Curated preparedness search

Ask a preparedness question. Get an answer with the source attached.

The hosted demo searches a fixed collection instead of the open web, shows where each answer came from, and keeps the path open to run the same experience on your own data later.

Source-cited answers Curated collection Local and offline-capable Bring your own data
Disaster Clippy

Example search

We lost power after a storm. What should we do first?
Start with safety, water, refrigeration, lighting, and backup communication. Then check the strongest nearby guidance in your selected collection.

References

Power outage checklist Step-by-step household priorities
Emergency water storage How long stored water lasts and how to treat more
Collection-backed answers Hosted now, portable to your own deployment later

See it working

Try the demo

The live app runs on preparedness data. Ask it anything, see which source backed the answer, and switch between collections. That is the same interface you get for your own data.

Open the demo

Why it is different

Not just a chatbot

Every answer comes from the collection you can inspect. If an answer looks wrong, you can read the source yourself instead of trusting a model to be right.

See how trust works

Why it matters

Built for offline use

A preparedness tool that only works with internet access is not enough. The same engine can run locally, on a Raspberry Pi, or fully offline with your own models and your own collection.

See deployment options

Built for more than one dataset

The preparedness collection is the example, not the limit. The same engine has been used for building codes, flu preparedness protocols, and humanitarian reference archives. If you have a website, PDFs, transcripts, or offline archives that people need to search, this is built to adapt to that. It is designed for curated knowledge sites and offline archives, and works with Kiwix ZIM archives as a straightforward way to bootstrap a local collection.

How it opens up

Start with the demo if you want to feel the product. Go to About if you want the trust model. Explore Collections if you want to see what is searchable today. Use the technical docs if you want the platform model, ingestion path, and GitHub handoff.

Start with a useful, trustworthy search experience, then follow the same pattern into your own data.