AI app for government · no coding needed
Emergency exercise after-action desk
Turn exercise evidence into accountable improvement work.
Made for: Local emergency planning teams

What it does for you
The problem
Exercise observations become long reports with weak action ownership.
What it gives you
Reviewed exercise improvement report
What you give it
Authorized exercise notesevaluation criteria
How it works, step by step
- Group exercise observations
- Link supporting records
- Distinguish simulation from reality
- Draft improvement actions
- Assign reviewed owners
- Export after-action report
What you see on screen
- Observation intake
- Finding clusters
- Action evidence
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Emergency exercise after-action desk with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Emergency exercise after-action desk with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links1 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare23 KB
- prompt-vps.mdThe same build on your own server (Docker)23 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data197 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
For local emergency planning teams, turn authorized exercise notes and evaluation criteria into reviewed exercise improvement report. Address this specific problem: exercise observations become long reports with weak action ownership. The aim: turn exercise evidence into accountable improvement work. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.
The buyer creates a project, supplies authorized exercise notes and evaluation criteria, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final reviewed exercise improvement report before use. Retain source links and a version history for the next cycle.
How the AI works
Synthesize observations while planners validate operational implications. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.
Safeguards
Preserve official source versions, accessibility and audit records. Confirm agency-specific procurement, records and data handling requirements during discovery. Post-exercise analysis; no live emergency command. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.
What to build first
Costed pilot: Post-exercise analysis; no live emergency command. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: group exercise observations; link supporting records. Support the third task through an assisted review queue: distinguish simulation from reality. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of reviewed exercise improvement report. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.
What it can connect to
Official publications, agency document stores and approved service workflows. Read-only business data exports, reporting databases and task trackers. Reconcile source totals before scheduling recurring data refreshes. Begin with uploads and exports of authorized exercise notes and evaluation criteria. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.
The screens in detail
Open with a compact overview and filters for the relevant period or segment. Let users drill from each theme or metric into underlying records. Keep source definitions and missing-data notes near the result. Use an action panel to assign investigations and record what was learned. Open with observation intake; move into finding clusters for the detailed task; finish in action evidence for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.





