Experience / Web3Mi S.r.l.
Ticket demand, budgets and the firm’s accounts, rebuilt from the data. AI agents, employed and learned deeply.
Three areas of work for the company behind Milano Blockchain Week: ticketing econometrics, budget modelling and corporate finance.
In ticketing, the focus was the firm’s own ticket demand, edition by edition, and how it moves with marketing and pricing. It ended in a pricing proposal for the 2027 edition.
In budgeting, the plans already in use and a model to replace them: a cost template and a revenue template, the same from one edition to the next, with two guides on how to use them. The firm has adopted the model.
In corporate finance, the company as a whole, from sponsors and audience to the filed accounts: twelve deliverables, up to the reconciliation of the books and a management cockpit.
Areas of work
Ticketing econometrics
Four editions of ticket demand, split, cumulated and set against marketing and pricing; competitor pricing handed to the firm’s AI agent; and a ticketing proposal left with the firm.
OpenBudget modelling
The budgets in use, checked for errors and time inconsistency; a new cost and revenue model, consistent over time, with two instruction guides; a proposed bridge from budget to income statement.
OpenCorporate finance
Twelve deliverables on the company’s finances, each a workbook with a PDF summary, built with one procedure and checked along independent routes, up to a management cockpit.
OpenData came through the firm’s AI agent, to my specifications: content, format, checks and privacy constraints. Processing and analysis ran locally.
- 01
Go back to the source
Every figure is recomputed from source, never quoted from memory.
- 02
Verify with a different tool
Key results are rebuilt from scratch along a separate route.
- 03
Separate observed from estimated
Each figure carries the strength of its evidence. Null is not zero.
- 04
Publish the limits
Every deliverable states what it cannot support.
Want to know more about booking curves, the comparables, the 2027 price ladder, the prudence factor λ, quantity and price effects, the management cockpit, the AI agents?