Complete AI Training

AI app for finance · no coding needed

Royalty statement discrepancy desk

Trace royalty discrepancies to statement lines and supplied terms.

Made for: Independent music and publishing finance teams

What Royalty statement discrepancy desk looks like
Open the demo For members · a working demo with sample data

What it does for you

The problem

Royalty statements use incompatible labels and units.

What it gives you

Royalty reconciliation query pack

What you give it

Licensed statementsapproved royalty terms

How it works, step by step

  1. Extract statement fields
  2. Normalize agreed units
  3. Match work identifiers
  4. Compare stated rates
  5. Flag unexplained deductions
  6. Export queries

What you see on screen

  • Statement import
  • Term comparison
  • Exception ledger

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 Royalty statement discrepancy 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 Royalty statement discrepancy 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 Cloudflare24 KB
  • prompt-vps.mdThe same build on your own server (Docker)24 KB
  • spec.jsonData model, API, AI pipeline, acceptance criteria14 KB
  • demo/index.htmlThe working demo on sample data194 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 independent music and publishing finance teams, turn licensed statements and approved royalty terms into royalty reconciliation query pack. Address this specific problem: royalty statements use incompatible labels and units. The aim: trace royalty discrepancies to statement lines and supplied terms. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.

The buyer creates a project, supplies licensed statements and approved royalty terms, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final royalty reconciliation query pack before use. Retain source links and a version history for the next cycle.

How the AI works

Extract line items while deterministic rules recompute supplied rates. 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

Reconcile calculations to approved records. Keep proposed entries and payment actions under finance-team control. Never invent missing financial inputs. One statement family; legal interpretation excluded. 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: One statement family; legal interpretation excluded. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract statement fields; normalize agreed units. Support the third task through an assisted review queue: match work identifiers. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of royalty reconciliation query 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

Accounting exports, invoice records and finance review processes. Document repositories, procurement or contract records and spreadsheet exports. Preserve originals and avoid writing back interpretations without approval. Begin with uploads and exports of licensed statements and approved royalty terms. 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 side-by-side matrix with the same fields for every document or option. Clicking a value reveals its original passage. Highlight missing, different and uncertain items separately. Provide a clarification queue and reviewer annotations before exporting a decision pack. Open with statement import; move into term comparison for the detailed task; finish in exception ledger for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.