Complete AI Training

AI app for writers · no coding needed

Backlist edition update mapper

Plan a new edition around the consequences of each approved update.

Made for: Independent nonfiction publishers

What Backlist edition update mapper looks like
Open the demo For members · a working demo with sample data

What it does for you

The problem

New editions require changes scattered across chapters and references.

What it gives you

Editor-approved edition update plan

What you give it

Owned prior editionsauthor-approved update notes

How it works, step by step

  1. Index update topics
  2. Find affected passages
  3. Flag dependent examples
  4. Track reference checks
  5. Assign editorial tasks
  6. Export revision schedules

What you see on screen

  • Edition comparison
  • Affected passages
  • Revision plan

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 Backlist edition update 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 Backlist edition update 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 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 data196 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 independent nonfiction publishers, turn owned prior editions and author-approved update notes into editor-approved edition update plan. Address this specific problem: new editions require changes scattered across chapters and references. The aim: plan a new edition around the consequences of each approved update. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.

The buyer creates a project, supplies owned prior editions and author-approved update notes, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final editor-approved edition update plan before use. Retain source links and a version history for the next cycle.

How the AI works

Suggest affected passages without inventing updated facts. 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 author voice, source attribution, quotation accuracy and usage permissions. Authors approve substantive changes and publication scope. Planning only; claims require current source verification. 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: Planning only; claims require current source verification. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: index update topics; find affected passages. Support the third task through an assisted review queue: flag dependent examples. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of editor-approved edition update plan. 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

Author-owned manuscripts, authorized interviews and permitted research sources. Read-only operational exports, calendars and finance or inventory records as relevant. Start with plan exports and retain human approval for execution. Begin with uploads and exports of owned prior editions and author-approved update notes. 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

Place editable drivers and constraints beside a clearly labeled scenario output. Include a baseline view, comparison chart or schedule, and an assumptions history. Let users trace a proposed quantity or date back to its inputs. Keep forecasts distinct from actual results. Open with edition comparison; move into affected passages for the detailed task; finish in revision plan for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.