Complete AI Training

AI app for hospitality and events · no coding needed

Event dietary request reconciliation

Track declared needs to explicit supplier acknowledgment.

Made for: Conference catering coordinators

What Event dietary request reconciliation looks like
Open the demo For members · a working demo with sample data

What it does for you

The problem

Dietary requests change after supplier counts are finalized.

What it gives you

Caterer-approved dietary service list

What you give it

Attendee-declared requestsapproved menus

How it works, step by step

  1. Normalize declared requests
  2. Flag ambiguous entries
  3. Compare menu labels
  4. Track changes
  5. Request caterer confirmation
  6. Export service lists

What you see on screen

  • Request register
  • Menu match
  • Caterer confirmation

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 Event dietary request reconciliation 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 Event dietary request reconciliation 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 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 conference catering coordinators, turn attendee-declared requests and approved menus into caterer-approved dietary service list. Address this specific problem: dietary requests change after supplier counts are finalized. The aim: track declared needs to explicit supplier acknowledgment. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.

The buyer creates a project, supplies attendee-declared requests and approved menus, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final caterer-approved dietary service list before use. Retain source links and a version history for the next cycle.

How the AI works

Flag possible conflicts; qualified caterers determine safe provision. 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

Verify property facts, availability and supplier conditions. Staff approve commercial exceptions and consequential booking changes. Administrative matching; no guarantee that meals are allergen-safe. 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: Administrative matching; no guarantee that meals are allergen-safe. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: normalize declared requests; flag ambiguous entries. Support the third task through an assisted review queue: compare menu labels. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of caterer-approved dietary service list. 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

Property records, event schedules, reservation exports and supplier information. 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 attendee-declared requests and approved menus. 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 request register; move into menu match for the detailed task; finish in caterer confirmation for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.