Francisco Rau_

One operation, four working tools

At a Dutch contact centre I owned operational BI end to end: capacity planning, performance measurement and live operational reporting. The four tools below show that kind of work, built on invented data: every name and number is made up, and the operations P&L is a concept of my own. Click into any of them.

From a market gap to a working product

Built on my own time: first the research that showed me the gap was real, then a booking platform for salons and barbershops in Mexico City, like Treatwell or Fresha. Deployed, not launched yet.

SaaS product · deployed, pre-launch

Citania — online booking for salons and barbershops in Latin America

The product the research pointed to: a multi-tenant booking platform in the Treatwell/Fresha category, built for Latin America (Spanish, MXN, CDMX-first). A TypeScript monorepo — Express 5 + Postgres, contract-first OpenAPI, a React web app and an Expo React Native mobile app.

• Over 4,000 automated tests covering the full booking flow, with database-level slot exclusivity — double-booking is blocked by a database constraint.
• Two-way Google Calendar sync live, with loop prevention via source tags, booking IDs and etags.
• Two AI features: a voice receptionist that books against the live calendar, and a photo import that reads a handwritten appointment book.
• Role-based pricing, cancellations, reviews, reminders, segmented marketing + loyalty, and a full owner dashboard (calendar, services, team, inventory, clients, reports).

Owner dashboard — the operator side of the product

Bookings · day calendar⤢ Team shifts · scheduler⤢ Reports · revenue & KPIs⤢ Marketing · segmented email⤢ Team · roles & pricing⤢

Consumer booking page — real-time availability, booking flow end to end. Open the booking page →

TypeScriptPostgreSQLExpressOpenAPIReactReact Native

A football scouting & valuation platform

Built alone, end to end: the data collection, the models, the product and the design. Over 29,000 players across 156 leagues, valued match by match, with the testing published.

Live product · beta

Alpha Scout — find undervalued players before the market does

Alpha Scout uses a machine-learning model, trained on more than 100,000 player-seasons, to estimate what every player should be worth from their performance, age, league strength and contract, and gives a calibrated probability that each one rises in value over the season. Does it work? I backtested it: I rebuilt its July shortlist for every year from 2019 to 2025, using only data available at the time. A year later, its picks were worth on average about 1.3 times as much as comparable players it didn't pick (same league level, position, age, minutes and price), in all seven years. That's a backtest, not a live track record.

• Real data at scale: over 29,000 players across 156 leagues, collected match by match and refreshed every week, with more than 100,000 player-seasons of history behind the estimates.
• Honest about its limits: the results are published league by league, including where it's weak, and it won't price players above €60M, where it hasn't been tested.
• Built for the people who buy: a club view with squad gaps and realistic budgets, similar players under €1M from your region, a contract-expiry radar and one-click club reports.

The live demo — real players, real data

Explorer · ranking, fair value & gap⤢ Player profile · fair value & similar players⤢ Club view · Boca Juniors budget⤢

Pythonstatisticsbacktestingautomationweb app

Try the live demo →

Modeling & research