Complete AI Training

AI app for marketing · no coding needed

Sponsorship activation evidence desk

Verify deliverables before debating sponsorship effectiveness.

Made for: Brands sponsoring niche events

What Sponsorship activation evidence desk looks like
Open the demo For members · a working demo with sample data

What it does for you

The problem

Sponsorship reports contain photos without proof of agreed delivery.

What it gives you

Sponsorship fulfillment evidence pack

What you give it

Sponsorship agreementsorganizer-supplied evidence

How it works, step by step

  1. Extract agreed placements
  2. Match submitted evidence
  3. Flag missing proof
  4. Compare dates
  5. Draft clarification requests
  6. Export delivery report

What you see on screen

  • Obligation map
  • Evidence review
  • Follow-up queue

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 Sponsorship activation evidence desk 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 Sponsorship activation evidence desk 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 Cloudflare25 KB
  • prompt-vps.mdThe same build on your own server (Docker)25 KB
  • spec.jsonData model, API, AI pipeline, acceptance criteria14 KB
  • demo/index.htmlThe working demo on sample data195 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 brands sponsoring niche events, turn sponsorship agreements and organizer-supplied evidence into sponsorship fulfillment evidence pack. Address this specific problem: sponsorship reports contain photos without proof of agreed delivery. The aim: verify deliverables before debating sponsorship effectiveness. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.

The buyer creates a project, supplies sponsorship agreements and organizer-supplied evidence, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final sponsorship fulfillment evidence pack before use. Retain source links and a version history for the next cycle.

How the AI works

Match assets to obligations with source links. 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 product claims and permissions. Distinguish observed campaign results from causal explanations and keep customer data collection authorized. Fulfillment review; no audience reach estimation without evidence. 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: Fulfillment review; no audience reach estimation without evidence. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract agreed placements; match submitted evidence. Support the third task through an assisted review queue: flag missing proof. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of sponsorship fulfillment evidence pack. 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 brand material, campaign exports and authorized customer research. Source repositories, task trackers and report exports. Keep findings as review proposals until authorized owners accept the resulting actions. Begin with uploads and exports of sponsorship agreements and organizer-supplied evidence. 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

Open on a review queue ordered by reviewer-selected priorities. Show each finding beside the original evidence and applicable rule. Provide accept, dismiss and needs-information controls with reasons. A separate report view summarizes confirmed findings and unresolved items, not raw AI flags. Open with obligation map; move into evidence review for the detailed task; finish in follow-up queue for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.