Complete AI Training

AI app for pr and communications · no coding needed

Public fact correction impact mapper

Correct the known spread of an error efficiently.

Made for: Communications teams managing owned channels

What Public fact correction impact mapper looks like
Open the demo For members · a working demo with sample data

What it does for you

The problem

Teams cannot identify which downstream assets repeat a corrected fact.

What it gives you

Editor-reviewed correction propagation map

What you give it

Approved correction recordsowned content graph

How it works, step by step

  1. Trace reused claim variants
  2. Rank known distribution dependencies
  3. Prepare targeted correction tasks
  4. Compare the reviewed result with the recorded baseline and value assumptions
  5. Capture corrections and named-owner approval before consequential use
  6. Export a versioned editor-reviewed correction propagation map with source references and unresolved questions

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 Public fact correction impact mapper 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.

Sign in Become a member

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 Public fact correction impact mapper with you.

Have Nexibeo build it

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 build3 KB
  • prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare27 KB
  • prompt-vps.mdThe same build on your own server (Docker)27 KB
  • spec.jsonData model, API, AI pipeline, acceptance criteria14 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

Correct the known spread of an error efficiently

Confirm the buyer's problem and scope, collect approved correction records and owned content graph, then follow this sequence: 1. Trace reused claim variants. 2. Rank known distribution dependencies. 3. Prepare targeted correction tasks. Resolve uncertain cases with qualified reviewers, approve editor-reviewed correction propagation map, and measure uncorrected owned copies and reviewer hours per verified correction against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. No automated external takedowns or unsupported reach estimates. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Verify public facts and quotations. Keep publication authority explicit and preserve the original context behind media and reputation findings. No automated external takedowns or unsupported reach estimates. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: No automated external takedowns or unsupported reach estimates. Implement one approved input format, a bounded representative case set and the first two task modules: trace reused claim variants; rank known distribution dependencies. Support the third module with operator review: prepare targeted correction tasks. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

What it can connect to

Approved company facts, permitted media sources and publication workflows. Read-only business data exports, reporting databases and task trackers. Reconcile source totals before scheduling recurring data refreshes. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Data and definitions, Pattern investigation, Action and value review. 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. Make the task-specific outcome editor-reviewed correction propagation map visible beside its evidence, review state and value baseline.