Complete AI Training

AI app for human resources · no coding needed

Job architecture terminology harmonizer

Make inconsistent role language visible before compensation analysis.

Made for: Growing companies' HR operations teams

What Job architecture terminology harmonizer looks like
Open the demo For members · a working demo with sample data

What it does for you

The problem

The same job level means different things across departments.

What it gives you

Approved job architecture glossary

What you give it

Approved role profilesleveling principles

How it works, step by step

  1. Extract responsibility language
  2. Group equivalent terms
  3. Flag inconsistent levels
  4. Draft clarification questions
  5. Record HR decisions
  6. Export role dictionary

What you see on screen

  • Role catalog
  • Terminology comparison
  • Review decisions

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 Job architecture terminology harmonizer 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 Job architecture terminology harmonizer 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 Cloudflare24 KB
  • prompt-vps.mdThe same build on your own server (Docker)24 KB
  • spec.jsonData model, API, AI pipeline, acceptance criteria13 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 growing companies' HR operations teams, turn approved role profiles and leveling principles into approved job architecture glossary. Address this specific problem: the same job level means different things across departments. The aim: make inconsistent role language visible before compensation analysis. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.

The buyer creates a project, supplies approved role profiles and leveling principles, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final approved job architecture glossary before use. Retain source links and a version history for the next cycle.

How the AI works

Compare declared responsibilities without employee performance inference. 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

Keep employee data access explicit and confidential. Use human judgment for personnel decisions and do not infer protected traits or hidden personal characteristics. Language harmonization; no automated job valuation. 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: Language harmonization; no automated job valuation. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract responsibility language; group equivalent terms. Support the third task through an assisted review queue: flag inconsistent levels. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of approved job architecture glossary. 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

Approved HR documents, employee directories and learning records. Source systems, catalog exports and cloud file storage. Start with reversible CSV or file imports and validate identifiers before any direct writes. Begin with uploads and exports of approved role profiles and leveling principles. 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

Use a searchable table or visual gallery with filters for the domain’s important attributes. Open each item into a detail drawer containing source records, ownership and history. Put proposed merges and field changes in a separate review queue. Provide a preview before any bulk export. Open with role catalog; move into terminology comparison for the detailed task; finish in review decisions for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.