Complete AI Training

AI app for operations · no coding needed

Manufacturing batch genealogy replay

Reduce trace investigation time and uncover broken lot relationships.

Made for: Operations quality teams validating material traceability after batch splits and merges

What Manufacturing batch genealogy replay looks like
Open the demo For members · a working demo with sample data

What it does for you

The problem

ERP batch links become difficult to verify when production lots are divided, combined or reworked.

What it gives you

Quality-manager-reviewed genealogy graph and unresolved reconciliation list

What you give it

Authorized lot movementsmeasured quantitiesprocess rulescontrol totals

How it works, step by step

  1. Reconstruct candidate lot relationships
  2. Reconcile quantity conservation across transformations
  3. Run bounded backward and forward trace exercises
  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 quality-manager-reviewed genealogy graph and unresolved reconciliation list 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 Manufacturing batch genealogy replay 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 Manufacturing batch genealogy replay 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 build2 KB
  • prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare28 KB
  • prompt-vps.mdThe same build on your own server (Docker)28 KB
  • spec.jsonData model, API, AI pipeline, acceptance criteria14 KB
  • demo/index.htmlThe working demo on sample data200 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

Reduce trace investigation time and uncover broken lot relationships

Confirm the buyer's problem and scope, collect authorized lot movements, measured quantities, process rules and control totals, then follow this sequence: 1. Reconstruct candidate lot relationships. 2. Reconcile quantity conservation across transformations. 3. Run bounded backward and forward trace exercises. Resolve uncertain cases with qualified reviewers, approve quality-manager-reviewed genealogy graph and unresolved reconciliation list, and measure time to verified trace result and quantity reconciliation exceptions 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. One process line; no invented lot links or automated product release and recall decisions. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Make operational states and ownership explicit. Validate data and require appropriate approval before purchases, scheduling commitments or external system writes. One process line; no invented lot links or automated product release and recall decisions. 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: One process line; no invented lot links or automated product release and recall decisions. Implement one approved input format, a bounded representative case set and the first two task modules: reconstruct candidate lot relationships; reconcile quantity conservation across transformations. Support the third module with operator review: run bounded backward and forward trace exercises. 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

Orders, inventory, supplier files, process documents and workflow records. Learning portals, calendar scheduling and authorized session exports. Make recording, sharing and retention controls explicit in the product. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Scenario designer, Interactive replay, Evidence and debrief. Use a scenario catalog with clear goals and difficulty settings. The main session area supports text, optional voice and visible context. Follow it with a replay or decision map, annotated feedback and a next-practice plan. Facilitators can author scenarios and review participant-selected sessions. Make the task-specific outcome quality-manager-reviewed genealogy graph and unresolved reconciliation list visible beside its evidence, review state and value baseline.