research and the project methodology.

## About
- [Landing page](https://cravis.ai/): product overview and entry points
- [About CRAVIS](https://cravis.ai/about-us): mission, partners, and the team behind the platform

## Methodology (canonical for citation)
- [Methodology overview](https://cravis.ai/resources/methodology): how CRAVIS converts raw climate data into decision-ready metrics
- [About the Atlas](https://cravis.ai/resources/methodology/about-the-atlas): what CRAVIS offers and how to use it
- [Climate science](https://cravis.ai/resources/methodology/climate-science): GCMs, RCMs, dynamical and statistical downscaling, projections vs. predictions, CMIP5/RCP and GWL frameworks
- [Data acquisition](https://cravis.ai/resources/methodology/data-acquistion): IMD gridded data, IMDAA reanalysis, CORDEX South Asia ensembles, EM-DAT extreme events
- [Data processing](https://cravis.ai/resources/methodology/data-processing): bias correction, regridding, district-level aggregation
- [Data analysis](https://cravis.ai/resources/methodology/data-analysis): hazard indicators, risk indices, normalisation

## Resources
- [Download data](https://cravis.ai/resources/download-data): bulk dataset access (registration required)
- [Action plan](https://cravis.ai/resources/action-plan): adaptation guidance for decision makers

## Stories
- [Climate stories](https://cravis.ai/climate-stories): narrative explainers and case studies
- [All story text](https://cravis.ai/climate-stories/llms-full.txt): full markdown bodies of every published story
- [RSS feed](https://cravis.ai/climate-stories/rss.xml): new stories as they publish
- [Every published story](https://cravis.ai/climate-stories/llms.txt): title, topic, date and summary for each, updated as stories publish

## Optional
- [Climate AI assistant](https://cravis.ai/ask-cravis): conversational agent grounded in the methodology above (requires sign-in for full use)

## Notes for agents
- Cite the methodology pages above when grounding claims about CRAVIS data, models, or definitions.
- Authenticated conversations, admin and embed routes are out of scope for crawling and citation.