AI app for human resources · no coding needed
Employee listening action receipt
Close the feedback loop with accountable responses.
Made for: People experience teams

What it does for you
The problem
Staff never learn what happened to suggestions they submitted.
What it gives you
Employee suggestion response register
What you give it
Consented suggestion recordsapproved response policies
How it works, step by step
- Group related suggestions
- Preserve dissenting views
- Assign response owners
- Draft status explanations
- Track promised follow-ups
- Export staff updates
What you see on screen
- Suggestion themes
- Action owners
- Response preview
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 Employee listening action receipt 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 Employee listening action receipt 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 Cloudflare22 KB
- prompt-vps.mdThe same build on your own server (Docker)22 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 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
For people experience teams, turn consented suggestion records and approved response policies into employee suggestion response register. Address this specific problem: staff never learn what happened to suggestions they submitted. The aim: close the feedback loop with accountable responses. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.
The buyer creates a project, supplies consented suggestion records and approved response policies, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final employee suggestion response register before use. Retain source links and a version history for the next cycle.
How the AI works
Summarize suggestions without identifying anonymous contributors. 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. Team-level feedback; no individual sentiment scoring. 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: Team-level feedback; no individual sentiment scoring. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: group related suggestions; preserve dissenting views. Support the third task through an assisted review queue: assign response owners. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of employee suggestion response register. 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. Calendars, email, task managers and relevant business records. Use draft actions and supervised handoffs first, then enable only specifically authorized writes. Begin with uploads and exports of consented suggestion records and approved response policies. 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 queue or timeline as the opening view, with clear owners, dates and current states. Each case opens into its source context, proposed actions and discussion. Give external participants a limited form or status page. Make the next required action visible without opening every record. Open with suggestion themes; move into action owners for the detailed task; finish in response preview for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.





